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https://github.com/Psychic-DL/Awesome-Traffic-Agent-Trajectory-Prediction
This is a list of papers related to traffic agent trajectory prediction.
https://github.com/Psychic-DL/Awesome-Traffic-Agent-Trajectory-Prediction
List: Awesome-Traffic-Agent-Trajectory-Prediction
awesome dataset deep-learning papers source-code traffic-agent trajectory-prediction
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This is a list of papers related to traffic agent trajectory prediction.
- Host: GitHub
- URL: https://github.com/Psychic-DL/Awesome-Traffic-Agent-Trajectory-Prediction
- Owner: Psychic-DL
- License: mit
- Created: 2022-04-14T04:04:31.000Z (over 2 years ago)
- Default Branch: main
- Last Pushed: 2024-05-27T11:58:35.000Z (7 months ago)
- Last Synced: 2024-05-28T12:18:54.397Z (7 months ago)
- Topics: awesome, dataset, deep-learning, papers, source-code, traffic-agent, trajectory-prediction
- Homepage:
- Size: 2.27 MB
- Stars: 321
- Watchers: 21
- Forks: 40
- Open Issues: 1
-
Metadata Files:
- Readme: README.md
- License: LICENSE
Awesome Lists containing this project
- awesome-open-transport - (dataset) Vehicles Trajectory
- ultimate-awesome - Awesome-Traffic-Agent-Trajectory-Prediction - This is a list of papers related to traffic agent trajectory prediction. . (Other Lists / PowerShell Lists)
README
# Awesome-Traffic-Agent-Trajectory-Prediction
       
# 🤝 Contributions
This is a list of the latest research materials (datasets, papers, and codes) related to traffic agent trajectory prediction. Continuously updated, welcome to pay attention!
**Maintainers: Chaoneng Li (Lanzhou Jiaotong University) Emails: [email protected]**
Please feel free to pull requests to add new resources or send emails to us for questions, discussions, and collaborations. **We would like to connect more students, teachers, and bigwigs in the field of multi-agent trajectory prediction, and if you would like to do the same, you can add me on WeChat (CN15691969157). Let's create the Trajectory Prediction Community Group together!**
# 🧐 Citation
Please consider citing our papers if this repository accelerates your research:
```
@inproceedings{li2022fidelity,
title={Fidelity Evaluation of Virtual Traffic Based on Anomalous Trajectory Detection},
author={Li, Chaoneng and Chao, Qianwen and Feng, Guanwen and Wang, Qiongyan and Liu, Pengfei and Li, Yunan and Miao, Qiguang},
booktitle={2022 IEEE/RSJ International Conference on Intelligent Robots and Systems (IROS)},
pages={8157--8164},
year={2022},
organization={IEEE}
}
@article{li2024difftad,
title={DiffTAD: Denoising diffusion probabilistic models for vehicle trajectory anomaly detection},
author={Li, Chaoneng and Feng, Guanwen and Li, Yunan and Liu, Ruyi and Miao, Qiguang and Chang, Liang},
journal={Knowledge-Based Systems},
volume={286},
pages={111387},
year={2024},
publisher={Elsevier}
}
```
******
# 📜 Table of Contents
- [**📚 Traditional Methods**](#-traditional-methods)
- [**📚 2018 and Before Conference and Journal Papers**](#-2018-and-before-conference-and-journal-papers)
- [Conference Papers](#conference-papers)
- [Journal Papers](#journal-papers)
- [Others](#others)
- [**📚 2019 Conference and Journal Papers**](#-2019-conference-and-journal-papers)
- [Conference Papers](#conference-papers-2019)
- [Journal Papers](#journal-papers-2019)
- [Others](#others-2019)
- [**📚 2020 Conference and Journal Papers**](#-2020-conference-and-journal-papers)
- [Conference Papers](#conference-papers-2020)
- [Journal Papers](#journal-papers-2020)
- [Others](#others-2020)
- [**📚 2021 Conference and Journal Papers**](#-2021-conference-and-journal-papers)
- [Conference Papers](#conference-papers-2021)
- [Journal Papers](#journal-papers-2021)
- [Others](#others-2021)
- [**📚 2022 Conference and Journal Papers**](#-2022-conference-and-journal-papers)
- [Conference Papers](#conference-papers-2022)
- [Journal Papers](#journal-papers-2022)
- [Others](#others-2022)
- [**📚 2023 Conference and Journal Papers**](#-2023-conference-and-journal-papers)
- [Conference Papers](#conference-papers-2023)
- [Journal Papers](#journal-papers-2023)
- [Others](#others-2023)
- [**📚 2024 Conference and Journal Papers**](#-2024-conference-and-journal-papers)
- [Conference Papers](#conference-papers-2024)
- [Journal Papers](#journal-papers-2024)
- [Others](#others-2024)
- [**📚 Related Review Papers**](#-related-review-papers)
- [**📚 Datasets**](#-datasets)
- [Reviews about Datasets](#reviews-about-datasets)
- [Vehicles Publicly Available Datasets](#vehicles-publicly-available-datasets)
- [Pedestrians Publicly Available Datasets](#pedestrians-publicly-available-datasets)
- [Others Agents Datasets](#others-agents-datasets)
- [Aircraft](#aircraft)
- [Ship](#ship)
- [Hurricane and Animal](#hurricane-and-animal)
- [**🌹 Acknowledgments**](#-acknowledgments)
- [**🌟 Star History**](#-star-history)
******
# 📚 Traditional Methods
* Social force model for pedestrian dynamics, Physical review E 1995. [[paper](https://arxiv.org/pdf/cond-mat/9805244.pdf?ref=https://githubhelp.com)]
* Simulating dynamical features of escape panic, Nature 2000. [[paper](https://arxiv.org/pdf/cond-mat/0009448.pdf)] [[code](https://github.com/obisargoni/repastInterSim)]
* Congested traffic states in empirical observations and microscopic simulations, Physical review E 2000. [[paper](https://arxiv.org/pdf/cond-mat/0002177.pdf)]
* A methodology for automated trajectory prediction analysis, AIAA Guidance, Navigation, and Control Conference and Exhibit 2004. [[paper](http://citeseerx.ist.psu.edu/viewdoc/download?doi=10.1.1.76.2942&rep=rep1&type=pdf)]
* Continuum crowds, ACM Transactions on Graphics (TOG 2006). [[paper](https://www.khoury.neu.edu/home/scooper/index_files/pub/treuille2006continuum.pdf)]
* New Algorithms for Aircraft Intent Inference and Trajectory Prediction, Journal of guidance, control, and dynamics 2007. [[paper](https://sci-hub.hkvisa.net/10.2514/1.26750)]
* Reciprocal Velocity Obstacles for Real-Time Multi-Agent Navigation, ICRA 2008. [[paper](http://citeseerx.ist.psu.edu/viewdoc/download?doi=10.1.1.161.9395&rep=rep1&type=pdf)]
* You’ll Never Walk Alone: Modeling Social Behavior for Multi-target Tracking, ICCV 2009. [[paper](http://vision.cse.psu.edu/courses/Tracking/vlpr12/PellegriniNeverWalkAlone.pdf)]
* Real time trajectory prediction for collision risk estimation between vehicles, International Conference on Intelligent Computer Communication and Processing 2009. [[paper](https://hal.inria.fr/inria-00438624/document)]
* People Tracking with Human Motion Predictions from Social Forces, ICRA 2010. [[paper](https://ieeexplore.ieee.org/stamp/stamp.jsp?tp=&arnumber=5509779)]
* Unfreezing the robot: Navigation in dense, interacting crowds, IROS 2010. [[paper](https://ieeexplore.ieee.org/stamp/stamp.jsp?tp=&arnumber=5654369)]
* Who are you with and where are you going?, CVPR 2011. [[paper](https://ieeexplore.ieee.org/stamp/stamp.jsp?tp=&arnumber=5995468)]
* Social force model with explicit collision prediction, Europhysics Letters 2011. [[paper](https://iopscience.iop.org/article/10.1209/0295-5075/93/68005/pdf)]
* A Machine Learning Approach to Trajectory Prediction, AIAA Guidance, Navigation, and Control (GNC) Conference 2013. [[paper](https://sci-hub.hkvisa.net/10.2514/6.2013-4782)]
* Cyclist Social Force Model at Unsignalized Intersections With Heterogeneous Traffic, IEEE Transactions on Industrial Informatics 2016. [[paper](https://ieeexplore.ieee.org/stamp/stamp.jsp?tp=&arnumber=7536132)]
* Walking Ahead: The Headed Social Force Model, PLoS ONE 2017. [[paper](https://journals.plos.org/plosone/article/file?id=10.1371/journal.pone.0169734&type=printable)]
* AutoRVO: Local Navigation with Dynamic Constraints in Dense Heterogeneous Traffic, arXiv preprint arXiv:1804.02915, 2018. [[paper](https://arxiv.org/pdf/1804.02915.pdf)]
* Social force models for pedestrian traffic – state of the art, Transport reviews 2018. [[paper](https://www.researchgate.net/profile/Xu-Chen-67/publication/320872442_Social_force_models_for_pedestrian_traffic_-_state_of_the_art/links/5bce680b4585152b144eac39/Social-force-models-for-pedestrian-traffic-state-of-the-art.pdf)]
# 📚 2018 and Before Conference and Journal Papers
## Conference Papers
* Social GAN: Socially Acceptable Trajectories with Generative Adversarial Networks, CVPR 2018. [[paper](https://openaccess.thecvf.com/content_cvpr_2018/papers/Gupta_Social_GAN_Socially_CVPR_2018_paper.pdf)] [[code](https://github.com/agrimgupta92/sgan)]
* Encoding Crowd Interaction with Deep Neural Network for Pedestrian Trajectory Prediction, CVPR 2018. [[paper](https://openaccess.thecvf.com/content_cvpr_2018/papers/Xu_Encoding_Crowd_Interaction_CVPR_2018_paper.pdf)] [[code](https://github.com/svip-lab/CIDNN)]
* Fast and Furious: Real Time End-to-End 3D Detection, Tracking and Motion Forecasting with a Single Convolutional Net, CVPR 2018. [[paper](https://openaccess.thecvf.com/content_cvpr_2018/papers/Luo_Fast_and_Furious_CVPR_2018_paper.pdf)]
* MX-LSTM: Mixing Tracklets and Vislets to Jointly Forecast Trajectories and Head Poses, CVPR 2018. [[paper](https://openaccess.thecvf.com/content_cvpr_2018/papers/Hasan_MX-LSTM_Mixing_Tracklets_CVPR_2018_paper.pdf)]
* Long-Term On-Board Prediction of People in Traffic Scenes under Uncertainty, CVPR 2018. [[paper](https://openaccess.thecvf.com/content_cvpr_2018/papers/Bhattacharyya_Long-Term_On-Board_Prediction_CVPR_2018_paper.pdf)] [[code](https://github.com/apratimbhattacharyya18/onboard_long_term_prediction)]
* R2P2: A ReparameteRized Pushforward Policy for Diverse, Precise Generative Path Forecasting, ECCV 2018. [[paper](https://openaccess.thecvf.com/content_ECCV_2018/papers/Nicholas_Rhinehart_R2P2_A_ReparameteRized_ECCV_2018_paper.pdf)]
* Where Will They Go? Predicting Fine-Grained Adversarial Multi-Agent Motion using Conditional Variational Autoencoders, ECCV 2018. [[paper](https://openaccess.thecvf.com/content_ECCV_2018/papers/Panna_Felsen_Where_Will_They_ECCV_2018_paper.pdf)]
* Generating Comfortable, Safe and Comprehensible Trajectories for Automated Vehicles in Mixed Traffic, International Conference on Intelligent Transportation Systems (ITSC 2018). [[paper](https://ieeexplore.ieee.org/stamp/stamp.jsp?tp=&arnumber=8569658)]
* Set-Based Prediction of Pedestrians in Urban Environments Considering Formalized Traffic Rules, ITSC 2018. [[paper](https://ieeexplore.ieee.org/stamp/stamp.jsp?tp=&arnumber=8569434)]
* Intention-aware Long Horizon Trajectory Prediction of Surrounding Vehicles using Dual LSTM Networks, ITSC 2018. [[paper](https://ieeexplore.ieee.org/stamp/stamp.jsp?tp=&arnumber=8569595)]
* Social Attention: Modeling Attention in Human Crowds, ICRA 2018. [[paper](https://ieeexplore.ieee.org/stamp/stamp.jsp?tp=&arnumber=8460504)]
* A Data-driven Model for Interaction-Aware Pedestrian Motion Prediction in Object Cluttered Environments, ICRA 2018. [[paper](https://ieeexplore.ieee.org/stamp/stamp.jsp?tp=&arnumber=8461157)]
* Multimodal Probabilistic Model-Based Planning for Human-Robot Interaction, ICRA 2018. [[paper](https://ieeexplore.ieee.org/stamp/stamp.jsp?tp=&arnumber=8460766)] [[code](https://github.com/StanfordASL/TrafficWeavingCVAE)]
* GD-GAN: Generative Adversarial Networks for Trajectory Prediction and Group Detection in Crowds, ACCV 2018. [[paper](https://arxiv.org/pdf/1812.07667.pdf)]
* Multi-Modal Trajectory Prediction of Surrounding Vehicles with Maneuver based LSTMs, IEEE Intelligent Vehicles Symposium (IV 2018). [[paper](https://ieeexplore.ieee.org/stamp/stamp.jsp?tp=&arnumber=8500493)]
* Sequence-to-Sequence Prediction of Vehicle Trajectory via LSTM Encoder-Decoder Architecture, IV 2018. [[paper](https://ieeexplore.ieee.org/stamp/stamp.jsp?tp=&arnumber=8500658)]
* Predicting Trajectories of Vehicles Using Large-Scale Motion Priors, IV 2018. [[paper](http://mssuraj.com/publications/2018_IV_0596.pdf)]
* Road Infrastructure Indicators for Trajectory Prediction, IV 2018. [[paper](https://ieeexplore.ieee.org/stamp/stamp.jsp?tp=&arnumber=8500678)]
* Mixed Traffic Trajectory Prediction Using LSTM–Based Models in Shared Space, Annual International Conference on Geographic Information Science 2018. [[paper](https://link.springer.com/content/pdf/10.1007/978-3-319-78208-9_16.pdf)]
* SS-LSTM: A Hierarchical LSTM Model for Pedestrian Trajectory Prediction, WACV 2018. [[paper](https://ieeexplore.ieee.org/stamp/stamp.jsp?tp=&arnumber=8354239)] [[code](https://github.com/xuehaouwa/SS-LSTM)]
* “Seeing is Believing”: Pedestrian Trajectory Forecasting Using Visual Frustum of Attention, WACV 2018. [[paper](http://irtizahasan.com/WACV_2018_Seeing_is_believing.pdf)]
* Tracking by Prediction: A Deep Generative Model for Mutli-person Localisation and Tracking, WACV 2018. [[paper](https://ieeexplore.ieee.org/stamp/stamp.jsp?tp=&arnumber=8354232)]
* Context-Aware Trajectory Prediction, International Conference on Pattern Recognition (ICPR 2018). [[paper](https://ieeexplore.ieee.org/stamp/stamp.jsp?tp=&arnumber=8545447)]
* Transferable Pedestrian Motion Prediction Models at Intersections, IROS 2018. [[paper](https://ieeexplore.ieee.org/stamp/stamp.jsp?tp=&arnumber=8593783)]
* Generative Modeling of Multimodal Multi-Human Behavior, IROS 2018. [[paper](https://ieeexplore.ieee.org/stamp/stamp.jsp?tp=&arnumber=8594393)] [[code](https://github.com/StanfordASL/NHumanModeling)]
* Building Prior Knowledge: A Markov Based Pedestrian Prediction Model Using Urban Environmental Data, International Conference on Control, Automation, Robotics and Vision (ICARCV 2018). [[paper](https://ieeexplore.ieee.org/stamp/stamp.jsp?tp=&arnumber=8581368)]
* Cyclist Trajectory Prediction Using Bidirectional Recurrent Neural Networks, Australasian Joint Conference on Artificial Intelligence 2018. [[paper](https://link.springer.com/content/pdf/10.1007/978-3-030-03991-2_28.pdf)]
* Attention Is All You Need, NIPS 2017. [[paper](https://proceedings.neurips.cc/paper/2017/file/3f5ee243547dee91fbd053c1c4a845aa-Paper.pdf)]
* Bi-Prediction: Pedestrian Trajectory Prediction Based on Bidirectional LSTM Classification, International Conference on Digital Image Computing: Techniques and Applications (DICTA 2017). [[paper](https://www.researchgate.net/profile/Du-Huynh-2/publication/322001876_Bi-Prediction_Pedestrian_Trajectory_Prediction_Based_on_Bidirectional_LSTM_Classification/links/5c03cef4a6fdcc1b8d5029bb/Bi-Prediction-Pedestrian-Trajectory-Prediction-Based-on-Bidirectional-LSTM-Classification.pdf)]
* Probabilistic Vehicle Trajectory Prediction over Occupancy Grid Map via Recurrent Neural Network, ITSC 2017. [[paper](https://ieeexplore.ieee.org/stamp/stamp.jsp?tp=&arnumber=8317943)]
* Natural Vision Based Method for Predicting Pedestrian Behaviour in Urban Environments, ITSC 2017. [[paper](https://ieeexplore.ieee.org/stamp/stamp.jsp?tp=&arnumber=8317848)]
* How good is my prediction? Finding a similarity measure for trajectory prediction evaluation, ITSC 2017. [[paper](https://ieeexplore.ieee.org/stamp/stamp.jsp?tp=&arnumber=8317825)]
* An LSTM network for highway trajectory prediction, ITSC 2017. [[paper](https://ieeexplore.ieee.org/stamp/stamp.jsp?tp=&arnumber=8317913)]
* DESIRE: Distant Future Prediction in Dynamic Scenes with Interacting Agents, CVPR 2017. [[paper](https://openaccess.thecvf.com/content_cvpr_2017/papers/Lee_DESIRE_Distant_Future_CVPR_2017_paper.pdf)] [[code](https://github.com/tdavchev/DESIRE)]
* Forecasting Interactive Dynamics of Pedestrians with Fictitious Play, CVPR 2017. [[paper](https://openaccess.thecvf.com/content_cvpr_2017/papers/Ma_Forecasting_Interactive_Dynamics_CVPR_2017_paper.pdf)]
* Forecast the Plausible Paths in Crowd Scenes, IJCAI 2017. [[paper](https://www.ijcai.org/proceedings/2017/0386.pdf)]
* What will Happen Next? Forecasting Player Moves in Sports Videos, ICCV 2017. [[paper](https://openaccess.thecvf.com/content_ICCV_2017/papers/Felsen_What_Will_Happen_ICCV_2017_paper.pdf)]
* Using road topology to improve cyclist path prediction, IV 2017. [[paper](https://ieeexplore.ieee.org/stamp/stamp.jsp?tp=&arnumber=7995734)]
* Short-term 4D Trajectory Prediction Using Machine Learning Methods, Proc. SID 2017. [[paper](https://www.sesarju.eu/sites/default/files/documents/sid/2017/SIDs_2017_paper_11.pdf)]
* Generating Long-term Trajectories Using Deep Hierarchical Networks, NIPS 2016. [[paper](https://proceedings.neurips.cc/paper/2016/file/fe8c15fed5f808006ce95eddb7366e35-Paper.pdf)]
* Learning Social Etiquette: Human Trajectory Understanding In Crowded Scenes, ECCV 2016. [[paper](https://link.springer.com/content/pdf/10.1007/978-3-319-46484-8_33.pdf)]
* Knowledge Transfer for Scene-Specific Motion Prediction, ECCV 2016. [[paper](https://link.springer.com/content/pdf/10.1007/978-3-319-46448-0_42.pdf)]
* Structural-RNN: Deep Learning on Spatio-Temporal Graphs, CVPR 2016. [[paper](https://openaccess.thecvf.com/content_cvpr_2016/papers/Jain_Structural-RNN_Deep_Learning_CVPR_2016_paper.pdf)] [[code](https://github.com/asheshjain399/RNNexp)]
* Visual Path Prediction in Complex Scenes with Crowded Moving Objects, CVPR 2016. [[paper](https://www.cv-foundation.org/openaccess/content_cvpr_2016/papers/Yoo_Visual_Path_Prediction_CVPR_2016_paper.pdf)]
* Social LSTM: Human Trajectory Prediction in Crowded Spaces, CVPR 2016. [[paper](https://openaccess.thecvf.com/content_cvpr_2016/papers/Alahi_Social_LSTM_Human_CVPR_2016_paper.pdf)] [[code](https://github.com/quancore/social-lstm)]
* Comparison and Evaluation of Pedestrian Motion Models for Vehicle Safety Systems, ITSC 2016. [[paper](https://ieeexplore.ieee.org/stamp/stamp.jsp?tp=&arnumber=7795912)]
* Intent-aware long-term prediction of pedestrian motion, ICRA 2016. [[paper](https://ieeexplore.ieee.org/stamp/stamp.jsp?tp=&arnumber=7487409)]
* Novel planning-based algorithms for human motion prediction, ICRA 2016. [[paper](https://ieeexplore.ieee.org/stamp/stamp.jsp?tp=&arnumber=7487505)]
* GLMP-realtime pedestrian path prediction using global and local movement patterns, ICRA 2016. [[paper](https://ieeexplore.ieee.org/stamp/stamp.jsp?tp=&arnumber=7487768)]
* Augmented Dictionary Learning for Motion Prediction, ICRA 2016. [[paper](https://ieeexplore.ieee.org/stamp/stamp.jsp?tp=&arnumber=7487407&tag=1)]
* Predicting Future Agent Motions for Dynamic Environments, International Conference on Machine Learning and Applications (ICMLA 2016). [[paper](https://ieeexplore.ieee.org/stamp/stamp.jsp?tp=&arnumber=7838128)]
* Trajectory prediction of cyclists using a physical model and an artificial neural network, IV 2016. [[paper](https://ieeexplore.ieee.org/stamp/stamp.jsp?tp=&arnumber=7535484)]
* STF-RNN: Space Time Features-based Recurrent Neural Network for predicting people next location, IEEE Symposium Series on Computational Intelligence (SSCI 2016). [[paper](https://ieeexplore.ieee.org/stamp/stamp.jsp?tp=&arnumber=7849919)]
* Trajectory analysis and prediction for improved pedestrian safety: Integrated framework and evaluations, IV 2015. [[paper](https://ieeexplore.ieee.org/stamp/stamp.jsp?tp=&arnumber=7225707)]
* Bayesian intention inference for trajectory prediction with an unknown goal destination, IROS 2015. [[paper](https://ieeexplore.ieee.org/stamp/stamp.jsp?tp=&arnumber=7354203)]
* Unsupervised robot learning to predict person motion, ICRA 2015. [[paper](https://ieeexplore.ieee.org/stamp/stamp.jsp?tp=&arnumber=7139254)]
* A Controlled Interactive Multiple Model Filter for Combined Pedestrian Intention Recognition and Path Prediction, ITSC 2015. [[paper](https://ieeexplore.ieee.org/stamp/stamp.jsp?tp=&arnumber=7313129)]
* Socially-aware Large-scale Crowd Forecasting, CVPR 2014. [[paper](https://www.cv-foundation.org/openaccess/content_cvpr_2014/papers/Alahi_Socially-aware_Large-scale_Crowd_2014_CVPR_paper.pdf)]
* Patch to the Future: Unsupervised Visual Prediction, CVPR 2014. [[paper](https://www.cv-foundation.org/openaccess/content_cvpr_2014/papers/Walker_Patch_to_the_2014_CVPR_paper.pdf)]
* Online maneuver recognition and multimodal trajectory prediction for intersection assistance using non-parametric regression, IV 2014. [[paper](https://ieeexplore.ieee.org/stamp/stamp.jsp?tp=&arnumber=6856480)]
* Pedestrian Path Prediction using Body Language Traits, IV 2014. [[paper](https://ieeexplore.ieee.org/stamp/stamp.jsp?tp=&arnumber=6856498)]
* Behavior estimation for a complete framework for human motion prediction in crowded environments, ICRA 2014. [[paper](https://ieeexplore.ieee.org/stamp/stamp.jsp?tp=&arnumber=6907734)]
* Learning to predict trajectories of cooperatively navigating agents, ICRA 2014. [[paper](https://ieeexplore.ieee.org/stamp/stamp.jsp?tp=&arnumber=6907442)]
* Pedestrian's Trajectory Forecast in Public Traffic with Artificial Neural Networks, International Conference on Pattern Recognition (ICPR 2014). [[paper](https://ieeexplore.ieee.org/stamp/stamp.jsp?tp=&arnumber=6977417)]
* Context-Based Pedestrian Path Prediction, ECCV 2014. [[paper](https://link.springer.com/content/pdf/10.1007/978-3-319-10599-4_40.pdf)]
* Bayesian, Maneuver-Based, Long-Term Trajectory Prediction and Criticality Assessment for Driver Assistance Systems, ITSC 2014. [[paper](https://www.researchgate.net/profile/Matthias-Schreier/publication/266954831_Bayesian_Maneuver-Based_Long-Term_Trajectory_Prediction_and_Criticality_Assessment_for_Driver_Assistance_Systems/links/543fb6250cf2be1758cf3c39/Bayesian-Maneuver-Based-Long-Term-Trajectory-Prediction-and-Criticality-Assessment-for-Driver-Assistance-Systems.pdf)]
* Trajectory generator for autonomous vehicles in urban environments, ICRA 2013. [[paper](https://hal.inria.fr/file/index/docid/789760/filename/ICRA_Perez_et_al_2360.pdf)]
* Vehicle trajectory prediction based on motion model and maneuver recognition, IROS 2013. [[paper](https://hal.archives-ouvertes.fr/hal-00881100/document)]
* Predictive maneuver evaluation for enhancement of Car-to-X mobility data, IV 2012. [[paper](https://ieeexplore.ieee.org/stamp/stamp.jsp?tp=&arnumber=6232217)]
* Probabilistic trajectory prediction with Gaussian mixture models, IV 2012. [[paper](https://ieeexplore.ieee.org/stamp/stamp.jsp?tp=&arnumber=6232277)]
* Exploiting map information for driver intention estimation at road intersections, IV 2011. [[paper](https://ieeexplore.ieee.org/stamp/stamp.jsp?tp=&arnumber=5940452)]
* Trajectory Prediction: Learning to Map Situations to Robot Trajectories, ICML 2009. [[paper](https://dl.acm.org/doi/pdf/10.1145/1553374.1553433)]
* Monte Carlo based Threat Assessment: Analysis and Improvements, IV 2007. [[paper](https://ieeexplore.ieee.org/stamp/stamp.jsp?tp=&arnumber=4290120)]
* Gaussian Processes in Machine Learning, Summer school on machine learning 2003. [[paper](https://link.springer.com/content/pdf/10.1007/978-3-540-28650-9_4.pdf)]
## Journal Papers
* Soft + Hardwired Attention: An LSTM Framework for Human Trajectory Prediction and Abnormal Event Detection, Neural networks 2018. [[paper](https://arxiv.org/pdf/1702.05552.pdf?ref=https://githubhelp.com)]
* Long-term path prediction in urban scenarios using circular distributions, Image and Vision Computing 2018. [[paper](https://reader.elsevier.com/reader/sd/pii/S0262885617301853?token=DAD7B9F10835E05341405E75C5AB9F8F114FE99410544AD2BB4EFAA23BFC99D63EA8811C4A8C4F679593A61D0D3E35B6&originRegion=eu-west-1&originCreation=20220509082210)]
* An Efficient Algorithm for Optimal Trajectory Generation for Heterogeneous Multi-Agent Systems in Non-Convex Environments, RAL 2018. [[paper](https://ieeexplore.ieee.org/stamp/stamp.jsp?tp=&arnumber=8260912)]
* Network-Wide Vehicle Trajectory Prediction in Urban Traffic Networks using Deep Learning, Transportation Research Record 2018. [[paper](https://www.researchgate.net/profile/Seongjin-Choi-2/publication/327524033_Network-Wide_Vehicle_Trajectory_Prediction_in_Urban_Traffic_Networks_using_Deep_Learning/links/5e3a123e458515072d8015d2/Network-Wide-Vehicle-Trajectory-Prediction-in-Urban-Traffic-Networks-using-Deep-Learning.pdf)]
* Intent Prediction of Pedestrians via Motion Trajectories Using Stacked Recurrent Neural Networks, IEEE Transactions on Intelligent Vehicles 2018. [[paper](https://ieeexplore.ieee.org/stamp/stamp.jsp?tp=&arnumber=8481390)]
* How Would Surround Vehicles Move? A Unified Framework for Maneuver Classification and Motion Prediction, IEEE Transactions on Intelligent Vehicles 2018. [[paper](https://ieeexplore.ieee.org/abstract/document/8286935)]
* Pedestrian Path, Pose, and Intention Prediction Through Gaussian Process Dynamical Models and Pedestrian Activity Recognition, TITS 2018. [[paper](https://ieeexplore.ieee.org/stamp/stamp.jsp?tp=&arnumber=8370119)]
* Dictionary-based Fidelity Measure for Virtual Traffic, TVCG 2018. [[paper](https://ieeexplore.ieee.org/stamp/stamp.jsp?tp=&arnumber=8481568)]
* Realistic Data-Driven Traffic Flow Animation Using Texture Synthesis, TVCG 2017. [[paper](https://ieeexplore.ieee.org/stamp/stamp.jsp?tp=&arnumber=7814314)]
* Vehicle Trajectory Prediction by Integrating Physics- and Maneuver-Based Approaches Using Interactive Multiple Models, IEEE Transactions on Industrial Electronics 2017. [[paper](https://www.researchgate.net/profile/Jianqiang-Wang/publication/321738692_Vehicle_Trajectory_Prediction_by_Integrating_Physics-_and_Maneuver-Based_Approaches_Using_Interactive_Multiple_Models/links/5fcde8c445851568d1469e52/Vehicle-Trajectory-Prediction-by-Integrating-Physics-and-Maneuver-Based-Approaches-Using-Interactive-Multiple-Models.pdf)]
* Real-Time Certified Probabilistic Pedestrian Forecasting, RAL 2017. [[paper](https://ieeexplore.ieee.org/stamp/stamp.jsp?tp=&arnumber=7959047)]
* Deep Learning Driven Visual Path Prediction from a Single Image, TIP 2016. [[paper](https://ieeexplore.ieee.org/stamp/stamp.jsp?tp=&arnumber=7576681)]
* Age and Group-driven Pedestrian Behaviour: from Observations to Simulations, Collective Dynamics 2016. [[paper](https://collective-dynamics.eu/index.php/cod/article/view/A3/5)]
* An Integrated Approach to Maneuver-Based Trajectory Prediction and Criticality Assessment in Arbitrary Road Environments, TITS 2016. [[paper](https://ieeexplore.ieee.org/stamp/stamp.jsp?tp=&arnumber=7412746)]
* Trajectory Data and Flow Characteristics of Mixed Traffic, Transportation Research Record 2015. [[paper](https://www.researchgate.net/profile/Gowri-Asaithambi/publication/284708700_Trajectory_Data_and_Flow_Characteristics_of_Mixed_Traffic/links/5710718008ae68dc79097605/Trajectory-Data-and-Flow-Characteristics-of-Mixed-Traffic.pdf)]
* Predicting and recognizing human interactions in public spaces, Journal of Real-Time Image Processing 2015. [[paper](https://fabiopoiesi.github.io/files/papers/journals/2014_JRTIP_PredictingRecognizingInteractionsPublic_Poiesi_Cavallaro.pdf)]
* Learning Collective Crowd Behaviors with Dynamic Pedestrian-Agents, International Journal of Computer Vision 2015. [[paper](https://dspace.mit.edu/bitstream/handle/1721.1/103360/11263_2014_735_ReferencePDF.pdf?sequence=1&isAllowed=y)]
* Real-Time Predictive Modeling and Robust Avoidance of Pedestrians with Uncertain, Changing Intentions, Algorithmic Foundations of Robotics XI 2015. [[paper](https://link.springer.com/content/pdf/10.1007/978-3-319-16595-0_10.pdf)]
* BRVO: Predicting pedestrian trajectories using velocity-space reasoning, International Journal of Robotics Research 2015. [[paper](https://www.cs.cityu.edu.hk/~rynson/papers/ijrr15.pdf)]
* Learning intentions for improved human motion prediction, Robotics and Autonomous Systems 2014. [[paper](https://www.techunited.nl/media/images/Kwalificatie%20materiaal%202014/Elfring_2014.pdf)]
* A Self-Adaptive Parameter Selection Trajectory Prediction Approach via Hidden Markov Models, TITS 2014. [[paper](https://ieeexplore.ieee.org/stamp/stamp.jsp?tp=&arnumber=6918501)]
* TraPlan: An Effective Three-in-One Trajectory-Prediction Model in Transportation Networks, TITS 2014. [[paper](https://ieeexplore.ieee.org/stamp/stamp.jsp?tp=&arnumber=6899589)]
* Will the Pedestrian Cross? A Study on Pedestrian Path Prediction, TITS 2013. [[paper](https://ieeexplore.ieee.org/stamp/stamp.jsp?tp=&arnumber=6632960)]
* Mobile Agent Trajectory Prediction Using Bayesian Nonparametric Reachability Trees, Infotech@ Aerospace 2011. [[paper](https://dspace.mit.edu/bitstream/handle/1721.1/114899/Aoude_Infotech11.pdf?sequence=1&isAllowed=y)]
* Gaussian Process Dynamical Models for Human Motion, TPAMI 2008. [[paper](https://ieeexplore.ieee.org/stamp/stamp.jsp?tp=&arnumber=4359316)]
* A new approach to linear filtering and prediction problems, Journal of Basic Engineering 1960. [[paper](http://160.78.24.2/Public/Kalman/Kalman1960.pdf)]
## Others
* An Evaluation of Trajectory Prediction Approaches and Notes on the TrajNet Benchmark. arXiv preprint arXiv:1805.07663, 2018. [[paper](https://arxiv.org/pdf/1805.07663.pdf)] [[paper](https://openaccess.thecvf.com/content_ECCVW_2018/papers/11131/Becker_RED_A_simple_but_effective_Baseline_Predictor_for_the_TrajNet_ECCVW_2018_paper.pdf)]
* Scene-LSTM: A Model for Human Trajectory Prediction, arXiv preprint arXiv:1808.04018, 2018. [[paper](https://arxiv.org/ftp/arxiv/papers/1808/1808.04018.pdf)]
* Convolutional Social Pooling for Vehicle Trajectory Prediction, CVPR Workshops 2018. [[paper](https://openaccess.thecvf.com/content_cvpr_2018_workshops/papers/w29/Deo_Convolutional_Social_Pooling_CVPR_2018_paper.pdf)] [[code](https://github.com/nachiket92/conv-social-pooling)]
* Convolutional Neural Network for Trajectory Prediction, ECCV Workshops 2018. [[paper](https://openaccess.thecvf.com/content_ECCVW_2018/papers/11131/Nikhil_Convolutional_Neural_Network_for_Trajectory_Prediction_ECCVW_2018_paper.pdf)]
* Group LSTM: Group Trajectory Prediction in Crowded Scenarios, ECCV Workshops 2018. [[paper](https://openaccess.thecvf.com/content_ECCVW_2018/papers/11131/Bisagno_Group_LSTM_Group_Trajectory_Prediction_in_Crowded_Scenarios_ECCVW_2018_paper.pdf)]
* Are they going to cross? a benchmark dataset and baseline for pedestrian crosswalk behavior, ICCV Workshops 2017. [[paper](https://openaccess.thecvf.com/content_ICCV_2017_workshops/papers/w3/Rasouli_Are_They_Going_ICCV_2017_paper.pdf)] [[website](https://data.nvision2.eecs.yorku.ca/JAAD_dataset/)]
* Human Trajectory Prediction using Spatially aware Deep Attention Models, arXiv preprint arXiv:1705.09436, 2017. [[paper](https://arxiv.org/pdf/1705.09436.pdf)]
* Modeling Spatial-Temporal Dynamics of Human Movements for Predicting Future Trajectories, AAAI Workshops 2015. [[paper](https://www.diva-portal.org/smash/get/diva2:808848/FULLTEXT01.pdf)]
# 📚 2019 Conference and Journal Papers
## Conference Papers 2019
* MultiPath: Multiple Probabilistic Anchor Trajectory Hypotheses for Behavior Prediction, Conference on Robot Learning (CoRL 2019). [[paper](https://arxiv.org/pdf/1910.05449.pdf)]
* Generating Multi-Agent Trajectories using Programmatic Weak Supervision, ICLR 2019. [[paper](https://arxiv.org/pdf/1803.07612.pdf)] [[code](https://github.com/ezhan94/multiagent-programmatic-supervision)]
* Stochastic Prediction of Multi-Agent Interactions from Partial Observations, ICLR 2019. [[paper](https://arxiv.org/pdf/1902.09641.pdf)]
* TrafficPredict: Trajectory Prediction for Heterogeneous Traffic-Agents, AAAI 2019. [[paper](https://ojs.aaai.org/index.php/AAAI/article/view/4569/4447)] [[code](https://github.com/huang-xx/TrafficPredict)]
* Data-Driven Crowd Simulation with Generative Adversarial Networks, International Conference on Computer Animation and Social Agents (CASA 2019). [[paper](https://dl.acm.org/doi/pdf/10.1145/3328756.3328769)] [[code](https://github.com/amiryanj/crowdGAN)]
* RobustTP: End-to-End Trajectory Prediction for Heterogeneous Road-Agents in Dense Traffic with Noisy Sensor Inputs, ACM Computer Science in Cars Symposium (CSCS 2019). [[paper](https://dl.acm.org/doi/pdf/10.1145/3359999.3360495)] [[code](https://github.com/rohanchandra30/TrackNPred)]
* Which Way Are You Going? Imitative Decision Learning for Path Forecasting in Dynamic Scenes, CVPR 2019. [[paper](https://openaccess.thecvf.com/content_CVPR_2019/papers/Li_Which_Way_Are_You_Going_Imitative_Decision_Learning_for_Path_CVPR_2019_paper.pdf)]
* Multi-Agent Tensor Fusion for Contextual Trajectory Prediction, CVPR 2019. [[paper](https://openaccess.thecvf.com/content_CVPR_2019/papers/Zhao_Multi-Agent_Tensor_Fusion_for_Contextual_Trajectory_Prediction_CVPR_2019_paper.pdf)] [[code](https://github.com/programmingLearner/MATF-architecture-details)]
* Peeking into the Future: Predicting Future Person Activities and Locations in Videos, CVPR 2019. [[paper](https://openaccess.thecvf.com/content_CVPR_2019/papers/Liang_Peeking_Into_the_Future_Predicting_Future_Person_Activities_and_Locations_CVPR_2019_paper.pdf)] [[code](https://github.com/google/next-prediction)] [[website](https://next.cs.cmu.edu/)]
* SoPhie: An Attentive GAN for Predicting Paths Compliant to Social and Physical Constraints, CVPR 2019. [[paper](https://openaccess.thecvf.com/content_CVPR_2019/papers/Sadeghian_SoPhie_An_Attentive_GAN_for_Predicting_Paths_Compliant_to_Social_CVPR_2019_paper.pdf)] [[code](https://github.com/coolsunxu/sophie)]
* SR-LSTM: State Refinement for LSTM towards Pedestrian Trajectory Prediction, CVPR 2019. [[paper](https://openaccess.thecvf.com/content_CVPR_2019/papers/Zhang_SR-LSTM_State_Refinement_for_LSTM_Towards_Pedestrian_Trajectory_Prediction_CVPR_2019_paper.pdf)] [[code](https://github.com/zhangpur/SR-LSTM)]
* TraPHic: Trajectory Prediction in Dense and Heterogeneous Traffic Using Weighted Interactions, CVPR 2019. [[paper](https://openaccess.thecvf.com/content_CVPR_2019/papers/Chandra_TraPHic_Trajectory_Prediction_in_Dense_and_Heterogeneous_Traffic_Using_Weighted_CVPR_2019_paper.pdf)] [[code](https://github.com/BenMSK/trajectory_prediction_TraPHic)]
* Overcoming Limitations of Mixture Density Networks: A Sampling and Fitting Framework for Multimodal Future Prediction, CVPR 2019. [[paper](https://openaccess.thecvf.com/content_CVPR_2019/papers/Makansi_Overcoming_Limitations_of_Mixture_Density_Networks_A_Sampling_and_Fitting_CVPR_2019_paper.pdf)] [[code](https://github.com/lmb-freiburg/Multimodal-Future-Prediction)]
* Argoverse: 3D Tracking and Forecasting with Rich Maps, CVPR 2019. [[paper](https://openaccess.thecvf.com/content_CVPR_2019/papers/Chang_Argoverse_3D_Tracking_and_Forecasting_With_Rich_Maps_CVPR_2019_paper.pdf)] [[code](https://github.com/argoai/argoverse-api)]
* Diverse Generation for Multi-agent Sports Games, CVPR 2019. [[paper](https://openaccess.thecvf.com/content_CVPR_2019/papers/Yeh_Diverse_Generation_for_Multi-Agent_Sports_Games_CVPR_2019_paper.pdf)]
* Looking to Relations for Future Trajectory Forecast, ICCV 2019. [[paper](https://openaccess.thecvf.com/content_ICCV_2019/papers/Choi_Looking_to_Relations_for_Future_Trajectory_Forecast_ICCV_2019_paper.pdf)]
* Analyzing the Variety Loss in the Context of Probabilistic Trajectory Prediction, ICCV 2019. [[paper](https://openaccess.thecvf.com/content_ICCV_2019/papers/Thiede_Analyzing_the_Variety_Loss_in_the_Context_of_Probabilistic_Trajectory_ICCV_2019_paper.pdf)]
* The Trajectron: Probabilistic Multi-Agent Trajectory Modeling With Dynamic Spatiotemporal Graphs, ICCV 2019. [[paper](https://openaccess.thecvf.com/content_ICCV_2019/papers/Ivanovic_The_Trajectron_Probabilistic_Multi-Agent_Trajectory_Modeling_With_Dynamic_Spatiotemporal_Graphs_ICCV_2019_paper.pdf)] [[code](https://github.com/StanfordASL/Trajectron)]
* Joint Prediction for Kinematic Trajectories in Vehicle-Pedestrian-Mixed Scenes, ICCV 2019. [[paper](https://openaccess.thecvf.com/content_ICCV_2019/papers/Bi_Joint_Prediction_for_Kinematic_Trajectories_in_Vehicle-Pedestrian-Mixed_Scenes_ICCV_2019_paper.pdf)]
* STGAT: Modeling Spatial-Temporal Interactions for Human Trajectory Prediction, ICCV 2019. [[paper](https://openaccess.thecvf.com/content_ICCV_2019/papers/Huang_STGAT_Modeling_Spatial-Temporal_Interactions_for_Human_Trajectory_Prediction_ICCV_2019_paper.pdf)] [[code](https://github.com/huang-xx/STGAT)]
* PIE: A Large-Scale Dataset and Models for Pedestrian Intention Estimation and Trajectory Prediction, ICCV 2019. [[paper](https://openaccess.thecvf.com/content_ICCV_2019/papers/Rasouli_PIE_A_Large-Scale_Dataset_and_Models_for_Pedestrian_Intention_Estimation_ICCV_2019_paper.pdf)] [[code](https://github.com/aras62/PIEPredict)]
* A Multi-Vehicle Trajectories Generator to Simulate Vehicle-to-Vehicle Encountering Scenarios, ICRA 2019. [[paper](https://ieeexplore.ieee.org/stamp/stamp.jsp?tp=&arnumber=8793776)]
* Multimodal Trajectory Predictions for Autonomous Driving using Deep Convolutional Networks, ICRA 2019. [[paper](https://ieeexplore.ieee.org/stamp/stamp.jsp?tp=&arnumber=8793868)] [[code](https://github.com/daeheepark/PathPredictNusc)]
* Force-based Heterogeneous Traffic Simulation for Autonomous Vehicle Testing, ICRA 2019. [[paper](https://ieeexplore.ieee.org/stamp/stamp.jsp?tp=&arnumber=8794430)]
* Interaction-aware Multi-agent Tracking and Probabilistic Behavior Prediction via Adversarial Learning, ICRA 2019. [[paper](https://ieeexplore.ieee.org/stamp/stamp.jsp?tp=&arnumber=8793661)]
* StarNet: Pedestrian Trajectory Prediction using Deep Neural Network in Star Topology, IROS 2019. [[paper](https://ieeexplore.ieee.org/stamp/stamp.jsp?tp=&arnumber=8967811)]
* Deep Predictive Autonomous Driving Using Multi-Agent Joint Trajectory Prediction and Traffic Rules, IROS 2019. [[paper](https://ieeexplore.ieee.org/stamp/stamp.jsp?tp=&arnumber=8967708)]
* Conditional Generative Neural System for Probabilistic Trajectory Prediction, IROS 2019. [[paper](https://ieeexplore.ieee.org/stamp/stamp.jsp?tp=&arnumber=8967822)]
* Jointly Learnable Behavior and Trajectory Planning for Self-Driving Vehicles, IROS 2019. [[paper](https://ieeexplore.ieee.org/stamp/stamp.jsp?tp=&arnumber=8967615)]
* INFER: INtermediate representations for FuturE pRediction, IROS 2019. [[paper](https://ieeexplore.ieee.org/stamp/stamp.jsp?tp=&arnumber=8968553)] [[code](https://github.com/talsperre/INFER)] [[website](https://talsperre.github.io/INFER/)]
* Stochastic Sampling Simulation for Pedestrian Trajectory Prediction, IROS 2019. [[paper](https://ieeexplore.ieee.org/stamp/stamp.jsp?tp=&arnumber=8967857)]
* Long-term Prediction of Motion Trajectories Using Path Homology Clusters, IROS 2019. [[paper](https://ieeexplore.ieee.org/stamp/stamp.jsp?tp=&arnumber=8968125)]
* Social-BiGAT: Multimodal Trajectory Forecasting using Bicycle-GAN and Graph Attention Networks, NIPS 2019. [[paper](https://proceedings.neurips.cc/paper/2019/file/d09bf41544a3365a46c9077ebb5e35c3-Paper.pdf)]
* Multiple Futures Prediction, NIPS 2019. [[paper](https://proceedings.neurips.cc/paper/2019/file/86a1fa88adb5c33bd7a68ac2f9f3f96b-Paper.pdf)] [[code](https://github.com/apple/ml-multiple-futures-prediction)]
* Trajectory Prediction by Coupling Scene-LSTM with Human Movement LSTM, International Symposium on Visual Computing (ISVC 2019). [[paper](https://link.springer.com/content/pdf/10.1007/978-3-030-33720-9_19.pdf)]
* Pedestrian Trajectory Prediction Using a Social Pyramid, Pacific Rim International Conference on Artificial Intelligence (PRICAI 2019). [[paper](https://link.springer.com/content/pdf/10.1007/978-3-030-29911-8_34.pdf)]
* Situation-Aware Pedestrian Trajectory Prediction with Spatio-Temporal Attention Model, Computer Vision Winter Workshop (CVWW 2019). [[paper](https://arxiv.org/pdf/1902.05437.pdf)]
* Location-Velocity Attention for Pedestrian Trajectory Prediction, WACV 2019. [[paper](https://ieeexplore.ieee.org/stamp/stamp.jsp?tp=&arnumber=8659060)]
* Coordination and trajectory prediction for vehicle interactions via bayesian generative modeling, IV 2019. [[paper](https://ieeexplore.ieee.org/stamp/stamp.jsp?tp=&arnumber=8813821)]
* Wasserstein Generative Learning with Kinematic Constraints for Probabilistic Interactive Driving Behavior Prediction, IV 2019. [[paper](https://ieeexplore.ieee.org/stamp/stamp.jsp?tp=&arnumber=8813783)]
* AGen: Adaptable Generative Prediction Networks for Autonomous Driving, IV 2019. [[paper](http://www.cs.cmu.edu/~cliu6/files/iv19-1.pdf)]
* Vehicle Trajectory Prediction at Intersections using Interaction based Generative Adversarial Networks, ITSC 2019. [[paper](https://ieeexplore.ieee.org/stamp/stamp.jsp?tp=&arnumber=8916927), [paper](https://www.researchgate.net/profile/Debaditya-Roy-2/publication/337629029_Vehicle_Trajectory_Prediction_at_Intersections_using_Interaction_based_Generative_Adversarial_Networks/links/5de5e6224585159aa45cc76c/Vehicle-Trajectory-Prediction-at-Intersections-using-Interaction-based-Generative-Adversarial-Networks.pdf)]
* GRIP: Graph-based Interaction-aware Trajectory Prediction, Intelligent Transportation Systems Conference (ITSC 2019). [[paper](https://ieeexplore.ieee.org/stamp/stamp.jsp?tp=&arnumber=8917228)] [[code](https://github.com/xincoder/GRIP)]
* GRIP++: Enhanced Graph-based Interaction-aware Trajectory Prediction for Autonomous Driving, arXiv preprint arXiv:1907.07792, 2019. [[paper](https://arxiv.org/pdf/1907.07792.pdf)] [[code](https://github.com/xincoder/GRIP)]
* Pose Based Trajectory Forecast of Vulnerable Road Users, IEEE Symposium Series on Computational Intelligence (SSCI 2019). [[paper](https://ieeexplore.ieee.org/stamp/stamp.jsp?tp=&arnumber=9003023)]
* Path Predictions using Object Attributes and Semantic Environment, International Joint Conference on Computer Vision, Imaging and Computer Graphics Theory and Applications (VISIGRAPP 2019). [[paper](https://pdfs.semanticscholar.org/1d36/88ae8738335f6452147de3c2f33bcfbd81b3.pdf)]
* Probabilistic Path Planning using Obstacle Trajectory Prediction, CoDS-COMAD 2019. [[paper](https://dl.acm.org/doi/pdf/10.1145/3297001.3297006)]
* Human Trajectory Prediction using Adversarial Loss, Proceedings of the 19th Swiss Transport Research Conference 2019. [[paper](https://www.strc.ch/2019/Kothari_Alahi.pdf)] [[code](https://github.com/vita-epfl/AdversarialLoss-SGAN)]
## Journal Papers 2019
* A Scalable Framework for Trajectory Prediction, TITS. [[paper](https://ieeexplore.ieee.org/stamp/stamp.jsp?tp=&arnumber=8658195)]
* Contextual Recurrent Predictive Model for Long-Term Intent Prediction of Vulnerable Road Users, TITS. [[paper](https://ieeexplore.ieee.org/stamp/stamp.jsp?tp=&arnumber=8766889&tag=1)]
* Interactive Trajectory Prediction of Surrounding Road Users for Autonomous Driving Using Structural-LSTM Network, TITS. [[paper](https://ieeexplore.ieee.org/stamp/stamp.jsp?tp=&arnumber=8848853)]
* A Deep Learning-Based Framework for Intersectional Traffic Simulation and Editing, TVCG. [[paper](https://ieeexplore.ieee.org/stamp/stamp.jsp?tp=&arnumber=8600335)]
* Heter-Sim: Heterogeneous Multi-Agent Systems Simulation by Interactive Data-Driven Optimization, TVCG. [[paper](https://ieeexplore.ieee.org/stamp/stamp.jsp?tp=&arnumber=8865441)]
* AADS: Augmented Autonomous Driving Simulation using Data-driven Algorithms, SCIENCE ROBOTICS. [[paper](https://arxiv.org/ftp/arxiv/papers/1901/1901.07849.pdf)]
* Learning Generative Socially Aware Models of Pedestrian Motion, RAL. [[paper](https://ieeexplore.ieee.org/stamp/stamp.jsp?tp=&arnumber=8760356)]
* Pedestrian Trajectory Prediction in Extremely Crowded Scenarios, Sensors. [[paper](https://www.mdpi.com/1424-8220/19/5/1223/pdf)]
* Human trajectory prediction in crowded scene using social-affinity Long Short-Term Memory, PR. [[paper](https://www.sciencedirect.com/science/article/pii/S0031320319301712)]
## Others 2019
* Joint Interaction and Trajectory Prediction for Autonomous Driving using Graph Neural Networks, arXiv preprint arXiv:1912.07882, 2019. [[paper](https://arxiv.org/pdf/1912.07882.pdf)]
* Learning to Infer Relations for Future Trajectory Forecast, CVPR Workshops 2019. [[paper](https://openaccess.thecvf.com/content_CVPRW_2019/papers/Precognition/Choi_Learning_to_Infer_Relations_for_Future_Trajectory_Forecast_CVPRW_2019_paper.pdf)]
* Social Ways: Learning Multi-Modal Distributions of Pedestrian Trajectories, CVPR Workshops 2019. [[paper](https://openaccess.thecvf.com/content_CVPRW_2019/papers/Precognition/Amirian_Social_Ways_Learning_Multi-Modal_Distributions_of_Pedestrian_Trajectories_With_GANs_CVPRW_2019_paper.pdf)] [[code](https://github.com/crowdbotp/socialways)]
* Social and Scene-Aware Trajectory Prediction in Crowded Spaces, ICCV Workshops 2019. [[paper](https://arxiv.org/pdf/1909.08840.pdf)] [[code](https://github.com/Oghma/sns-lstm/)]
* Probabilistic Trajectory Prediction for Autonomous Vehicles with Attentive Recurrent Neural Process, arXiv preprint arXiv:1910.08102, 2019. [[paper](https://arxiv.org/pdf/1910.08102.pdf)]
* Stochastic Trajectory Prediction with Social Graph Network, arXiv preprint arXiv:1907.10233, 2019. [[paper](https://arxiv.org/pdf/1907.10233.pdf)]
# 📚 2020 Conference and Journal Papers
## Conference Papers 2020
* Spatio-Temporal Graph Transformer Networks for Pedestrian Trajectory Prediction, ECCV 2020. [[paper](https://arxiv.org/pdf/2005.08514.pdf)] [[code](https://github.com/Majiker/STAR)]
* AutoTrajectory: Label-Free Trajectory Extraction and Prediction from Videos Using Dynamic Points, ECCV 2020. [[paper](https://link.springer.com/content/pdf/10.1007/978-3-030-58601-0_38.pdf)]
* PiP: Planning-Informed Trajectory Prediction for Autonomous Driving, ECCV 2020. [[paper](https://link.springer.com/content/pdf/10.1007/978-3-030-58589-1_36.pdf)] [[code](https://github.com/Haoran-SONG/PiP-Planning-informed-Prediction)]
* SMART: Simultaneous Multi-Agent Recurrent Trajectory Prediction, ECCV 2020. [[paper](https://link.springer.com/content/pdf/10.1007/978-3-030-58583-9_28.pdf)]
* Trajectron++: Dynamically-Feasible Trajectory Forecasting with Heterogeneous Data, ECCV 2020. [[paper](https://link.springer.com/content/pdf/10.1007/978-3-030-58523-5_40.pdf)] [[code](https://github.com/StanfordASL/Trajectron-plus-plus)]
* SimAug: Learning Robust Representations from Simulation for Trajectory Prediction, ECCV 2020. [[paper](https://link.springer.com/content/pdf/10.1007/978-3-030-58601-0_17.pdf)] [[code](https://next.cs.cmu.edu/simaug/)]
* Diverse and Admissible Trajectory Forecasting Through Multimodal Context Understanding, ECCV 2020. [[paper](https://link.springer.com/content/pdf/10.1007/978-3-030-58621-8_17.pdf)] [[code](https://github.com/kami93/CMU-DATF)]
* It Is Not the Journey But the Destination: Endpoint Conditioned Trajectory Prediction, ECCV 2020. [[paper](https://link.springer.com/content/pdf/10.1007/978-3-030-58536-5_45.pdf)] [[code](https://github.com/HarshayuGirase/Human-Path-Prediction)]
* How Can I See My Future? FvTraj: Using First-Person View for Pedestrian Trajectory Prediction, ECCV 2020. [[paper](https://link.springer.com/content/pdf/10.1007/978-3-030-58571-6_34.pdf)]
* Dynamic and Static Context-Aware LSTM for Multi-agent Motion Prediction, ECCV 2020. [[paper](https://link.springer.com/content/pdf/10.1007/978-3-030-58589-1_33.pdf)]
* Learning Lane Graph Representations for Motion Forecasting, ECCV 2020. [[paper](https://link.springer.com/content/pdf/10.1007/978-3-030-58536-5_32.pdf)] [[code](https://github.com/uber-research/LaneGCN)]
* Implicit Latent Variable Model for Scene-Consistent Motion Forecasting, ECCV 2020. [[paper](https://link.springer.com/content/pdf/10.1007/978-3-030-58592-1_37.pdf)]
* Testing the Safety of Self-driving Vehicles by Simulating Perception and Prediction, ECCV 2020. [[paper](https://link.springer.com/content/pdf/10.1007/978-3-030-58574-7_19.pdf)]
* Perceive, Predict, and Plan: Safe Motion Planning Through Interpretable Semantic Representations, ECCV 2020. [[paper](https://link.springer.com/content/pdf/10.1007/978-3-030-58592-1_25.pdf)]
* Transformer Networks for Trajectory Forecasting, International Conference on Pattern Recognition (ICPR 2020). [[paper](https://ieeexplore.ieee.org/stamp/stamp.jsp?tp=&arnumber=9412190)] [[code](https://github.com/FGiuliari/Trajectory-Transformer)]
* DAG-Net: Double Attentive Graph Neural Network for Trajectory Forecasting, ICPR 2020. [[paper](https://ieeexplore.ieee.org/stamp/stamp.jsp?tp=&arnumber=9412114)] [[code](https://github.com/alexmonti19/dagnet)]
* TNT: Target-driveN Trajectory Prediction, Conference on Robot Learning (CoRL 2020). [[paper](https://arxiv.org/pdf/2008.08294.pdf)] [[code](https://github.com/Henry1iu/TNT-Trajectory-Predition)]
* Social-VRNN: One-Shot Multi-modal Trajectory Prediction for Interacting Pedestrians, CoRL 2020. [[paper](https://autonomousrobots.nl/docs/20-Brito-CoRL.pdf)] [[code](https://github.com/tud-amr/social_vrnn)]
* Kernel Trajectory Maps for Multi-Modal Probabilistic Motion Prediction, CoRL 2020. [[paper](http://proceedings.mlr.press/v100/zhi20a/zhi20a.pdf)] [[code](https://github.com/wzhi/KernelTrajectoryMaps)]
* MATS: An Interpretable Trajectory Forecasting Representation for Planning and Control, CoRL 2020. [[paper](https://arxiv.org/pdf/2009.07517)] [[code](https://github.com/StanfordASL/MATS)]
* An Attention-Based Interaction-Aware Spatio-Temporal Graph Neural Network for Trajectory Prediction, International Conference on Neural Information Processing (ICONIP 2020). [[paper](https://link.springer.com/content/pdf/10.1007/978-3-030-63823-8_5.pdf)]
* OpenTraj: Assessing Prediction Complexity in Human Trajectories Datasets, ACCV 2020. [[paper](https://openaccess.thecvf.com/content/ACCV2020/papers/Amirian_OpenTraj_Assessing_Prediction_Complexity_in_Human_Trajectories_Datasets_ACCV_2020_paper.pdf)] [[code](https://github.com/crowdbotp/OpenTraj)]
* Goal-GAN: Multimodal Trajectory Prediction Based on Goal Position Estimation, ACCV 2020. [[paper](https://arxiv.org/pdf/2010.01114.pdf)] [[code](https://github.com/dendorferpatrick/GoalGAN)]
* Semantic Synthesis of Pedestrian Locomotion, ACCV 2020. [[paper](https://openaccess.thecvf.com/content/ACCV2020/papers/Priisalu_Semantic_Synthesis_of_Pedestrian_Locomotion_ACCV_2020_paper.pdf)] [[code](https://github.com/MariaPriisalu/spl)]
* EvolveGraph: Multi-Agent Trajectory Prediction with Dynamic Relational Reasoning, NIPS 2020. [[paper](https://proceedings.neurips.cc/paper/2020/file/e4d8163c7a068b65a64c89bd745ec360-Paper.pdf)] [[website](https://jiachenli94.github.io/publications/Evolvegraph/)]
* Multi-agent Trajectory Prediction with Fuzzy Query Attention, NIPS 2020. [[paper](https://proceedings.neurips.cc/paper/2020/file/fe87435d12ef7642af67d9bc82a8b3cd-Paper.pdf)] [[code](https://github.com/nitinkamra1992/FQA)]
* Spatio-Temporal Graph Structure Learning for Traffic Forecasting, AAAI 2020. [[paper](https://ojs.aaai.org/index.php/AAAI/article/view/5470/5326)]
* GMAN: A Graph Multi-Attention Network for Traffic Prediction, AAAI 2020. [[paper](https://ojs.aaai.org/index.php/AAAI/article/view/5477/5333)] [[code](https://github.com/zhengchuanpan/GMAN)]
* CF-LSTM: Cascaded Feature-Based Long Short-Term Networks for Predicting Pedestrian Trajectory, AAAI 2020. [[paper](https://ojs.aaai.org/index.php/AAAI/article/view/6943/6797)]
* OMuLeT: Online Multi-Lead Time Location Prediction for Hurricane Trajectory Forecasting, AAAI 2020. [[paper](https://ojs.aaai.org/index.php/AAAI/article/view/5444/5300)]
* Multimodal Interaction-Aware Trajectory Prediction in Crowded Space, AAAI 2020. [[paper](https://ojs.aaai.org/index.php/AAAI/article/view/6874/6728)]
* STINet: Spatio-Temporal-Interactive Network for Pedestrian Detection and Trajectory Prediction, CVPR 2020. [[paper](https://openaccess.thecvf.com/content_CVPR_2020/papers/Zhang_STINet_Spatio-Temporal-Interactive_Network_for_Pedestrian_Detection_and_Trajectory_Prediction_CVPR_2020_paper.pdf)]
* CoverNet: Multimodal Behavior Prediction using Trajectory Sets, CVPR 2020. [[paper](https://openaccess.thecvf.com/content_CVPR_2020/papers/Phan-Minh_CoverNet_Multimodal_Behavior_Prediction_Using_Trajectory_Sets_CVPR_2020_paper.pdf)]
* TPNet: Trajectory Proposal Network for Motion Prediction, CVPR 2020. [[paper](https://openaccess.thecvf.com/content_CVPR_2020/papers/Fang_TPNet_Trajectory_Proposal_Network_for_Motion_Prediction_CVPR_2020_paper.pdf)]
* Reciprocal Learning Networks for Human Trajectory Prediction, CVPR 2020. [[paper](https://openaccess.thecvf.com/content_CVPR_2020/papers/Sun_Reciprocal_Learning_Networks_for_Human_Trajectory_Prediction_CVPR_2020_paper.pdf)]
* MANTRA: Memory Augmented Networks for Multiple Trajectory Prediction, CVPR 2020. [[paper](https://openaccess.thecvf.com/content_CVPR_2020/papers/Marchetti_MANTRA_Memory_Augmented_Networks_for_Multiple_Trajectory_Prediction_CVPR_2020_paper.pdf)]
* Recursive Social Behavior Graph for Trajectory Prediction, CVPR 2020. [[paper](https://openaccess.thecvf.com/content_CVPR_2020/papers/Sun_Recursive_Social_Behavior_Graph_for_Trajectory_Prediction_CVPR_2020_paper.pdf)]
* The Garden of Forking Paths: Towards Multi-Future Trajectory Prediction, CVPR 2020. [[paper](https://openaccess.thecvf.com/content_CVPR_2020/papers/Liang_The_Garden_of_Forking_Paths_Towards_Multi-Future_Trajectory_Prediction_CVPR_2020_paper.pdf)] [[code](https://next.cs.cmu.edu/multiverse/)]
* Social-STGCNN: A Social Spatio-Temporal Graph Convolutional Neural Network for Human Trajectory Prediction, CVPR 2020. [[paper](https://openaccess.thecvf.com/content_CVPR_2020/papers/Mohamed_Social-STGCNN_A_Social_Spatio-Temporal_Graph_Convolutional_Neural_Network_for_Human_CVPR_2020_paper.pdf)] [[code](https://github.com/abduallahmohamed/Social-STGCNN)]
* VectorNet: Encoding HD Maps and Agent Dynamics from Vectorized Representation, CVPR 2020. [[paper](https://openaccess.thecvf.com/content_CVPR_2020/papers/Gao_VectorNet_Encoding_HD_Maps_and_Agent_Dynamics_From_Vectorized_Representation_CVPR_2020_paper.pdf)] [[code](https://github.com/DQSSSSS/VectorNet)]
* Imitative Non-Autoregressive Modeling for Trajectory Forecasting and Imputation, CVPR 2020. [[paper](https://openaccess.thecvf.com/content_CVPR_2020/papers/Qi_Imitative_Non-Autoregressive_Modeling_for_Trajectory_Forecasting_and_Imputation_CVPR_2020_paper.pdf)]
* Collaborative Motion Prediction via Neural Motion Message Passing, CVPR 2020. [[paper](https://openaccess.thecvf.com/content_CVPR_2020/papers/Hu_Collaborative_Motion_Prediction_via_Neural_Motion_Message_Passing_CVPR_2020_paper.pdf)] [[code](https://github.com/PhyllisH/NMMP)]
* UST: Unifying Spatio-Temporal Context for Trajectory Prediction in Autonomous Driving, IROS 2020. [[paper](https://ieeexplore.ieee.org/stamp/stamp.jsp?tp=&arnumber=9340943)]
* Interaction-Aware Trajectory Prediction of Connected Vehicles using CNN-LSTM Networks, Annual Conference of the IEEE Industrial Electronics Society (IECON 2020). [[paper](https://ieeexplore.ieee.org/stamp/stamp.jsp?tp=&arnumber=9255162)]
* GISNet:Graph-Based Information Sharing Network For Vehicle Trajectory Prediction, International Joint Conference on Neural Networks (IJCNN 2020). [[paper](https://ieeexplore.ieee.org/stamp/stamp.jsp?tp=&arnumber=9206770)]
* Disentangling Human Dynamics for Pedestrian Locomotion Forecasting with Noisy Supervision, WACV 2020. [[paper](https://openaccess.thecvf.com/content_WACV_2020/papers/Mangalam_Disentangling_Human_Dynamics_for_Pedestrian_Locomotion_Forecasting_with_Noisy_Supervision_WACV_2020_paper.pdf)] [[website](https://karttikeya.github.io/publication/plf/)]
* Deep Imitative Models for Flexible Inference, Planning, and Control, ICLR 2020. [[paper](https://openreview.net/pdf?id=Skl4mRNYDr)] [[code](https://github.com/nrhine1/deep_imitative_models)] [[website](https://sites.google.com/view/imitative-models)]
* Diverse Trajectory Forecasting with Determinantal Point Processes, ICLR 2020. [[paper](https://arxiv.org/pdf/1907.04967.pdf)] [[code](https://github.com/Gruntrexpewrus/TrajectoryFor-and-DPP)]
* Trajectory Prediction in Heterogeneous Environment via Attended Ecology Embedding, ACM International Conference on Multimedia 2020. [[paper](http://basiclab.lab.nycu.edu.tw/assets/AEE-GAN_MM2020.pdf)] [[code](https://github.com/Ego2Eco/AEE-GAN)]
* Multiple Trajectory Prediction with Deep Temporal and Spatial Convolutional Neural Networks, IROS 2020. [[paper](http://ras.papercept.net/images/temp/IROS/files/1081.pdf)]
* Probabilistic Multi-modal Trajectory Prediction with Lane Attention for Autonomous Vehicles, IROS 2020. [[paper](https://ieeexplore.ieee.org/abstract/document/9341034/)]
* Lane-Attention: Predicting Vehicles’ Moving Trajectories by Learning Their Attention Over Lanes, IROS 2020. [[paper](https://arxiv.org/pdf/1909.13377.pdf)]
* Interaction-aware Kalman Neural Networks for Trajectory Prediction, IEEE Intelligent Vehicles Symposium (IV 2020). [[paper](https://arxiv.org/pdf/1902.10928.pdf)]
* Multi-Head Attention for Multi-Modal Joint Vehicle Motion Forecasting, ICRA 2020. [[paper](https://ieeexplore.ieee.org/stamp/stamp.jsp?tp=&arnumber=9197340)]
## Journal Papers 2020
* TrajVAE: A Variational AutoEncoder model for trajectory generation, Neurocomputing. [[paper](https://www.sciencedirect.com/science/article/pii/S0925231220312017)]
* Social-Aware Pedestrian Trajectory Prediction via States Refinement LSTM, TPAMI. [[paper](https://ieeexplore.ieee.org/stamp/stamp.jsp?tp=&arnumber=9261113)]
* Forecasting Trajectory and Behavior of Road-Agents Using Spectral Clustering in Graph-LSTMs, IEEE Robotics and Automation Letters. [[paper](https://ieeexplore.ieee.org/stamp/stamp.jsp?tp=&arnumber=9126166)]
* Attention Based Vehicle Trajectory Prediction, IEEE Transactions on Intelligent Vehicles. [[paper](https://ieeexplore.ieee.org/stamp/stamp.jsp?tp=&arnumber=9084255)]
* AC-VRNN: Attentive Conditional-VRNN for multi-future trajectory prediction, Computer Vision and Image Understanding. [[paper](https://reader.elsevier.com/reader/sd/pii/S1077314221000898?token=F06466B50D3AE170EC14D460C1AFE91DFE5D61047357252C808857A2BBD4FE4CF2FF3076AD391F842F155CAD2B102C5F&originRegion=eu-west-1&originCreation=20220421024623)]
* PoPPL: Pedestrian Trajectory Prediction by LSTM With Automatic Route Class Clustering, TNNLS. [[paper](https://ieeexplore.ieee.org/stamp/stamp.jsp?tp=&arnumber=9031707)]
* Real Time Trajectory Prediction Using Deep Conditional Generative Models, IEEE Robotics and Automation Letters. [[paper](https://ieeexplore.ieee.org/stamp/stamp.jsp?tp=&arnumber=8957482)]
* Scene Compliant Trajectory Forecast with Agent-Centric Spatio-Temporal Grids, RAL. [[paper](https://ieeexplore.ieee.org/stamp/stamp.jsp?tp=&arnumber=9000540)]
* What the Constant Velocity Model Can Teach Us About Pedestrian Motion Prediction, RAL. [[paper](https://arxiv.org/pdf/1903.07933.pdf)] [[code](https://github.com/cschoeller/constant_velocity_pedestrian_motion)]
* Multimodal Deep Generative Models for Trajectory Prediction: A Conditional Variational Autoencoder Approach, RAL. [[paper](https://ieeexplore.ieee.org/stamp/stamp.jsp?tp=&arnumber=9286482)]
* Deep Context Maps: Agent Trajectory Prediction using Location-specific Latent Maps, RAL. [[paper](http://ras.papercept.net/images/temp/IROS/files/2532.pdf)]
* Learning Structured Representations of Spatial and Interactive Dynamics for Trajectory Prediction in Crowded Scenes, RAL. [[paper](https://ieeexplore.ieee.org/stamp/stamp.jsp?tp=&arnumber=9309332)] [[code](https://github.com/tdavchev/structured-trajectory-prediction), [code](https://github.com/tdavchev/Stochastic-Futures-Prediction)]
* Probabilistic Crowd GAN: Multimodal Pedestrian Trajectory Prediction Using a Graph Vehicle-Pedestrian Attention Network, RAL. [[paper](https://ieeexplore.ieee.org/stamp/stamp.jsp?tp=&arnumber=9123560)]
* Multimodal Interaction-aware Motion Prediction for Autonomous Street Crossing, International Journal of Robotics Research. [[paper](https://arxiv.org/pdf/1808.06887)]
* Pedestrian Trajectory Prediction Based on Deep Convolutional LSTM Network, TITS. [[paper](https://ieeexplore.ieee.org/stamp/stamp.jsp?tp=&arnumber=9043898)] [[code](https://github.com/ParadiseCK/DeepConvLstmNet)]
* Multi-Vehicle Collaborative Learning for Trajectory Prediction With Spatio-Temporal Tensor Fusion, TITS. [[paper](https://ieeexplore.ieee.org/stamp/stamp.jsp?tp=&arnumber=9151374)]
* Multiple Trajectory Prediction of Moving Agents with Memory Augmented Networks, TPAMI. [[paper](https://ieeexplore.ieee.org/stamp/stamp.jsp?tp=&arnumber=9138768)]
* Regularizing Neural Networks for Future Trajectory Prediction via Inverse Reinforcement Learning Framework, IET Computer Vision. [[paper](https://ietresearch.onlinelibrary.wiley.com/doi/epdf/10.1049/iet-cvi.2019.0546)] [[code](https://github.com/d1024choi/traj-pred-irl)]
* Motion trajectory prediction based on a CNN-LSTM sequential model, Science China Information Sciences. [[paper](https://link.springer.com/content/pdf/10.1007/s11432-019-2761-y.pdf)]
## Others 2020
* Scene Gated Social Graph: Pedestrian Trajectory Prediction Based on Dynamic Social Graphs and Scene Constraints, arXiv preprint arXiv:2010.05507, 2020. [[paper](https://arxiv.org/pdf/2010.05507.pdf)]
* Robust Trajectory Forecasting for Multiple Intelligent Agents in Dynamic Scene, arXiv preprint arXiv:2005.13133, 2020. [[paper](https://arxiv.org/pdf/2005.13133.pdf)]
* Map-Adaptive Goal-Based Trajectory Prediction, arXiv preprint arXiv:2009.04450, 2020. [[paper](https://arxiv.org/pdf/2009.04450.pdf)]
* A Spatial-Temporal Attentive Network with Spatial Continuity for Trajectory Prediction, arXiv preprint arXiv:2003.06107, 2020. [[paper](https://arxiv.org/pdf/2003.06107v1.pdf)]
* Trajformer: Trajectory Prediction with Local Self-Attentive Contexts for Autonomous Driving, arXiv preprint arXiv:2011.14910, 2020. [[paper](https://arxiv.org/pdf/2011.14910.pdf)] [[code](https://github.com/Manojbhat09/Trajformer)]
* TPPO: A Novel Trajectory Predictor with Pseudo Oracle, arXiv preprint arXiv:2002.01852, 2020. [[paper](https://arxiv.org/pdf/2002.01852.pdf)]
* Vehicle Trajectory Prediction by Transfer Learning of Semi-Supervised Models, arXiv preprint arXiv:2007.06781, 2020. [[paper](https://arxiv.org/pdf/2007.06781.pdf)]
* Social-WaGDAT: Interaction-aware Trajectory Prediction via Wasserstein Graph Double-Attention Network, arXiv preprint arXiv:2002.06241, 2020. [[paper](https://arxiv.org/pdf/2002.06241.pdf)]
* Trajectory Forecasts in Unknown Environments Conditioned on Grid-Based Plans, arXiv preprint arXiv:2001.00735, 2020. [[paper](https://arxiv.org/pdf/2001.00735.pdf)] [[code](https://github.com/nachiket92/P2T)]
* Multi-modal Trajectory Prediction for Autonomous Driving with Semantic Map and Dynamic Graph Attention Network, NIPS Workshops 2020. [[paper](https://arxiv.org/pdf/2103.16273.pdf)]
* Scene Gated Social Graph: Pedestrian Trajectory Prediction Based on Dynamic Social Graphs and Scene Constraints, arXiv preprint arXiv:2010.05507, 2020. [[paper](https://arxiv.org/pdf/2010.05507v1.pdf)]
* PathGAN: Local Path Planning with Attentive Generative Adversarial Networks, arXiv preprint arXiv:2007.03877, 2020. [[paper](https://arxiv.org/pdf/2007.03877.pdf)] [[code](https://github.com/d1024choi/pathgan_pytorch)]
# 📚 2021 Conference and Journal Papers
## Conference Papers 2021
* Collaborative Uncertainty in Multi-Agent Trajectory Forecasting, NIPS 2021. [[paper](https://proceedings.neurips.cc/paper/2021/file/31ca0ca71184bbdb3de7b20a51e88e90-Paper.pdf)]
* GRIN: Generative Relation and Intention Network for Multi-agent Trajectory Prediction, NIPS 2021. [[paper](https://proceedings.neurips.cc/paper/2021/file/e3670ce0c315396e4836d7024abcf3dd-Paper.pdf)] [[code](https://github.com/longyuanli/GRIN_NeurIPS21)]
* LibCity: An Open Library for Traffic Prediction, SIGSPATIAL 2021. [[paper](https://dl.acm.org/doi/pdf/10.1145/3474717.3483923)] [[code](https://github.com/LibCity/Bigscity-LibCity)]
* Predicting Vehicles Trajectories in Urban Scenarios with Transformer Networks and Augmented Information, IEEE Intelligent Vehicles Symposium (IV 2021). [[paper](https://ieeexplore.ieee.org/stamp/stamp.jsp?tp=&arnumber=9575242)]
* Social-STAGE: Spatio-Temporal Multi-Modal Future Trajectory Forecast, ICRA 2021. [[paper](https://arxiv.org/pdf/2011.04853.pdf)]
* AVGCN: Trajectory Prediction using Graph Convolutional Networks Guided by Human Attention, ICRA 2021. [[paper](https://arxiv.org/pdf/2101.05682.pdf)]
* Exploring Dynamic Context for Multi-path Trajectory Prediction, ICRA 2021. [[paper](https://ieeexplore.ieee.org/stamp/stamp.jsp?tp=&arnumber=9562034)] [[code](https://github.com/wtliao/DCENet)]
* Pedestrian Trajectory Prediction using Context-Augmented Transformer Networks, ICRA 2021. [[paper](https://www.researchgate.net/publication/346614349_Pedestrian_Trajectory_Prediction_using_Context-Augmented_Transformer_Networks)] [[code](https://github.com/KhaledSaleh/Context-Transformer-PedTraj)]
* Spectral Temporal Graph Neural Network for Trajectory Prediction, ICRA 2021. [[paper](https://arxiv.org/pdf/2106.02930.pdf)]
* Congestion-aware Multi-agent Trajectory Prediction for Collision Avoidance, ICRA 2021. [[paper](https://ieeexplore.ieee.org/stamp/stamp.jsp?tp=&arnumber=9560994)] [[code](https://github.com/xuxie1031/CollisionFreeMultiAgentTrajectoryPrediciton)]
* Anticipatory Navigation in Crowds by Probabilistic Prediction of Pedestrian Future Movements, ICRA 2021. [[paper](https://ieeexplore.ieee.org/stamp/stamp.jsp?tp=&arnumber=9561022)]
* AgentFormer: Agent-Aware Transformers for Socio-Temporal Multi-Agent Forecasting, ICCV 2021. [[paper](https://openaccess.thecvf.com/content/ICCV2021/papers/Yuan_AgentFormer_Agent-Aware_Transformers_for_Socio-Temporal_Multi-Agent_Forecasting_ICCV_2021_paper.pdf)] [[code](https://github.com/Khrylx/AgentFormer)] [[website](https://ye-yuan.com/agentformer/)]
* Likelihood-Based Diverse Sampling for Trajectory Forecasting, ICCV 2021. [[paper](https://openaccess.thecvf.com/content/ICCV2021/papers/Jason_Likelihood-Based_Diverse_Sampling_for_Trajectory_Forecasting_ICCV_2021_paper.pdf)] [[code](https://github.com/JasonMa2016/LDS)]
* MG-GAN: A Multi-Generator Model Preventing Out-of-Distribution Samples in Pedestrian Trajectory Prediction, ICCV 2021. [[paper](https://arxiv.org/pdf/2108.09274.pdf)] [[code](https://github.com/selflein/MG-GAN)]
* Spatial-Temporal Consistency Network for Low-Latency Trajectory Forecasting, ICCV 2021. [[paper](https://openaccess.thecvf.com/content/ICCV2021/papers/Li_Spatial-Temporal_Consistency_Network_for_Low-Latency_Trajectory_Forecasting_ICCV_2021_paper.pdf)]
* Three Steps to Multimodal Trajectory Prediction: Modality Clustering, Classification and Synthesis, ICCV 2021. [[paper](https://openaccess.thecvf.com/content/ICCV2021/papers/Sun_Three_Steps_to_Multimodal_Trajectory_Prediction_Modality_Clustering_Classification_and_ICCV_2021_paper.pdf)]
* From Goals, Waypoints & Paths To Long Term Human Trajectory Forecasting, ICCV 2021. [[paper](https://openaccess.thecvf.com/content/ICCV2021/papers/Mangalam_From_Goals_Waypoints__Paths_to_Long_Term_Human_Trajectory_ICCV_2021_paper.pdf)] [[code](https://karttikeya.github.io/publication/ynet/)]
* Where are you heading? Dynamic Trajectory Prediction with Expert Goal Examples, ICCV 2021. [[paper](https://openaccess.thecvf.com/content/ICCV2021/papers/Zhao_Where_Are_You_Heading_Dynamic_Trajectory_Prediction_With_Expert_Goal_ICCV_2021_paper.pdf)] [[code](https://github.com/JoeHEZHAO/expert_traj)]
* DenseTNT: End-to-end Trajectory Prediction from Dense Goal Sets, ICCV 2021. [[paper](https://openaccess.thecvf.com/content/ICCV2021/papers/Gu_DenseTNT_End-to-End_Trajectory_Prediction_From_Dense_Goal_Sets_ICCV_2021_paper.pdf)]
* Safety-aware Motion Prediction with Unseen Vehicles for Autonomous Driving, ICCV 2021. [[paper](https://openaccess.thecvf.com/content/ICCV2021/papers/Ren_Safety-Aware_Motion_Prediction_With_Unseen_Vehicles_for_Autonomous_Driving_ICCV_2021_paper.pdf)] [[code](https://github.com/xrenaa/Safety-Aware-Motion-Prediction)]
* LOKI: Long Term and Key Intentions for Trajectory Prediction, ICCV 2021. [[paper](https://openaccess.thecvf.com/content/ICCV2021/papers/Girase_LOKI_Long_Term_and_Key_Intentions_for_Trajectory_Prediction_ICCV_2021_paper.pdf)] [[dataset](https://usa.honda-ri.com/loki)]
* Human Trajectory Prediction via Counterfactual Analysis, ICCV 2021. [[paper](https://openaccess.thecvf.com/content/ICCV2021/papers/Chen_Human_Trajectory_Prediction_via_Counterfactual_Analysis_ICCV_2021_paper.pdf)] [[code](https://github.com/CHENGY12/CausalHTP)]
* Personalized Trajectory Prediction via Distribution Discrimination, ICCV 2021. [[paper](https://openaccess.thecvf.com/content/ICCV2021/papers/Chen_Personalized_Trajectory_Prediction_via_Distribution_Discrimination_ICCV_2021_paper.pdf)] [[code](https://github.com/CHENGY12/DisDis)]
* Unlimited Neighborhood Interaction for Heterogeneous Trajectory Prediction, ICCV 2021. [[paper](https://openaccess.thecvf.com/content/ICCV2021/papers/Zheng_Unlimited_Neighborhood_Interaction_for_Heterogeneous_Trajectory_Prediction_ICCV_2021_paper.pdf)] [[code](https://github.com/zhengfang1997/Unlimited-Neighborhood-Interaction-for-Heterogeneous-Trajectory-Prediction)]
* Social NCE: Contrastive Learning of Socially-aware Motion Representations, ICCV 2021. [[paper](https://openaccess.thecvf.com/content/ICCV2021/papers/Liu_Social_NCE_Contrastive_Learning_of_Socially-Aware_Motion_Representations_ICCV_2021_paper.pdf)] [[code](https://github.com/vita-epfl/social-nce)]
* RAIN: Reinforced Hybrid Attention Inference Network for Motion Forecasting, ICCV 2021. [[paper](https://openaccess.thecvf.com/content/ICCV2021/papers/Li_RAIN_Reinforced_Hybrid_Attention_Inference_Network_for_Motion_Forecasting_ICCV_2021_paper.pdf)]
* Temporal Pyramid Network for Pedestrian Trajectory Prediction with Multi-Supervision, AAAI 2021. [[paper](https://arxiv.org/pdf/2012.01884.pdf)]
* SCAN: A Spatial Context Attentive Network for Joint Multi-Agent Intent Prediction, AAAI 2021. [[paper](https://arxiv.org/pdf/2102.00109.pdf)]
* Disentangled Multi-Relational Graph Convolutional Network for Pedestrian Trajectory Prediction, AAAI 2021. [[paper](https://www.aaai.org/AAAI21Papers/AAAI-1677.BaeI.pdf)] [[code](https://github.com/InhwanBae/DMRGCN)]
* MotionRNN: A Flexible Model for Video Prediction with Spacetime-Varying Motions, CVPR 2021. [[paper](https://openaccess.thecvf.com/content/CVPR2021/papers/Wu_MotionRNN_A_Flexible_Model_for_Video_Prediction_With_Spacetime-Varying_Motions_CVPR_2021_paper.pdf)]
* Multimodal Motion Prediction with Stacked Transformers, CVPR 2021. [[paper](https://openaccess.thecvf.com/content/CVPR2021/papers/Liu_Multimodal_Motion_Prediction_With_Stacked_Transformers_CVPR_2021_paper.pdf)] [[code](https://github.com/decisionforce/mmTransformer)] [[website](https://decisionforce.github.io/mmTransformer/?utm_source=catalyzex.com)]
* SGCN: Sparse Graph Convolution Network for Pedestrian Trajectory Prediction, CVPR 2021. [[paper](https://openaccess.thecvf.com/content/CVPR2021/papers/Shi_SGCN_Sparse_Graph_Convolution_Network_for_Pedestrian_Trajectory_Prediction_CVPR_2021_paper.pdf)] [[code](https://github.com/shuaishiliu/SGCN)]
* LaPred: Lane-Aware Prediction of Multi-Modal Future Trajectories of Dynamic Agents, CVPR 2021. [[paper](https://openaccess.thecvf.com/content/CVPR2021/papers/Kim_LaPred_Lane-Aware_Prediction_of_Multi-Modal_Future_Trajectories_of_Dynamic_Agents_CVPR_2021_paper.pdf)]
* Divide-and-Conquer for Lane-Aware Diverse Trajectory Prediction, CVPR 2021. [[paper](https://arxiv.org/pdf/2104.08277.pdf)]
* Euro-PVI: Pedestrian Vehicle Interactions in Dense Urban Centers, CVPR 2021. [[paper](https://openaccess.thecvf.com/content/CVPR2021/papers/Bhattacharyya_Euro-PVI_Pedestrian_Vehicle_Interactions_in_Dense_Urban_Centers_CVPR_2021_paper.pdf)] [[dataset](https://www.mpi-inf.mpg.de/departments/computer-vision-and-machine-learning/research/euro-pvi-dataset)]
* Trajectory Prediction with Latent Belief Energy-Based Model, CVPR 2021. [[paper](https://openaccess.thecvf.com/content/CVPR2021/papers/Pang_Trajectory_Prediction_With_Latent_Belief_Energy-Based_Model_CVPR_2021_paper.pdf)] [[code](https://github.com/bpucla/lbebm)]
* Shared Cross-Modal Trajectory Prediction for Autonomous Driving, CVPR 2021. [[paper](https://openaccess.thecvf.com/content/CVPR2021/papers/Choi_Shared_Cross-Modal_Trajectory_Prediction_for_Autonomous_Driving_CVPR_2021_paper.pdf)]
* Pedestrian and Ego-vehicle Trajectory Prediction from Monocular Camera, CVPR 2021. [[paper](https://openaccess.thecvf.com/content/CVPR2021/papers/Neumann_Pedestrian_and_Ego-Vehicle_Trajectory_Prediction_From_Monocular_Camera_CVPR_2021_paper.pdf)] [[code](https://gitlab.com/lukeN86/pedFutureTracking)]
* Interpretable Social Anchors for Human Trajectory Forecasting in Crowds, CVPR 2021. [[paper](https://openaccess.thecvf.com/content/CVPR2021/papers/Kothari_Interpretable_Social_Anchors_for_Human_Trajectory_Forecasting_in_Crowds_CVPR_2021_paper.pdf)]
* Introvert: Human Trajectory Prediction via Conditional 3D Attention, CVPR 2021. [[paper](https://openaccess.thecvf.com/content/CVPR2021/papers/Shafiee_Introvert_Human_Trajectory_Prediction_via_Conditional_3D_Attention_CVPR_2021_paper.pdf)]
* MP3: A Unified Model to Map, Perceive, Predict and Plan, CVPR 2021. [[paper](https://arxiv.org/pdf/2101.06806.pdf)]
* TrafficSim: Learning to Simulate Realistic Multi-Agent Behaviors, CVPR 2021. [[paper](https://openaccess.thecvf.com/content/CVPR2021/papers/Suo_TrafficSim_Learning_To_Simulate_Realistic_Multi-Agent_Behaviors_CVPR_2021_paper.pdf)]
* SceneGen: Learning to Generate Realistic Traffic Scenes, CVPR 2021. [[paper](https://openaccess.thecvf.com/content/CVPR2021/papers/Tan_SceneGen_Learning_To_Generate_Realistic_Traffic_Scenes_CVPR_2021_paper.pdf)]
* Multimodal Transformer Network for Pedestrian Trajectory Prediction, IJCAI 2021. [[paper](https://www.ijcai.org/proceedings/2021/0174.pdf)] [[code](https://github.com/ericyinyzy/MTN_trajectory)]
* Decoder Fusion RNN: Context and Interaction Aware Decoders for Trajectory Prediction, IROS 2021. [[paper](https://arxiv.org/pdf/2108.05814.pdf)]
* Joint Intention and Trajectory Prediction Based on Transformer, IROS 2021. [[paper](https://ieeexplore.ieee.org/stamp/stamp.jsp?tp=&arnumber=9636241)]
* Maneuver-based Trajectory Prediction for Self-driving Cars Using Spatio-temporal Convolutional Networks, IROS 2021. [[paper](https://ieeexplore.ieee.org/stamp/stamp.jsp?tp=&arnumber=9636875)]
* Multiple Contextual Cues Integrated Trajectory Prediction for Autonomous Driving, IROS 2021. [[paper](https://ieeexplore.ieee.org/stamp/stamp.jsp?tp=&arnumber=9476975)]
* MultiXNet: Multiclass Multistage Multimodal Motion Prediction, IEEE Intelligent Vehicles Symposium (IV 2021). [[paper](https://ieeexplore.ieee.org/stamp/stamp.jsp?tp=&arnumber=9575718)]
* Trajectory Prediction for Autonomous Driving based on Multi-Head Attention with Joint Agent-Map Representation, IEEE Intelligent Vehicles Symposium (IV 2021). [[paper](https://ieeexplore.ieee.org/stamp/stamp.jsp?tp=&arnumber=9576054)]
* Social-IWSTCNN: A Social Interaction-Weighted Spatio-Temporal Convolutional Neural Network for Pedestrian Trajectory Prediction in Urban Traffic Scenarios, IV 2021. [[paper](https://ieeexplore.ieee.org/abstract/document/9575958)]
* Generating Scenarios with Diverse Pedestrian Behaviors for Autonomous Vehicle Testing, Conference on Robot Learning (CoRL 2021). [[paper](https://openreview.net/pdf?id=HTfApPeT4DZ)] [[code](https://github.com/MariaPriisalu/spl)]
* Multimodal Trajectory Prediction Conditioned on Lane-Graph Traversals, CoRL 2021. [[paper](https://proceedings.mlr.press/v164/deo22a.html)] [[code](https://github.com/nachiket92/PGP)]
* Learning to Predict Vehicle Trajectories with Model-based Planning, CoRL 2021. [[paper](https://arxiv.org/pdf/2103.04027.pdf)]
* Pose Based Trajectory Forecast of Vulnerable Road Users Using Recurrent Neural Networks, International Conference on Pattern Recognition (ICPR 2021). [[paper](https://link.springer.com/content/pdf/10.1007/978-3-030-68763-2_5.pdf)]
* GraphTCN: Spatio-Temporal Interaction Modeling for Human Trajectory Prediction, WACV 2021. [[paper](https://openaccess.thecvf.com/content/WACV2021/papers/Wang_GraphTCN_Spatio-Temporal_Interaction_Modeling_for_Human_Trajectory_Prediction_WACV_2021_paper.pdf)]
* Goal-driven Long-Term Trajectory Prediction, WACV 2021. [[paper](https://openaccess.thecvf.com/content/WACV2021/papers/Tran_Goal-Driven_Long-Term_Trajectory_Prediction_WACV_2021_paper.pdf)]
* Multimodal Trajectory Predictions for Autonomous Driving without a Detailed Prior Map, WACV 2021. [[paper](https://openaccess.thecvf.com/content/WACV2021/papers/Kawasaki_Multimodal_Trajectory_Predictions_for_Autonomous_Driving_Without_a_Detailed_Prior_WACV_2021_paper.pdf)]
* Self-Growing Spatial Graph Network for Context-Aware Pedestrian Trajectory Prediction, IEEE International Conference on Image Processing (ICIP 2021). [[paper](https://arxiv.org/pdf/2012.06320v2.pdf)] [[code](https://github.com/serenetech90/AOL_ovsc)]
* S2TNet: Spatio-Temporal Transformer Networks for Trajectory Prediction in Autonomous Driving, Asian Conference on Machine Learning 2021. [[paper](https://arxiv.org/pdf/2206.10902.pdf)] [[code](https://github.com/chenghuang66/s2tnet)]
* Trajectory Prediction using Equivariant Continuous Convolution, ICLR 2021. [[paper](https://arxiv.org/pdf/2010.11344.pdf)] [[code](https://github.com/Rose-STL-Lab/ECCO)]
* TridentNet: A Conditional Generative Model for Dynamic Trajectory Generation, International Conference on Intelligent Autonomous Systems 2021. [[paper](https://link.springer.com/chapter/10.1007/978-3-030-95892-3_31#Abs1)]
* HOME: Heatmap Output for future Motion Estimation, ITSC 2021. [[paper](https://arxiv.org/pdf/2105.10968.pdf)]
* Graph and Recurrent Neural Network-based Vehicle Trajectory Prediction For Highway Driving, ITSC 2021. [[paper](https://ieeexplore.ieee.org/abstract/document/9564929)]
* SCSG Attention: A Self-Centered Star Graph with Attention for Pedestrian Trajectory Prediction, International Conference on Database Systems for Advanced Applications (DASFAA 2021). [[paper](https://link.springer.com/content/pdf/10.1007/978-3-030-73194-6_29.pdf)]
* Leveraging Trajectory Prediction for Pedestrian Video Anomaly Detection, IEEE Symposium Series on Computational Intelligence (SSCI 2021). [[paper](https://ieeexplore.ieee.org/stamp/stamp.jsp?tp=&arnumber=9660004)] [[code](https://github.com/akanuasiegbu/Leveraging-Trajectory-Prediction-for-Pedestrian-Video-Anomaly-Detection)]
## Journal Papers 2021
* Are socially-aware trajectory prediction models really socially-aware?, Transportation Research: Part C. [[paper](https://arxiv.org/pdf/2108.10879.pdf), [paper](https://iccv21-adv-workshop.github.io/short_paper/s-attack-arow2021.pdf)] [[code](https://s-attack.github.io/)]
* Injecting knowledge in data-driven vehicle trajectory predictors, Transportation Research: Part C. [[paper](https://reader.elsevier.com/reader/sd/pii/S0968090X21000425?token=F03D20769BFB255F56662C10348A81F3D07A42C6B4AB9BA19E3F7B2A5F1DA7D99B96B783616BDA86C12866AFCF4C5671&originRegion=eu-west-1&originCreation=20220506090622)] [[code](https://github.com/vita-epfl/RRB)]
* Decoding pedestrian and automated vehicle interactions using immersive virtual reality and interpretable deep learning, Transportation Research: Part C. [[paper](https://www.sciencedirect.com/science/article/pii/S0968090X2030855X)]
* Human Trajectory Forecasting in Crowds: A Deep Learning Perspective, IEEE Transactions on Intelligent Transportation Systems. [[paper](https://ieeexplore.ieee.org/stamp/stamp.jsp?tp=&arnumber=9408398)] [[code](https://github.com/vita-epfl/trajnetplusplusbaselines)]
* NetTraj: A Network-Based Vehicle Trajectory Prediction Model With Directional Representation and Spatiotemporal Attention Mechanisms, TITS. [[paper](https://ieeexplore.ieee.org/stamp/stamp.jsp?tp=&arnumber=9629362)]
* Spatio-Temporal Graph Dual-Attention Network for Multi-Agent Prediction and Tracking, TITS. [[paper](https://ieeexplore.ieee.org/stamp/stamp.jsp?tp=&arnumber=9491972)]
* A Hierarchical Framework for Interactive Behaviour Prediction of Heterogeneous Traffic Participants Based on Graph Neural Network, TITS. [[paper](https://ieeexplore.ieee.org/stamp/stamp.jsp?tp=&arnumber=9468360&tag=1)]
* TrajGAIL: Generating urban vehicle trajectories using generative adversarial imitation learning, Transportation Research Part C. [[paper](https://reader.elsevier.com/reader/sd/pii/S0968090X21001121?token=3DEACAF2AD919E99B3331E74F747B61A0EAC2741E79B6F99F4F806155EB394F163D74F2F83806358BBD65911E107EF01&originRegion=us-east-1&originCreation=20220416040814)] [[code](https://github.com/benchoi93/TrajGAIL)]
* Vehicle Trajectory Prediction Using Generative Adversarial Network With Temporal Logic Syntax Tree Features, IEEE ROBOTICS AND AUTOMATION LETTERS. [[paper](https://www.gilitschenski.org/igor/publications/202104-ral-logic_gan/ral21-logic_gan.pdf)]
* Vehicle Trajectory Prediction Using LSTMs with Spatial-Temporal Attention Mechanisms, IEEE Intelligent Transportation Systems Magazine. [[paper](http://urdata.net/files/2020_VTP.pdf)] [[code](https://github.com/leilin-research/VTP)]
* Long Short-Term Memory-Based Human-Driven Vehicle Longitudinal Trajectory Prediction in a Connected and Autonomous Vehicle Environment, Transportation Research Record. [[paper](http://sage.cnpereading.com/paragraph/download/?doi=10.1177/0361198121993471)]
* Temporal Pyramid Network with Spatial-Temporal Attention for Pedestrian Trajectory Prediction, IEEE Transactions on Network Science and Engineering. [[paper](https://ieeexplore.ieee.org/stamp/stamp.jsp?tp=&arnumber=9373939)]
* An efficient Spatial–Temporal model based on gated linear units for trajectory prediction, Neurocomputing. [[paper](https://reader.elsevier.com/reader/sd/pii/S0925231221018907?token=C894F657732BB6078B77AEC9BD3858338C1A7F1254CCC0BBC34ADA1421A95CF9A4F68BDCA8812457DE27FB37EEB8F198&originRegion=us-east-1&originCreation=20220420144432)]
* SRAI-LSTM: A Social Relation Attention-based Interaction-aware LSTM for human trajectory prediction, Neurocomputing. [[paper](https://reader.elsevier.com/reader/sd/pii/S0925231221018014?token=BB22DAAC41E3BF453C326A9D72A0CC900C2DFFD0D8AE07B7DEED51C7F2250B9CB40CC89B6812CA20DBFA6A7EDD32AAD6&originRegion=us-east-1&originCreation=20220512100647)]
* AST-GNN: An attention-based spatio-temporal graph neural network for Interaction-aware pedestrian trajectory prediction, Neurocomputing. [[paper](https://www.sciencedirect.com/science/article/pii/S092523122100388X)]
* Multi-PPTP: Multiple Probabilistic Pedestrian Trajectory Prediction in the Complex Junction Scene, IEEE Transactions on Intelligent Transportation Systems. [[paper](https://ieeexplore.ieee.org/stamp/stamp.jsp?tp=&arnumber=9619864)]
* A Novel Graph-Based Trajectory Predictor With Pseudo-Oracle, TNNLS. [[paper](https://ieeexplore.ieee.org/stamp/stamp.jsp?tp=&arnumber=9447207)]
* Large Scale GPS Trajectory Generation Using Map Based on Two Stage GAN, Journal of Data Science. [[paper](https://www.jds-online.com/files/JDS202001-08.pdf)] [[code](https://github.com/XingruiWang/Two-Stage-Gan-in-trajectory-generation)]
* Pose and Semantic Map Based Probabilistic Forecast of Vulnerable Road Users’ Trajectories, IEEE Transactions on Intelligent Vehicles. [[paper](https://ieeexplore.ieee.org/stamp/stamp.jsp?tp=&arnumber=9707640)]
* STI-GAN: Multimodal Pedestrian Trajectory Prediction Using Spatiotemporal Interactions and a Generative Adversarial Network, IEEE Access. [[paper](https://ieeexplore.ieee.org/stamp/stamp.jsp?arnumber=9387292)]
* Holistic LSTM for Pedestrian Trajectory Prediction, TIP. [[paper](https://ieeexplore.ieee.org/stamp/stamp.jsp?tp=&arnumber=9361440)]
* Pedestrian trajectory prediction with convolutional neural networks, PR. [[paper](https://www.sciencedirect.com/science/article/pii/S0031320321004325)]
* LSTM based trajectory prediction model for cyclist utilizing multiple interactions with environment, PR. [[paper](https://www.sciencedirect.com/science/article/pii/S0031320320306038)]
* Human trajectory prediction and generation using LSTM models and GANs, PR. [[paper](https://www.sciencedirect.com/science/article/pii/S003132032100323X)]
* Vehicle trajectory prediction and generation using LSTM models and GANs, Plos one. [[paper](https://journals.plos.org/plosone/article?id=10.1371/journal.pone.0253868)]
* BiTraP: Bi-Directional Pedestrian Trajectory Prediction With Multi-Modal Goal Estimation, RAL. [[paper](https://ieeexplore.ieee.org/stamp/stamp.jsp?tp=&arnumber=9345445)] [[code](https://github.com/umautobots/bidireaction-trajectory-prediction)]
* A Kinematic Model for Trajectory Prediction in General Highway Scenarios, RAL. [[paper](https://ieeexplore.ieee.org/stamp/stamp.jsp?tp=&arnumber=9472993)] [[code](https://github.com/umautobots/kinematic_highway)]
* Trajectory Prediction in Autonomous Driving With a Lane Heading Auxiliary Loss, RAL. [[paper](https://ieeexplore.ieee.org/stamp/stamp.jsp?tp=&arnumber=9387075)]
* Vehicle Trajectory Prediction Using Generative Adversarial Network With Temporal Logic Syntax Tree Features, RAL. [[paper](https://ieeexplore.ieee.org/stamp/stamp.jsp?tp=&arnumber=9366373)]
* Tra2Tra: Trajectory-to-Trajectory Prediction With a Global Social Spatial-Temporal Attentive Neural Network, RAL. [[paper](https://ieeexplore.ieee.org/stamp/stamp.jsp?tp=&arnumber=9347678)]
* Social graph convolutional LSTM for pedestrian trajectory prediction, IET Intelligent Transport Systems. [[paper](https://ietresearch.onlinelibrary.wiley.com/doi/epdf/10.1049/itr2.12033)]
* HSTA: A Hierarchical Spatio-Temporal Attention Model for Trajectory Prediction, IEEE Transactions on Vehicular Technology (TVT). [[paper](https://ieeexplore.ieee.org/stamp/stamp.jsp?tp=&arnumber=9548801)]
* Environment-Attention Network for Vehicle Trajectory Prediction, TVT. [[paper](https://ieeexplore.ieee.org/stamp/stamp.jsp?tp=&arnumber=9534487)]
* Where Are They Going? Predicting Human Behaviors in Crowded Scenes, ACM Transactions on Multimedia Computing, Communications, and Applications (TOMM). [[paper](https://dl.acm.org/doi/pdf/10.1145/3449359)]
* Multi-Agent Trajectory Prediction with Spatio-Temporal Sequence Fusion, IEEE Transactions on Multimedia (TMM). [[paper](https://ieeexplore.ieee.org/stamp/stamp.jsp?tp=&arnumber=9580659)]
## Others 2021
* Trajectory Prediction using Generative Adversarial Network in Multi-Class Scenarios, arXiv preprint arXiv:2110.11401, 2021. [[paper](https://arxiv.org/pdf/2110.11401.pdf)]
* Spatial-Channel Transformer Network for Trajectory Prediction on the Traffic Scenes, arXiv preprint arXiv:2101.11472, 2021. [[paper](https://arxiv.org/ftp/arxiv/papers/2101/2101.11472.pdf)]
* Physically Feasible Vehicle Trajectory Prediction, arXiv preprint arXiv:2104.14679, 2021. [[paper](https://arxiv.org/pdf/2104.14679.pdf)]
* MSN: Multi-Style Network for Trajectory Prediction, arXiv preprint arXiv:2107.00932, 2021. [[paper](https://arxiv.org/pdf/2107.00932.pdf)] [[code](https://github.com/NorthOcean/MSN)]
* Rethinking Trajectory Forecasting Evaluation, arXiv preprint arXiv:2107.10297, 2021. [[paper](https://arxiv.org/pdf/2107.10297)]
* Pedestrian Trajectory Prediction via Spatial Interaction Transformer Network, IEEE Intelligent Vehicles Symposium Workshops (IV Workshops 2021). [[paper](https://arxiv.org/pdf/2112.06624)]
* Deep Social Force, arXiv preprint arXiv:2109.12081, 2021. [[paper](https://arxiv.org/pdf/2109.12081)] [[code](https://github.com/svenkreiss/socialforce)]
# 📚 2022 Conference and Journal Papers
## Conference Papers 2022
* Social Interpretable Tree for Pedestrian Trajectory Prediction, AAAI 2022. [[paper](https://arxiv.org/pdf/2205.13296.pdf)] [[code](https://github.com/lssiair/SIT)]
* Complementary Attention Gated Network for Pedestrian Trajectory Prediction, AAAI 2022. [[paper](https://www.aaai.org/AAAI22Papers/AAAI-1963.DuanJ.pdf)] [[code](https://github.com/jinghaiD/CAGN)]
* Scene Transformer: A unified architecture for predicting future trajectories of multiple agents, ICLR 2022. [[paper](https://openreview.net/pdf?id=Wm3EA5OlHsG)]
* You Mostly Walk Alone: Analyzing Feature Attribution in Trajectory Prediction, ICLR 2022. [[paper](https://arxiv.org/pdf/2110.05304.pdf)]
* Latent Variable Sequential Set Transformers For Joint Multi-Agent Motion Prediction, ICLR 2022. [[paper](https://openreview.net/pdf?id=Dup_dDqkZC5)] [[code](https://fgolemo.github.io/autobots/)]
* THOMAS: Trajectory Heatmap Output with learned Multi-Agent Sampling, ICLR 2022. [[paper](https://arxiv.org/pdf/2110.06607)]
* Remember Intentions: Retrospective-Memory-based Trajectory Prediction, CVPR 2022. [[paper](https://arxiv.org/pdf/2203.11474.pdf)] [[code](https://github.com/MediaBrain-SJTU/MemoNet)]
* STCrowd: A Multimodal Dataset for Pedestrian Perception in Crowded Scenes, CVPR 2022. [[paper](https://arxiv.org/pdf/2204.01026.pdf)] [[code](https://github.com/4DVLab/STCrowd.git)]
* Vehicle trajectory prediction works, but not everywhere, CVPR 2022. [[paper](https://arxiv.org/pdf/2112.03909.pdf)] [[code](https://s-attack.github.io/)]
* Stochastic Trajectory Prediction via Motion Indeterminacy Diffusion, CVPR 2022. [[paper](https://arxiv.org/pdf/2203.13777.pdf)] [[code](https://github.com/gutianpei/MID)]
* Non-Probability Sampling Network for Stochastic Human Trajectory Prediction, CVPR 2022. [[paper](https://arxiv.org/pdf/2203.13471.pdf)] [[code](https://github.com/inhwanbae/NPSN)]
* On Adversarial Robustness of Trajectory Prediction for Autonomous Vehicles, CVPR 2022. [[paper](https://arxiv.org/pdf/2201.05057.pdf)] [[code](https://github.com/zqzqz/AdvTrajectoryPrediction)]
* Adaptive Trajectory Prediction via Transferable GNN, CVPR 2022. [[paper](https://arxiv.org/pdf/2203.05046.pdf)]
* Towards Robust and Adaptive Motion Forecasting: A Causal Representation Perspective, CVPR 2022. [[paper](https://arxiv.org/pdf/2111.14820.pdf)] [[code](https://github.com/vita-epfl/causalmotion), [code](https://github.com/sherwinbahmani/ynet_adaptive)]
* How many Observations are Enough? Knowledge Distillation for Trajectory Forecasting, CVPR 2022. [[paper](https://arxiv.org/pdf/2203.04781.pdf)]
* Learning from All Vehicles, CVPR 2022. [[paper](https://arxiv.org/pdf/2203.11934.pdf)] [[code](https://github.com/dotchen/LAV)]
* Forecasting from LiDAR via Future Object Detection, CVPR 2022. [[paper](https://arxiv.org/pdf/2203.16297.pdf)] [[code](https://github.com/neeharperi/FutureDet)]
* End-to-End Trajectory Distribution Prediction Based on Occupancy Grid Maps, CVPR 2022. [[paper](https://arxiv.org/pdf/2203.16910.pdf)] [[code](https://github.com/Kguo-cs/TDOR)]
* M2I: From Factored Marginal Trajectory Prediction to Interactive Prediction, CVPR 2022. [[paper](https://arxiv.org/pdf/2202.11884.pdf)] [[code](https://tsinghua-mars-lab.github.io/M2I/)]
* GroupNet: Multiscale Hypergraph Neural Networks for Trajectory Prediction with Relational Reasoning, CVPR 2022. [[paper](https://arxiv.org/pdf/2204.08770.pdf)] [[code](https://github.com/MediaBrain-SJTU/GroupNet)]
* Whose Track Is It Anyway? Improving Robustness to Tracking Errors with Affinity-Based Prediction, CVPR 2022. [[paper](https://xinshuoweng.com/papers/Affinipred/camera_ready.pdf)]
* ScePT: Scene-consistent, Policy-based Trajectory Predictions for Planning, CVPR 2022. [[paper](https://openaccess.thecvf.com/content/CVPR2022/papers/Chen_ScePT_Scene-Consistent_Policy-Based_Trajectory_Predictions_for_Planning_CVPR_2022_paper.pdf)] [[code](https://github.com/NVlabs/ScePT)]
* Graph-based Spatial Transformer with Memory Replay for Multi-future Pedestrian Trajectory Prediction, CVPR 2022. [[paper](https://openaccess.thecvf.com/content/CVPR2022/papers/Li_Graph-Based_Spatial_Transformer_With_Memory_Replay_for_Multi-Future_Pedestrian_Trajectory_CVPR_2022_paper.pdf)] [[code](https://github.com/Jacobieee/ST-MR)]
* MUSE-VAE: Multi-Scale VAE for Environment-Aware Long Term Trajectory Prediction, CVPR 2022. [[paper](https://openaccess.thecvf.com/content/CVPR2022/papers/Lee_MUSE-VAE_Multi-Scale_VAE_for_Environment-Aware_Long_Term_Trajectory_Prediction_CVPR_2022_paper.pdf)]
* LTP: Lane-based Trajectory Prediction for Autonomous Driving, CVPR 2022. [[paper](https://openaccess.thecvf.com/content/CVPR2022/papers/Wang_LTP_Lane-Based_Trajectory_Prediction_for_Autonomous_Driving_CVPR_2022_paper.pdf)]
* ATPFL: Automatic Trajectory Prediction Model Design under Federated Learning Framework, CVPR 2022. [[paper](https://openaccess.thecvf.com/content/CVPR2022/papers/Wang_ATPFL_Automatic_Trajectory_Prediction_Model_Design_Under_Federated_Learning_Framework_CVPR_2022_paper.pdf)]
* Human Trajectory Prediction with Momentary Observation, CVPR 2022. [[paper](https://openaccess.thecvf.com/content/CVPR2022/papers/Sun_Human_Trajectory_Prediction_With_Momentary_Observation_CVPR_2022_paper.pdf)]
* HiVT: Hierarchical Vector Transformer for Multi-Agent Motion Prediction, CVPR 2022. [[paper](https://openaccess.thecvf.com/content/CVPR2022/papers/Zhou_HiVT_Hierarchical_Vector_Transformer_for_Multi-Agent_Motion_Prediction_CVPR_2022_paper.pdf)] [[code](https://github.com/ZikangZhou/HiVT)]
* Path-Aware Graph Attention for HD Maps in Motion Prediction, ICRA 2022. [[paper](https://arxiv.org/pdf/2202.13772.pdf)]
* Trajectory Prediction with Linguistic Representations, ICRA 2022. [[paper](https://ieeexplore.ieee.org/stamp/stamp.jsp?tp=&arnumber=9811928)]
* Leveraging Smooth Attention Prior for Multi-Agent Trajectory Prediction, ICRA 2022. [[paper](https://ieeexplore.ieee.org/stamp/stamp.jsp?tp=&arnumber=9811718)] [[website](https://sites.google.com/view/smoothness-attention)]
* KEMP: Keyframe-Based Hierarchical End-to-End Deep Model for Long-Term Trajectory Prediction, ICRA 2022. [[paper](https://ieeexplore.ieee.org/document/9812337)]
* Domain Generalization for Vision-based Driving Trajectory Generation, ICRA 2022. [[paper](https://ieeexplore.ieee.org/document/9812070)] [[website](https://sites.google.com/view/dg-traj-gen)]
* A Deep Concept Graph Network for Interaction-Aware Trajectory Prediction, ICRA 2022. [[paper](https://ieeexplore.ieee.org/document/9811567)]
* Conditioned Human Trajectory Prediction using Iterative Attention Blocks, ICRA 2022. [[paper](https://ieeexplore.ieee.org/document/9812404)]
* StopNet: Scalable Trajectory and Occupancy Prediction for Urban Autonomous Driving, ICRA 2022. [[paper](https://ieeexplore.ieee.org/document/9811830)]
* Meta-path Analysis on Spatio-Temporal Graphs for Pedestrian Trajectory Prediction, ICRA 2022. [[paper](https://ieeexplore.ieee.org/document/9811632)] [[website](https://sites.google.com/illinois.edu/mesrnn/home)]
* Propagating State Uncertainty Through Trajectory Forecasting, ICRA 2022. [[paper](https://ieeexplore.ieee.org/document/9811776)] [[code](https://github.com/StanfordASL/PSU-TF)]
* HYPER: Learned Hybrid Trajectory Prediction via Factored Inference and Adaptive Sampling, ICRA 2022. [[paper](https://ieeexplore.ieee.org/document/9812254)]
* Grouptron: Dynamic Multi-Scale Graph Convolutional Networks for Group-Aware Dense Crowd Trajectory Forecasting, ICRA 2022. [[paper](https://ieeexplore.ieee.org/document/9811585)]
* Crossmodal Transformer Based Generative Framework for Pedestrian Trajectory Prediction, ICRA 2022. [[paper](https://ieeexplore.ieee.org/document/9812226)]
* Trajectory Prediction for Autonomous Driving with Topometric Map, ICRA 2022. [[paper](https://ieeexplore.ieee.org/document/9811712)] [[code](https://github.com/Jiaolong/trajectory-prediction)]
* CRAT-Pred: Vehicle Trajectory Prediction with Crystal Graph Convolutional Neural Networks and Multi-Head Self-Attention, ICRA 2022. [[paper](https://ieeexplore.ieee.org/stamp/stamp.jsp?tp=&arnumber=9811637)] [[code](https://github.com/schmidt-ju/crat-pred)]
* MultiPath++: Efficient Information Fusion and Trajectory Aggregation for Behavior Prediction, ICRA 2022. [[paper](https://ieeexplore.ieee.org/document/9812107)]
* Multi-modal Motion Prediction with Transformer-based Neural Network for Autonomous Driving, ICRA 2022. [[paper](https://ieeexplore.ieee.org/document/9812060/)]
* GOHOME: Graph-Oriented Heatmap Output for future Motion Estimation, ICRA 2022. [[paper](https://arxiv.org/pdf/2109.01827.pdf)]
* TridentNetV2: Lightweight Graphical Global Plan Representations for Dynamic Trajectory Generation, ICRA 2022. [[paper](https://ieeexplore.ieee.org/stamp/stamp.jsp?arnumber=9811591)]
* Heterogeneous-Agent Trajectory Forecasting Incorporating Class Uncertainty, IROS 2022. [[paper](https://arxiv.org/pdf/2104.12446.pdf)] [[code](https://github.com/TRI-ML/HAICU)] [[trajdata](https://github.com/nvr-avg/trajdata)]
* Trajectory Prediction with Graph-based Dual-scale Context Fusion, IROS 2022. [[paper](https://arxiv.org/pdf/2111.01592.pdf)] [[code](https://github.com/HKUST-Aerial-Robotics/DSP)]
* Learning Pedestrian Group Representations for Multi-modal Trajectory Prediction, ECCV 2022. [[paper](https://arxiv.org/pdf/2207.09953.pdf)] [[code](https://github.com/InhwanBae/GPGraph)]
* Social-Implicit: Rethinking Trajectory Prediction Evaluation and The Effectiveness of Implicit Maximum Likelihood Estimation, ECCV 2022. [[paper](https://arxiv.org/pdf/2203.03057.pdf)] [[code](https://github.com/abduallahmohamed/Social-Implicit)] [[website](https://www.abduallahmohamed.com/social-implicit-amdamv-adefde-demo)]
* Hierarchical Latent Structure for Multi-Modal Vehicle Trajectory Forecasting, ECCV 2022. [[paper](https://arxiv.org/pdf/2207.04624.pdf)] [[code](https://github.com/d1024choi/HLSTrajForecast)]
* SocialVAE: Human Trajectory Prediction using Timewise Latents, ECCV 2022. [[paper](https://arxiv.org/pdf/2203.08207.pdf)] [[code](https://github.com/xupei0610/SocialVAE)]
* View Vertically: A Hierarchical Network for Trajectory Prediction via Fourier Spectrums, ECCV 2022. [[paper](https://arxiv.org/pdf/2110.07288.pdf)] [[code](https://github.com/cocoon2wong/Vertical)]
* Entry-Flipped Transformer for Inference and Prediction of Participant Behavior, ECCV 2022. [[paper](https://arxiv.org/pdf/2207.06235.pdf)]
* D2-TPred: Discontinuous Dependency for Trajectory Prediction under Traffic Lights, ECCV 2022. [[paper](https://arxiv.org/pdf/2207.10398.pdf)] [[code](https://github.com/VTP-TL/D2-TPred)]
* Human Trajectory Prediction via Neural Social Physics, ECCV 2022. [[paper](https://arxiv.org/pdf/2207.10435.pdf)] [[code](https://github.com/realcrane/Human-Trajectory-Prediction-via-Neural-Social-Physics)]
* Social-SSL: Self-Supervised Cross-Sequence Representation Learning Based on Transformers for Multi-Agent Trajectory Prediction, ECCV 2022. [[paper](https://basiclab.lab.nycu.edu.tw/assets/Social-SSL.pdf)] [[code](https://github.com/Sigta678/Social-SSL)]
* Aware of the History: Trajectory Forecasting with the Local Behavior Data, ECCV 2022. [[paper](https://arxiv.org/pdf/2207.09646.pdf)] [[code](https://github.com/Kay1794/Aware-of-the-history)]
* Action-based Contrastive Learning for Trajectory Prediction, ECCV 2022. [[paper](https://arxiv.org/pdf/2207.08664.pdf)]
* AdvDO: Realistic Adversarial Attacks for Trajectory Prediction, ECCV 2022. [[paper](https://arxiv.org/pdf/2209.08744.pdf)]
* ST-P3: End-to-end Vision-based Autonomous Driving via Spatial-Temporal Feature Learning, ECCV 2022. [[paper](https://arxiv.org/pdf/2207.07601.pdf)] [[code](https://github.com/OpenPerceptionX/ST-P3)]
* Social ODE: Multi-Agent Trajectory Forecasting with Neural Ordinary Differential Equations, ECCV 2022. [[paper](https://www.ecva.net/papers/eccv_2022/papers_ECCV/papers/136820211.pdf)]
* Forecasting Human Trajectory from Scene History, NIPS 2022. [[paper](https://arxiv.org/pdf/2210.08732.pdf)] [[code](https://github.com/MaKaRuiNah/SHENet)]
* Trajectory-guided Control Prediction for End-to-end Autonomous Driving: A Simple yet Strong Baseline, NIPS 2022. [[paper](https://arxiv.org/pdf/2206.08129)] [[code](https://github.com/OpenPerceptionX/TCP)]
* Motion Transformer with Global Intention Localization and Local Movement Refinement, NIPS 2022. [[paper](https://arxiv.org/pdf/2209.13508.pdf)] [[website](https://vas.mpi-inf.mpg.de/motion-transformer-with-global-intention-localization-and-local-movement-refinement/)]
* Interaction Modeling with Multiplex Attention, NIPS 2022. [[paper](https://arxiv.org/pdf/2208.10660.pdf)] [[code](https://github.com/fanyun-sun/IMMA)]
* Deep Interactive Motion Prediction and Planning: Playing Games with Motion Prediction Models, Conference on Learning for Dynamics and Control (L4DC). [[paper](https://arxiv.org/pdf/2204.02392.pdf)] [[website](https://sites.google.com/view/deep-interactive-predict-plan)]
* Robust Trajectory Prediction against Adversarial Attacks, CoRL 2022. [[paper](https://arxiv.org/pdf/2208.00094.pdf)] [[code](https://robustav.github.io/RobustTraj/)]
* Planning with Diffusion for Flexible Behavior Synthesis, ICML 2022. [[paper](https://arxiv.org/abs/2205.09991)] [[website](https://diffusion-planning.github.io/)]
* Synchronous Bi-Directional Pedestrian Trajectory Prediction with Error Compensation, ACCV 2022. [[paper](https://openaccess.thecvf.com/content/ACCV2022/papers/Xie_Synchronous_Bi-Directional_Pedestrian_Trajectory_Prediction_with_Error_Compensation_ACCV_2022_paper.pdf)]
* Model-Based Imitation Learning for Urban Driving, NIPS 2022. [[paper](https://proceedings.neurips.cc/paper_files/paper/2022/file/827cb489449ea216e4a257c47e407d18-Paper-Conference.pdf)] [[code](https://github.com/wayveai/mile)]
## Journal Papers 2022
* AI-TP: Attention-based Interaction-aware Trajectory Prediction for Autonomous Driving, IEEE Transactions on Intelligent Vehicles. [[paper](https://ieeexplore.ieee.org/stamp/stamp.jsp?tp=&arnumber=9723649)] [[code](https://github.com/KP-Zhang/AI-TP)]
* MDST-DGCN: A Multilevel Dynamic Spatiotemporal Directed Graph Convolutional Network for Pedestrian Trajectory Prediction, Computational Intelligence and Neuroscience. [[paper](https://downloads.hindawi.com/journals/cin/2022/4192367.pdf)]
* Graph-Based Spatial-Temporal Convolutional Network for Vehicle Trajectory Prediction in Autonomous Driving, TITS. [[paper](https://ieeexplore.ieee.org/stamp/stamp.jsp?tp=&arnumber=9737058)]
* Multi-Agent Trajectory Prediction with Heterogeneous Edge-Enhanced Graph Attention Network, TITS. [[paper](https://dspace.lib.cranfield.ac.uk/bitstream/handle/1826/17541/Multi-agent_trajectory_prediction-2022.pdf?sequence=1&isAllowed=y)]
* Fully Convolutional Encoder-Decoder With an Attention Mechanism for Practical Pedestrian Trajectory Prediction, TITS. [[paper](https://ieeexplore.ieee.org/stamp/stamp.jsp?tp=&arnumber=9768201)]
* STGM: Vehicle Trajectory Prediction Based on Generative Model for Spatial-Temporal Features, TITS. [[paper](https://ieeexplore.ieee.org/stamp/stamp.jsp?tp=&arnumber=9743363)]
* Trajectory Prediction for Autonomous Driving Using Spatial-Temporal Graph Attention Transformer, TITS. [[paper](https://ieeexplore.ieee.org/stamp/stamp.jsp?tp=&arnumber=9768029)]
* Intention-Aware Vehicle Trajectory Prediction Based on Spatial-Temporal Dynamic Attention Network for Internet of Vehicles, TITS. [[paper](https://ieeexplore.ieee.org/stamp/stamp.jsp?tp=&arnumber=9767719)] [[code](https://xbchen82.github.io/resource/)]
* Trajectory Forecasting Based on Prior-Aware Directed Graph Convolutional Neural Network, TITS. [[paper](https://ieeexplore.ieee.org/stamp/stamp.jsp?tp=&arnumber=9686621&tag=1)]
* DeepTrack: Lightweight Deep Learning for Vehicle Trajectory Prediction in Highways, TITS. [[paper](https://ieeexplore.ieee.org/stamp/stamp.jsp?tp=&arnumber=9770480)]
* Interactive Trajectory Prediction Using a Driving Risk Map-Integrated Deep Learning Method for Surrounding Vehicles on Highways, TITS. [[paper](https://ieeexplore.ieee.org/stamp/stamp.jsp?tp=&arnumber=9745461&tag=1)]
* Vehicle Trajectory Prediction in Connected Environments via Heterogeneous Context-Aware Graph Convolutional Networks, TITS. [[paper](https://ieeexplore.ieee.org/stamp/stamp.jsp?tp=&arnumber=9781338)]
* Trajectory Prediction Neural Network and Model Interpretation Based on Temporal Pattern Attention, TITS. [[paper](https://ieeexplore.ieee.org/stamp/stamp.jsp?tp=&arnumber=9945660)]
* Learning Sparse Interaction Graphs of Partially Detected Pedestrians for Trajectory Prediction, RAL. [[paper](https://ieeexplore.ieee.org/stamp/stamp.jsp?tp=&arnumber=9664278)] [[code](https://github.com/tedhuang96/gst)]
* GAMMA: A General Agent Motion Prediction Model for Autonomous Driving, RAL. [[paper](https://arxiv.org/pdf/1906.01566.pdf)] [[code](https://github.com/AdaCompNUS/gamma)]
* Stepwise Goal-Driven Networks for Trajectory Prediction, RAL. [[paper](https://arxiv.org/pdf/2103.14107v3.pdf)] [[code](https://github.com/ChuhuaW/SGNet.pytorch)]
* GA-STT: Human Trajectory Prediction with Group Aware Spatial-Temporal Transformer, RAL. [[paper](https://ieeexplore.ieee.org/stamp/stamp.jsp?tp=&arnumber=9779572)]
* Long-term 4D trajectory prediction using generative adversarial networks, Transportation Research Part C: Emerging Technologies. [[paper](https://www.sciencedirect.com/science/article/pii/S0968090X22000031)]
* A context-aware pedestrian trajectory prediction framework for automated vehicles, Transportation Research Part C: Emerging Technologies. [[paper](https://www.sciencedirect.com/science/article/pii/S0968090X21004423)]
* Explainable multimodal trajectory prediction using attention models, Transportation Research Part C: Emerging Technologies. [[paper](https://www.sciencedirect.com/science/article/pii/S0968090X22002509)]
* CSCNet: Contextual semantic consistency network for trajectory prediction in crowded spaces, PR. [[paper](https://www.sciencedirect.com/science/article/pii/S0031320322000334)]
* CSR: Cascade Conditional Variational AutoEncoder with Social-aware Regression for Pedestrian Trajectory Prediction, PR. [[paper](https://www.sciencedirect.com/science/article/pii/S0031320322005106)]
* Step Attention: Sequential Pedestrian Trajectory Prediction, IEEE Sensors Journal. [[paper](https://ieeexplore.ieee.org/stamp/stamp.jsp?tp=&arnumber=9732437)]
* Vehicle Trajectory Prediction Method Coupled With Ego Vehicle Motion Trend Under Dual Attention Mechanism, IEEE Transactions on Instrumentation and Measurement. [[paper](https://ieeexplore.ieee.org/stamp/stamp.jsp?tp=&arnumber=9749176)]
* Spatio-temporal Interaction Aware and Trajectory Distribution Aware Graph Convolution Network for Pedestrian Multimodal Trajectory Prediction, IEEE Transactions on Instrumentation and Measurement. [[paper](https://ieeexplore.ieee.org/stamp/stamp.jsp?tp=&arnumber=9997233)]
* Deep encoder–decoder-NN: A deep learning-based autonomous vehicle trajectory prediction and correction model, Physica A: Statistical Mechanics and its Applications. [[paper](https://www.sciencedirect.com/science/article/pii/S0378437122000139)]
* PTPGC: Pedestrian trajectory prediction by graph attention network with ConvLSTM, Robotics and Autonomous Systems. [[paper](https://www.sciencedirect.com/science/article/pii/S0921889021002165)]
* GCHGAT: pedestrian trajectory prediction using group constrained hierarchical graph attention networks, Applied Intelligence. [[paper](https://link.springer.com/article/10.1007/s10489-021-02997-w)]
* Vehicles Trajectory Prediction Using Recurrent VAE Network, IEEE Access. [[paper](https://ieeexplore.ieee.org/stamp/stamp.jsp?tp=&arnumber=9740177)] [[code](https://github.com/midemig/traj_pred_vae)]
* SEEM: A Sequence Entropy Energy-Based Model for Pedestrian Trajectory All-Then-One Prediction, TPAMI. [[paper](https://ieeexplore.ieee.org/stamp/stamp.jsp?tp=&arnumber=9699076)]
* PTP-STGCN: Pedestrian Trajectory Prediction Based on a Spatio-temporal Graph Convolutional Neural Network, Applied Intelligence. [[paper](https://link.springer.com/article/10.1007/s10489-022-03524-1)]
* Trajectory distributions: A new description of movement for trajectory prediction, Computational Visual Media. [[paper](https://link.springer.com/content/pdf/10.1007/s41095-021-0236-6.pdf)]
* Trajectory prediction for autonomous driving based on multiscale spatial-temporal graph, IET Intelligent Transport Systems. [[paper](https://ietresearch.onlinelibrary.wiley.com/doi/pdfdirect/10.1049/itr2.12265)]
* Continual learning-based trajectory prediction with memory augmented networks, Knowledge-Based Systems. [[paper](https://www.sciencedirect.com/science/article/pii/S0950705122011157)]
* Atten-GAN: Pedestrian Trajectory Prediction with GAN Based on Attention Mechanism, Cognitive Computation. [[paper](https://link.springer.com/article/10.1007/s12559-022-10029-z#Abs1)]
* EvoSTGAT: Evolving spatiotemporal graph attention networks for pedestrian trajectory prediction, Neurocomputing. [[paper](https://www.sciencedirect.com/science/article/pii/S0925231222003460?ref=pdf_download&fr=RR-2&rr=7da0ead45e800fcc)]
## Others 2022
* Raising context awareness in motion forecasting, CVPR Workshops 2022. [[paper](https://arxiv.org/pdf/2109.08048.pdf)] [[code](https://github.com/valeoai/CAB)]
* Goal-driven Self-Attentive Recurrent Networks for Trajectory Prediction, CVPR Workshops 2022. [[paper](https://arxiv.org/pdf/2204.11561.pdf)] [[code](https://github.com/luigifilippochiara/Goal-SAR)]
* Importance Is in Your Attention: Agent Importance Prediction for Autonomous Driving, CVPR Workshops 2022. [[paper](https://arxiv.org/pdf/2204.09121.pdf)]
* MPA: MultiPath++ Based Architecture for Motion Prediction, CVPR Workshops 2022. [[paper](https://arxiv.org/pdf/2206.10041.pdf)] [[code](https://github.com/stepankonev/waymo-motion-prediction-challenge-2022-multipath-plus-plus)]
* TPAD: Identifying Effective Trajectory Predictions Under the Guidance of Trajectory Anomaly Detection Model, arXiv:2201.02941, 2022. [[paper](https://arxiv.org/pdf/2201.02941v1.pdf)]
* Wayformer: Motion Forecasting via Simple & Efficient Attention Networks, arXiv preprint arXiv:2207.05844, 2022. [[paper](https://arxiv.org/pdf/2207.05844.pdf)]
* PreTR: Spatio-Temporal Non-Autoregressive Trajectory Prediction Transformer, arXiv preprint arXiv:2203.09293, 2022. [[paper](https://arxiv.org/pdf/2203.09293.pdf)]
* LatentFormer: Multi-Agent Transformer-Based Interaction Modeling and Trajectory Prediction, arXiv preprint arXiv:2203.01880, 2022. [[paper](https://arxiv.org/pdf/2203.01880.pdf)]
* Diverse Multiple Trajectory Prediction Using a Two-stage Prediction Network Trained with Lane Loss, arXiv preprint arXiv:2206.08641, 2022. [[paper](https://arxiv.org/pdf/2206.08641.pdf)]
* Semi-supervised Semantics-guided Adversarial Training for Trajectory Prediction, arXiv preprint arXiv:2205.14230, 2022. [[paper](https://arxiv.org/pdf/2205.14230.pdf)]
* Heterogeneous Trajectory Forecasting via Risk and Scene Graph Learning, arXiv preprint arXiv:2211.00848, 2022. [[paper](https://arxiv.org/pdf/2211.00848.pdf)]
* GATraj: A Graph- and Attention-based Multi-Agent Trajectory Prediction Model, arXiv preprint arXiv:2209.07857, 2022. [[paper](https://arxiv.org/pdf/2209.07857.pdf)] [[code](https://github.com/mengmengliu1998/GATraj)]
* Dynamic-Group-Aware Networks for Multi-Agent Trajectory Prediction with Relational Reasoning, arXiv preprint arXiv:2206.13114, 2022. [[paper](https://arxiv.org/pdf/2206.13114.pdf)]
* Collaborative Uncertainty Benefits Multi-Agent Multi-Modal Trajectory Forecasting, arXiv preprint arXiv:2207.05195, 2022. [[paper](https://arxiv.org/abs/2207.05195)] [[code](https://github.com/MediaBrain-SJTU/Collaborative-Uncertainty)]
* Guided Conditional Diffusion for Controllable Traffic Simulation, arXiv preprint arXiv:2210.17366, 2022. [[paper](https://arxiv.org/pdf/2210.17366.pdf)] [[website](https://aiasd.github.io/ctg.github.io/)]
* PhysDiff: Physics-Guided Human Motion Diffusion Model, arXiv preprint arXiv:2212.02500, 2022. [[paper](http://xxx.itp.ac.cn/pdf/2212.02500.pdf)]
* Trajectory Forecasting on Temporal Graphs, arXiv preprint arXiv:2207.00255, 2022. [[paper](https://arxiv.org/pdf/2207.00255.pdf)] [[website](https://kuis-ai.github.io/ftgn/)]
# 📚 2023 Conference and Journal Papers
## Conference Papers 2023
* Human Joint Kinematics Diffusion-Refinement for Stochastic Motion Prediction, AAAI 2023. [[paper](https://arxiv.org/pdf/2210.05976.pdf)]
* Multi-stream Representation Learning for Pedestrian Trajectory Prediction, AAAI 2023. [[paper](https://ojs.aaai.org/index.php/AAAI/article/view/25389)] [[code](https://github.com/YuxuanIAIR/MSRL-master)]
* Continuous Trajectory Generation Based on Two-Stage GAN, AAAI 2023. [[paper](https://arxiv.org/pdf/2301.07103.pdf)] [[code](https://github.com/WenMellors/TS-TrajGen)]
* A Set of Control Points Conditioned Pedestrian Trajectory Prediction, AAAI 2023. [[paper](https://assets.underline.io/lecture/67747/paper/82988b653861eb7a0d5cdc91c4b26f8c.pdf)] [[code](https://github.com/InhwanBae/GraphTERN)]
* Leveraging Future Relationship Reasoning for Vehicle Trajectory Prediction, ICLR 2023. [[paper](https://openreview.net/forum?id=CGBCTp2M6lA)]
* IPCC-TP: Utilizing Incremental Pearson Correlation Coefficient for Joint Multi-Agent Trajectory Prediction, CVPR 2023. [[paper](https://arxiv.org/pdf/2303.00575.pdf)]
* FEND: A Future Enhanced Distribution-Aware Contrastive Learning Framework for Long-tail Trajectory Prediction, CVPR 2023. [[paper](https://arxiv.org/pdf/2303.16574.pdf)]
* Trace and Pace: Controllable Pedestrian Animation via Guided Trajectory Diffusion, CVPR 2023. [[paper](https://nv-tlabs.github.io/trace-pace/docs/trace_and_pace.pdf)] [[website](https://nv-tlabs.github.io/trace-pace/)]
* FJMP: Factorized Joint Multi-Agent Motion Prediction over Learned Directed Acyclic Interaction Graphs, CVPR 2023. [[paper](https://arxiv.org/pdf/2211.16197.pdf)] [[website](https://rluke22.github.io/FJMP/)]
* Leapfrog Diffusion Model for Stochastic Trajectory Prediction, CVPR 2023. [[paper](https://arxiv.org/pdf/2303.10895.pdf)] [[code](https://github.com/MediaBrain-SJTU/LED)]
* ViP3D: End-to-end Visual Trajectory Prediction via 3D Agent Queries, CVPR 2023. [[paper](http://xxx.itp.ac.cn/pdf/2208.01582.pdf)] [[website](https://tsinghua-mars-lab.github.io/ViP3D/)]
* EqMotion: Equivariant Multi-Agent Motion Prediction with Invariant Interaction Reasoning, CVPR 2023. [[paper](https://arxiv.org/pdf/2303.10876.pdf)] [[code](https://github.com/MediaBrain-SJTU/EqMotion)]
* Uncovering the Missing Pattern: Unified Framework Towards Trajectory Imputation and Prediction, CVPR 2023. [[paper](http://xxx.itp.ac.cn/pdf/2303.16005.pdf)]
* Unsupervised Sampling Promoting for Stochastic Human Trajectory Prediction, CVPR 2023. [[paper](https://chengy12.github.io/files/Bosampler.pdf)] [[code](https://github.com/viewsetting/Unsupervised_sampling_promoting)]
* Stimulus Verification is a Universal and Effective Sampler in Multi-modal Human Trajectory Prediction, CVPR 2023. [[paper](https://openaccess.thecvf.com/content/CVPR2023/papers/Sun_Stimulus_Verification_Is_a_Universal_and_Effective_Sampler_in_Multi-Modal_CVPR_2023_paper.pdf)]
* Query-Centric Trajectory Prediction, CVPR 2023. [[paper](https://openaccess.thecvf.com/content/CVPR2023/papers/Zhou_Query-Centric_Trajectory_Prediction_CVPR_2023_paper.pdf)] [[code](https://github.com/ZikangZhou/QCNet)] [[QCNeXt](https://arxiv.org/pdf/2306.10508.pdf)]
* Weakly Supervised Class-agnostic Motion Prediction for Autonomous Driving, CVPR 2023. [[paper](https://openaccess.thecvf.com/content/CVPR2023/papers/Li_Weakly_Supervised_Class-Agnostic_Motion_Prediction_for_Autonomous_Driving_CVPR_2023_paper.pdf)]
* Decompose More and Aggregate Better: Two Closer Looks at Frequency Representation Learning for Human Motion Prediction, CVPR 2023. [[paper](https://openaccess.thecvf.com/content/CVPR2023/papers/Gao_Decompose_More_and_Aggregate_Better_Two_Closer_Looks_at_Frequency_CVPR_2023_paper.pdf)]
* MotionDiffuser: Controllable Multi-Agent Motion Prediction using Diffusion, CVPR 2023. [[paper](https://openaccess.thecvf.com/content/CVPR2023/papers/Jiang_MotionDiffuser_Controllable_Multi-Agent_Motion_Prediction_Using_Diffusion_CVPR_2023_paper.pdf)]
* Planning-oriented Autonomous Driving, CVPR 2023. [[paper](https://openaccess.thecvf.com/content/CVPR2023/papers/Hu_Planning-Oriented_Autonomous_Driving_CVPR_2023_paper.pdf)] [[code](https://github.com/OpenDriveLab/UniAD)]
* TrafficGen: Learning to Generate Diverse and Realistic Traffic Scenarios, ICRA 2023. [[paper](https://arxiv.org/pdf/2210.06609.pdf)] [[code](https://github.com/metadriverse/trafficgen)]
* GANet: Goal Area Network for Motion Forecasting, ICRA 2023. [[paper](https://arxiv.org/pdf/2209.09723.pdf)] [[code](https://github.com/kingwmk/GANet)]
* TOFG: A Unified and Fine-Grained Environment Representation in Autonomous Driving, ICRA 2023. [[paper](https://arxiv.org/pdf/2305.20068.pdf)]
* SSL-Lanes: Self-Supervised Learning for Motion Forecasting in Autonomous Driving, CoRL 2023. [[paper](https://arxiv.org/pdf/2206.14116.pdf)] [[code](https://github.com/AutoVision-cloud/SSL-Lanes)]
* PowerBEV: A Powerful Yet Lightweight Framework for Instance Prediction in Bird’s-Eye View, IJCAI 2023. [[paper](https://arxiv.org/pdf/2306.10761.pdf)] [[code](https://github.com/EdwardLeeLPZ/PowerBEV)]
* HumanMAC: Masked Motion Completion for Human Motion Prediction, ICCV 2023. [[paper](https://arxiv.org/abs/2302.03665)] [[code](https://github.com/LinghaoChan/HumanMAC)]
* BeLFusion: Latent Diffusion for Behavior-Driven Human Motion Prediction, ICCV 2023. [[paper](https://arxiv.org/abs/2211.14304)] [[code](https://github.com/BarqueroGerman/BeLFusion)]
* EigenTrajectory: Low-Rank Descriptors for Multi-Modal Trajectory Forecasting, ICCV 2023. [[paper](https://arxiv.org/abs/2307.09306)] [[code](https://github.com/InhwanBae/EigenTrajectory)]
* ADAPT: Efficient Multi-Agent Trajectory Prediction with Adaptation, ICCV 2023. [[paper](https://arxiv.org/pdf/2307.14187.pdf)] [[code](https://kuis-ai.github.io/adapt/)]
* Evaluation of Differentially Constrained Motion Models for Graph-Based Trajectory Prediction, IV 2023. [[paper](https://arxiv.org/abs/2304.05116)] [[code](https://github.com/westny/mtp-go)]
* LimSim: A Long-term Interactive Multi-scenario Traffic Simulator, ITSC 2023. [[paper](https://arxiv.org/pdf/2307.06648.pdf)] [[code](https://github.com/PJLab-ADG/LimSim)]
* V2X-Seq: A Large-Scale Sequential Dataset for Vehicle-Infrastructure Cooperative Perception and Forecasting, CVPR 2023. [[paper](https://openaccess.thecvf.com/content/CVPR2023/papers/Yu_V2X-Seq_A_Large-Scale_Sequential_Dataset_for_Vehicle-Infrastructure_Cooperative_Perception_and_CVPR_2023_paper.pdf)] [[code](https://github.com/AIR-THU/DAIR-V2X-Seq)]
* INT2: Interactive Trajectory Prediction at Intersections, ICCV 2023. [[paper](https://openaccess.thecvf.com/content/ICCV2023/papers/Yan_INT2_Interactive_Trajectory_Prediction_at_Intersections_ICCV_2023_paper.pdf)] [[code](https://github.com/AIR-DISCOVER/INT2)]
* Trajectory Unified Transformer for Pedestrian Trajectory Prediction, ICCV 2023. [[paper](https://openaccess.thecvf.com/content/ICCV2023/papers/Shi_Trajectory_Unified_Transformer_for_Pedestrian_Trajectory_Prediction_ICCV_2023_paper.pdf)]
* Sparse Instance Conditioned Multimodal Trajectory Prediction, ICCV 2023. [[paper](https://openaccess.thecvf.com/content/ICCV2023/papers/Dong_Sparse_Instance_Conditioned_Multimodal_Trajectory_Prediction_ICCV_2023_paper.pdf)]
* MotionLM: Multi-Agent Motion Forecasting as Language Modeling, ICCV 2023. [[paper](https://openaccess.thecvf.com/content/ICCV2023/papers/Seff_MotionLM_Multi-Agent_Motion_Forecasting_as_Language_Modeling_ICCV_2023_paper.pdf)]
* ADAPT: Action-aware Driving Caption Transformer, ICRA 2023. [[paper](https://browse.arxiv.org/pdf/2302.00673.pdf)] [[code](https://github.com/jxbbb/ADAPT)]
* Scenario Diffusion: Controllable Driving Scenario Generation With Diffusion, NIPS 2023. [[paper](https://openreview.net/pdf?id=99MHSB98yZ)]
* BCDiff: Bidirectional Consistent Diffusion for Instantaneous Trajectory Prediction, NIPS 2023. [[paper](https://openreview.net/pdf?id=FOFJmR1oxt)]
* Conditional Variational Inference for Multi-modal Trajectory Prediction with Latent Diffusion Prior, Pacific Rim International Conference on Artificial Intelligence (PRICAI 2023). [[paper](https://link.springer.com/chapter/10.1007/978-981-99-7019-3_2)]
* Language-Guided Traffic Simulation via Scene-Level Diffusion, CoRL 2023. [[paper](https://arxiv.org/pdf/2306.06344.pdf)]
* Language Conditioned Traffic Generation, CoRL 2023. [[paper](https://arxiv.org/pdf/2307.07947)] [[code](https://ariostgx.github.io/lctgen/)]
* LightSim: Neural Lighting Simulation for Urban Scenes, NIPS 2023. [[paper](https://openreview.net/pdf?id=mcx8IGneYw)] [[website](https://waabi.ai/lightsim/)]
* What Truly Matters in Trajectory Prediction for Autonomous Driving? NIPS 2023. [[paper](https://arxiv.org/pdf/2306.15136.pdf)] [[code](https://whatmatters23.github.io/)]
## Journal Papers 2023
* MVHGN: Multi-View Adaptive Hierarchical Spatial Graph Convolution Network Based Trajectory Prediction for Heterogeneous Traffic-Agents, TITS. [[paper](https://ieeexplore.ieee.org/stamp/stamp.jsp?tp=&arnumber=10056303)]
* Adaptive and Simultaneous Trajectory Prediction for Heterogeneous Agents via Transferable Hierarchical Transformer Network, TITS. [[paper](https://ieeexplore.ieee.org/stamp/stamp.jsp?tp=&arnumber=10149109)]
* SSAGCN: Social Soft Attention Graph Convolution Network for Pedestrian Trajectory Prediction, TNNLS. [[paper](https://ieeexplore.ieee.org/stamp/stamp.jsp?tp=&arnumber=10063206)] [[code](https://github.com/WW-Tong/ssagcn_for_path_prediction)]
* Disentangling Crowd Interactions for Pedestrians Trajectory Prediction, RAL. [[paper](https://ieeexplore.ieee.org/stamp/stamp.jsp?tp=&arnumber=10083225)]
* VNAGT: Variational Non-Autoregressive Graph Transformer Network for Multi-Agent Trajectory Prediction, IEEE Transactions on Vehicular Technology. [[paper](https://ieeexplore.ieee.org/stamp/stamp.jsp?tp=&arnumber=10121688)]
* Spatial-Temporal-Spectral LSTM: A Transferable Model for Pedestrian Trajectory Prediction, TIV. [[paper](https://ieeexplore.ieee.org/stamp/stamp.jsp?tp=&arnumber=10149368)]
* Holistic Transformer: A Joint Neural Network for Trajectory Prediction and Decision-Making of Autonomous Vehicles, PR. [[paper](https://www.sciencedirect.com/science/article/pii/S0031320323002935)]
* Tri-HGNN: Learning triple policies fused hierarchical graph neural networks for pedestrian trajectory prediction, PR. [[paper](https://www.sciencedirect.com/science/article/pii/S0031320323004703)]
* Multimodal Vehicular Trajectory Prediction With Inverse Reinforcement Learning and Risk Aversion at Urban Unsignalized Intersections, TITS. [[paper](https://ieeexplore.ieee.org/stamp/stamp.jsp?tp=&arnumber=10164651)]
* Trajectory prediction for autonomous driving based on multiscale spatial‐temporal graph, IET Intelligent Transport Systems. [[paper](https://ietresearch.onlinelibrary.wiley.com/doi/pdfdirect/10.1049/itr2.12265)]
* Social Self-Attention Generative Adversarial Networks for Human Trajectory Prediction, IEEE Transactions on Artificial Intelligence. [[paper](https://ieeexplore.ieee.org/stamp/stamp.jsp?tp=&arnumber=10197467)]
* CSIR: Cascaded Sliding CVAEs With Iterative Socially-Aware Rethinking for Trajectory Prediction, TITS. [[paper](https://ieeexplore.ieee.org/stamp/stamp.jsp?tp=&arnumber=10215313)]
* Multimodal Manoeuvre and Trajectory Prediction for Automated Driving on Highways Using Transformer Networks, RAL. [[paper](https://ieeexplore.ieee.org/stamp/stamp.jsp?tp=&arnumber=10207845)]
* A physics-informed Transformer model for vehicle trajectory prediction on highways, Transportation Research Part C: Emerging Technologies. [[paper](https://www.sciencedirect.com/science/article/pii/S0968090X23002619)] [[code](https://github.com/Gengmaosi/PIT-IDM)]
* MacFormer: Map-Agent Coupled Transformer for Real-time and Robust Trajectory Prediction, RAL. [[paper](https://arxiv.org/pdf/2308.10280.pdf)]
* MRGTraj: A Novel Non-Autoregressive Approach for Human Trajectory Prediction, TCSVT. [[paper](https://ieeexplore.ieee.org/stamp/stamp.jsp?tp=&arnumber=10226250)] [[code](https://github.com/wisionpeng/MRGTraj)]
* Planning-inspired Hierarchical Trajectory Prediction via Lateral-Longitudinal Decomposition for Autonomous Driving, TIV. [[paper](https://ieeexplore.ieee.org/stamp/stamp.jsp?tp=&arnumber=10226224)]
* A multi-modal vehicle trajectory prediction framework via conditional diffusion model: A coarse-to-fine approach, KBS. [[paper](https://www.sciencedirect.com/science/article/pii/S0950705123007402)]
* Modality Exploration, Retrieval and Adaptation for Trajectory Prediction, TPAMI. [[paper](https://ieeexplore.ieee.org/stamp/stamp.jsp?tp=&arnumber=10254381)]
* MFAN: Mixing Feature Attention Network for Trajectory Prediction, PR. [[paper](https://www.sciencedirect.com/science/article/pii/S0031320323006957#abs0001)]
* IE-GAN: a data-driven crowd simulation method via generative adversarial networks, Multimedia Tools and Applications. [[paper](https://link.springer.com/article/10.1007/s11042-023-17346-x)]
* Trajectory Distribution Aware Graph Convolutional Network for Trajectory Prediction Considering Spatio-temporal Interactions and Scene Information, TKDE. [[paper](https://ieeexplore.ieee.org/stamp/stamp.jsp?tp=&arnumber=10309163)]
* Map-free Trajectory Prediction in Traffic with Multi-level Spatial-temporal Modeling, TIV. [[paper](https://ieeexplore.ieee.org/stamp/stamp.jsp?tp=&arnumber=10356823)]
* STIGCN: Spatial-Temporal Interaction-aware Graph Convolution Network for Pedestrian Trajectory Prediction, The Journal of Supercomputing. [[paper](https://link.springer.com/article/10.1007/s11227-023-05850-8)] [[code](https://github.com/Chenwangxing/STIGCN_master)]
* Stochastic Non-Autoregressive Transformer-Based Multi-Modal Pedestrian Trajectory Prediction for Intelligent Vehicles, TITS. [[paper](https://ieeexplore.ieee.org/stamp/stamp.jsp?tp=&arnumber=10367756)] [[code](https://github.com/xbchen82/SNARTF)]
* Trajectory Prediction for Autonomous Driving Based on Structural Informer Method, IEEE Transactions on Automation Science and Engineering. [[paper](https://ieeexplore.ieee.org/stamp/stamp.jsp?tp=&arnumber=10364872)]
* MTP-GO: Graph-Based Probabilistic Multi-Agent Trajectory Prediction with Neural ODEs, IEEE Transactions on Intelligent Vehicles. [[paper](https://arxiv.org/abs/2302.00735)] [[code](https://github.com/westny/mtp-go)]
## Others 2023
* Traj-MAE: Masked Autoencoders for Trajectory Prediction, arXiv preprint arXiv:2303.06697, 2023. [[paper](https://arxiv.org/pdf/2303.06697.pdf)]
* Uncertainty-Aware Pedestrian Trajectory Prediction via Distributional Diffusion, arXiv preprint arXiv:2303.08367, 2023. [[paper](https://arxiv.org/pdf/2303.08367.pdf)]
* DiffTraj: Generating GPS Trajectory with Diffusion Probabilistic Model, arXiv preprint arXiv:2304.11582, 2023. [[paper](https://arxiv.org/pdf/2304.11582.pdf)] [[code](https://github.com/Yasoz/DiffTraj)]
* Multiverse Transformer: 1st Place Solution for Waymo Open Sim Agents Challenge 2023, CVPR 2023 Workshop on Autonomous Driving. [[paper](https://arxiv.org/pdf/2306.11868.pdf)] [[website](https://multiverse-transformer.github.io/sim-agents/)]
* Joint-Multipath++ for Simulation Agents: 2nd Place Solution for Waymo Open Sim Agents Challenge 2023, CVPR 2023 Workshop on Autonomous Driving. [[paper](https://storage.googleapis.com/waymo-uploads/files/research/2023%20Technical%20Reports/SA_hm_jointMP.pdf)] [[code](https://github.com/wangwenxi-handsome/Joint-Multipathpp)]
* MTR++: Multi-Agent Motion Prediction with Symmetric Scene Modeling and Guided Intention Querying, 1st Place Solution for Waymo Open Motion Prediction Challenge 2023, CVPR 2023 Workshop on Autonomous Driving. [[paper](https://arxiv.org/pdf/2306.17770.pdf)] [[code](https://github.com/sshaoshuai/MTR)]
* GameFormer: Game-theoretic Modeling and Learning of Transformer-based Interactive Prediction and Planning for Autonomous Driving, arXiv preprint arXiv:2303.05760, 2023. [[paper](https://arxiv.org/pdf/2303.05760.pdf)] [[code](https://github.com/MCZhi/GameFormer)] [[website](https://mczhi.github.io/GameFormer/)]
* GameFormer Planner: A Learning-enabled Interactive Prediction and Planning Framework for Autonomous Vehicles, the nuPlan Planning Challenge at the CVPR 2023 End-to-End Autonomous Driving Workshop. [[paper](https://opendrivelab.com/e2ead/AD23Challenge/Track_4_AID.pdf)] [[code](https://github.com/MCZhi/GameFormer-Planner/)]
* trajdata: A Unified Interface to Multiple Human Trajectory Datasets, arXiv preprint arXiv:2307.13924, 2023. [[paper](https://arxiv.org/pdf/2307.13924.pdf)] [[code](https://github.com/NVlabs/trajdata)]
* Graph-Based Interaction-Aware Multimodal 2D Vehicle Trajectory Prediction using Diffusion Graph Convolutional Networks, arXiv preprint arXiv:2309.01981, 2023. [[paper](https://arxiv.org/pdf/2309.01981.pdf)]
* EquiDiff: A Conditional Equivariant Diffusion Model For Trajectory Prediction, arXiv preprint arXiv:2308.06564, 2023. [[paper](https://arxiv.org/pdf/2308.06564.pdf)]
* DICE: Diverse Diffusion Model with Scoring for Trajectory Prediction, arXiv preprint arXiv:2310.14570, 2023. [[paper](https://arxiv.org/pdf/2310.14570.pdf)]
* Pedestrian Trajectory Prediction Using Dynamics-based Deep Learning, arXiv preprint arXiv:2309.09021, 2023. [[paper](https://browse.arxiv.org/pdf/2309.09021.pdf)] [[code](https://github.com/sydney-machine-learning/pedestrianpathprediction)]
* VT-Former: A Transformer-based Vehicle Trajectory Prediction Approach For Intelligent Highway Transportation Systems, arXiv preprint arXiv:2311.06623, 2023. [[paper](https://arxiv.org/pdf/2311.06623.pdf)]
* Learning Cooperative Trajectory Representations for Motion Forecasting, arXiv preprint arXiv:2311.00371, 2023. [[paper](https://arxiv.org/pdf/2311.00371.pdf)] [[code](https://github.com/AIR-THU/V2X-Graph)]
* Social-Transmotion: Promptable Human Trajectory Prediction, arXiv preprint arXiv:2312.16168, 2023. [[paper](https://arxiv.org/pdf/2312.16168.pdf)] [[code](https://github.com/vita-epfl/social-transmotion)]
* RealGen: Retrieval Augmented Generation for Controllable Traffic Scenarios, arXiv preprint arXiv:2312.13303, 2023. [[paper](https://arxiv.org/pdf/2312.13303.pdf)] [[code](https://realgen.github.io/)]
* SceneDM: Scene-level Multi-agent Trajectory Generation with Consistent Diffusion Models, arXiv preprint arXiv:2311.15736, 2023. [[paper](https://arxiv.org/pdf/2311.15736.pdf)] [[website](https://alperen-hub.github.io/SceneDM/)]
* DriveDreamer: Towards Real-world-driven World Models for Autonomous Driving, arXiv preprint arXiv:2309.09777, 2023. [[paper](https://arxiv.org/pdf/2309.09777.pdf)] [[website](https://drivedreamer.github.io/)]
* Language Prompt for Autonomous Driving, arXiv preprint arXiv:2309.04379, 2023. [[paper](https://arxiv.org/pdf/2309.04379.pdf)] [[code](https://github.com/wudongming97/Prompt4Driving)]
* GAIA-1: A Generative World Model for Autonomous Driving, arXiv preprint arXiv:2309.17080, 2023. [[paper](https://browse.arxiv.org/pdf/2309.17080.pdf)] [[website](https://wayve.ai/thinking/scaling-gaia-1/)]
* LanguageMPC: Large Language Models as Decision Makers for Autonomous Driving, arXiv preprint arXiv:2310.03026, 2023. [[paper](https://arxiv.org/pdf/2310.03026.pdf)] [[website](https://sites.google.com/view/llm-mpc)]
* DriveGPT4: Interpretable End-to-end Autonomous Driving via Large Language Model, arXiv preprint arXiv:2310.01412, 2023. [[paper](https://browse.arxiv.org/pdf/2310.01412.pdf)] [[website](https://tonyxuqaq.github.io/projects/DriveGPT4/)]
* Drive Like a Human: Rethinking Autonomous Driving with Large Language Models, arXiv preprint arXiv:2307.07162, 2023. [[paper](https://arxiv.org/pdf/2307.07162.pdf)] [[code](https://github.com/PJLab-ADG/DriveLikeAHuman)]
* DiLu: A Knowledge-Driven Approach to Autonomous Driving with Large Language Models, arXiv preprint arXiv:2309.16292, 2023. [[paper](https://browse.arxiv.org/pdf/2309.16292.pdf)] [[website](https://pjlab-adg.github.io/DiLu/)]
* DrivingDiffusion: Layout-Guided multi-view driving scene video generation with latent diffusion model, arXiv preprint arXiv:2310.07771, 2023. [[paper](https://arxiv.org/pdf/2310.07771.pdf)] [[website](https://drivingdiffusion.github.io/)]
* Driving with LLMs: Fusing Object-Level Vector Modality for Explainable Autonomous Driving, arXiv preprint arXiv:2310.01957, 2023. [[paper](https://browse.arxiv.org/pdf/2310.01957.pdf)] [[code](https://github.com/wayveai/Driving-with-LLMs)]
* WEDGE: A Multi-Weather Autonomous Driving Dataset Built From Generative Vision-Language Models, CVPR Workshops 2023. [[paper](https://arxiv.org/pdf/2305.07528.pdf)] [[website](https://infernolia.github.io/WEDGE)]
* BEVGPT: Generative Pre-trained Large Model for Autonomous Driving Prediction, Decision-Making, and Planning, arXiv preprint arXiv:2310.10357, 2023. [[paper](https://arxiv.org/pdf/2310.10357.pdf)]
* Diffusion World Models, ICLR 2024 Conference Submission, 2023. [[paper](https://openreview.net/pdf?id=bAXmvOLtjA)]
* Waymax: An Accelerated, Data-Driven Simulator for Large-Scale Autonomous Driving Research, arXiv preprint arXiv:2310.08710, 2023. [[paper](https://arxiv.org/pdf/2310.08710.pdf)] [[code](https://github.com/waymo-research/waymax)] [[website](https://waymo.com/intl/zh-cn/research/waymax/)]
* MagicDrive: Street View Generation with Diverse 3D Geometry Control, arXiv preprint arXiv:2310.02601, 2023. [[paper](https://arxiv.org/pdf/2310.02601.pdf)] [[website](https://gaoruiyuan.com/magicdrive/)]
* GPT-Driver: Learning to Drive with GPT, arXiv preprint arXiv:2310.01415, 2023. [[paper](https://arxiv.org/pdf/2310.01415.pdf)] [[code](https://github.com/PointsCoder/GPT-Driver)]
* Can you text what is happening? Integrating pre-trained language encoders into trajectory prediction models for autonomous driving, arXiv preprint arXiv:2309.05282, 2023. [[paper](https://arxiv.org/pdf/2309.05282.pdf)]
* HiLM-D: Towards High-Resolution Understanding in Multimodal Large Language Models for Autonomous Driving, arXiv preprint arXiv:2309.05186, 2023. [[paper](https://arxiv.org/pdf/2309.05186.pdf)]
* A Language Agent for Autonomous Driving, arXiv preprint arXiv:2311.10813, 2023. [[paper](https://arxiv.org/pdf/2311.10813.pdf)] [[website](https://usc-gvl.github.io/Agent-Driver/)]
* ADriver-I: A General World Model for Autonomous Driving, arXiv preprint arXiv:2311.13549, 2023. [[paper](https://arxiv.org/pdf/2311.13549.pdf)]
* LLM4Drive: A Survey of Large Language Models for Autonomous Driving, arXiv preprint arXiv:2311.01043, 2023. [[paper](https://arxiv.org/pdf/2311.01043.pdf)] [[code](https://github.com/Thinklab-SJTU/Awesome-LLM4AD)]
* Vision Language Models in Autonomous Driving and Intelligent Transportation Systems, arXiv preprint arXiv:2310.14414, 2023. [[paper](https://arxiv.org/pdf/2310.14414.pdf)]
* On the Road with GPT-4V(ision): Early Explorations of Visual-Language Model on Autonomous Driving, arXiv preprint arXiv:2311.05332, 2023. [[paper](https://arxiv.org/pdf/2311.05332.pdf)] [[code](https://github.com/PJLab-ADG/GPT4V-AD-Exploration)]
* Driving into the Future: Multiview Visual Forecasting and Planning with World Model for Autonomous Driving, arXiv preprint arXiv:2311.17918, 2023. [[paper](https://arxiv.org/pdf/2311.17918.pdf)] [[code](https://github.com/BraveGroup/Drive-WM)] [[website](https://drive-wm.github.io/)]
* A Survey on Multimodal Large Language Models for Autonomous Driving, arXiv preprint arXiv:2311.12320, 2023. [[paper](https://arxiv.org/pdf/2311.12320.pdf)] [[code](https://github.com/IrohXu/Awesome-Multimodal-LLM-Autonomous-Driving)]
* Panacea: Panoramic and Controllable Video Generation for Autonomous Driving, arXiv preprint arXiv:2311.16813, 2023. [[paper](https://arxiv.org/pdf/2311.16813.pdf)] [[website](https://panacea-ad.github.io/)] [[code](https://github.com/wenyuqing/panacea)]
* LMDrive: Closed-Loop End-to-End Driving with Large Language Models, arXiv preprint arXiv:2312.07488, 2023. [[paper](https://arxiv.org/pdf/2312.07488.pdf)] [[code](https://github.com/opendilab/LMDrive)]
* DriveMLM: Aligning Multi-Modal Large Language Models with Behavioral Planning States for Autonomous Driving, arXiv preprint arXiv:2312.09245, 2023. [[paper](https://arxiv.org/pdf/2312.09245.pdf)] [[code](https://github.com/OpenGVLab/DriveMLM)]
* Language Models, Agent Models, and World Models: The LAW for Machine Reasoning and Planning, arXiv preprint arXiv:2312.05230, NIPS Tutorial 2023. [[paper](https://arxiv.org/pdf/2312.05230.pdf)] [[website](https://sites.google.com/view/neurips2023law)]
* Dolphins: Multimodal Language Model for Driving, arXiv preprint arXiv:2312.00438, 2023. [[paper](https://arxiv.org/pdf/2312.00438.pdf)] [[website](https://vlm-driver.github.io/)]
* DriveLM: Driving with Graph Visual Question Answering, arXiv preprint arXiv:2312.14150, 2023. [[paper](https://arxiv.org/pdf/2312.14150.pdf)] [[code](https://github.com/OpenDriveLab/DriveLM)] [[website](https://opendrivelab.github.io/DriveLM)]
* LingoQA: Video Question Answering for Autonomous Driving, arXiv preprint arXiv:2312.14115, 2023. [[paper](https://arxiv.org/pdf/2312.14115.pdf)] [[code](https://github.com/wayveai/LingoQA)]
* ViFiT: Reconstructing Vision Trajectories from IMU and Wi-Fi Fine Time Measurements, MobiCom ISACom Workshop 2023. [[paper](https://dl.acm.org/doi/10.1145/3615984.3616503)] [[code](https://github.com/bryanbocao/vifit)]
# 📚 2024 Conference and Journal Papers
## Conference Papers 2024
* BAT: Behavior-Aware Human-Like Trajectory Prediction for Autonomous Driving, AAAI 2024. [[paper](https://arxiv.org/pdf/2312.06371.pdf)] [[code](https://github.com/Petrichor625/BATraj-Behavior-aware-Model)]
* NuScenes-QA: A Multi-modal Visual Question Answering Benchmark for Autonomous Driving Scenario, AAAI 2024. [[paper](https://arxiv.org/pdf/2305.14836.pdf)] [[code](https://github.com/qiantianwen/NuScenes-QA)]
* SocialCVAE: Predicting Pedestrian Trajectory via Interaction Conditioned Latents, AAAI 2024. [[paper](http://www.cad.zju.edu.cn/home/jin/AAAI20242/SocialCVAE.pdf)] [[code](http://www.cad.zju.edu.cn/home/jin/AAAI20242/SocialCVAE.htm)]
* Human Observation-Inspired Trajectory Prediction for Autonomous Driving in Mixed-Autonomy Traffic Environments, ICRA 2024. [[paper](https://arxiv.org/abs/2402.04318)]
* Can Language Beat Numerical Regression? Language-Based Multimodal Trajectory Prediction, CVPR 2024. [[paper](https://arxiv.org/abs/2403.18447)] [[code](https://github.com/InhwanBae/LMTrajectory)]
* SingularTrajectory: Universal Trajectory Predictor using Diffusion Model, CVPR 2024. [[paper](https://arxiv.org/abs/2403.18452)] [[code](https://github.com/InhwanBae/SingularTrajectory)]
* Producing and Leveraging Online Map Uncertainty in Trajectory Prediction, CVPR 2024. [[paper](https://arxiv.org/pdf/2403.16439.pdf)] [[code](https://github.com/alfredgu001324/MapUncertaintyPrediction)]
* HPNet: Dynamic Trajectory Forecasting with Historical Prediction Attention, CVPR 2024. [[paper](https://arxiv.org/pdf/2404.06351.pdf)] [[code](https://github.com/XiaolongTang23/HPNet)]
* Adapting to Length Shift: FlexiLength Network for Trajectory Prediction, CVPR 2024. [[paper](https://arxiv.org/pdf/2404.00742.pdf)]
* T4P: Test-Time Training of Trajectory Prediction via Masked Autoencoder and Actor-specific Token Memory, CVPR 2024. [[paper](https://arxiv.org/pdf/2403.10052.pdf)] [[code](https://github.com/daeheepark/T4P)]
* SocialCircle: Learning the Angle-based Social Interaction Representation for Pedestrian Trajectory Prediction, CVPR 2024. [[paper](https://arxiv.org/pdf/2310.05370.pdf)] [[code](https://github.com/cocoon2wong/SocialCircle)]
* Adversarial Backdoor Attack by Naturalistic Data Poisoning on Trajectory Prediction in Autonomous Driving, CVPR 2024. [[paper](https://arxiv.org/pdf/2306.15755.pdf)]
* CaDeT: a Causal Disentanglement Approach for Robust Trajectory Prediction in Autonomous Driving, CVPR 2024.
* Higher-order Relational Reasoning for Pedestrian Trajectory Prediction, CVPR 2024.
* Density-Adaptive Model Based on Motif Matrix for Multi-Agent Trajectory Prediction, CVPR 2024.
* OOSTraj: Out-of-Sight Trajectory Prediction With Vision-Positioning Denoising, CVPR 2024. [[paper](https://arxiv.org/pdf/2404.02227.pdf)] [[code](https://github.com/Hai-chao-Zhang/OOSTraj)]
* SmartRefine: A Scenario-Adaptive Refinement Framework for Efficient Motion Prediction, CVPR 2024. [[paper](https://arxiv.org/pdf/2403.11492)] [[code](https://github.com/opendilab/SmartRefine)]
* MFTraj: Map-Free, Behavior-Driven Trajectory Prediction for Autonomous Driving, IJCAI 2024. [[paper](https://arxiv.org/pdf/2405.01266)]
* Characterized Diffusion and Spatial-Temporal Interaction Network for Trajectory Prediction in Autonomous Driving, IJCAI 2024. [[paper](https://arxiv.org/pdf/2405.02145)]
* A Cognitive-Driven Trajectory Prediction Model for Autonomous Driving in Mixed Autonomy Environment, IJCAI 2024. [[paper](https://arxiv.org/pdf/2404.17520)]
* Physics-Informed Trajectory Prediction for Autonomous Driving under Missing Observation, IJCAI 2024. [[paper](https://papers.ssrn.com/sol3/papers.cfm?abstract_id=4809575 )]
* Exploring Large Language Models for Trajectory Prediction: A Technical Perspective, ACM/IEEE International Conference on Human-Robot Interaction (HRI 2024). [[paper](https://dl.acm.org/doi/pdf/10.1145/3610978.3640625)]
* SpectrumNet: Spectrum-Based Trajectory Encode Neural Network for Pedestrian Trajectory Prediction, IEEE International Conference on Acoustics, Speech and Signal Processing (ICASSP 2024). [[paper](https://ieeexplore.ieee.org/abstract/document/10446706)]
* MapFlow: Multi-Agent Pedestrian Trajectory Prediction Using Normalizing Flow, ICASSP 2024. [[paper](https://ieeexplore.ieee.org/abstract/document/10448062)]
* Promptable Closed-loop Traffic Simulation, CoRL 2024. [[paper](https://arxiv.org/pdf/2409.05863)] [[code](https://ariostgx.github.io/ProSim/)]
## Journal Papers 2024
* SMEMO: Social Memory for Trajectory Forecasting, TPAMI. [[paper](https://arxiv.org/pdf/2203.12446.pdf)]
* A Cognitive-Based Trajectory Prediction Approach for Autonomous Driving, TIV. [[paper](https://arxiv.org/abs/2402.19251)]
* EMSIN: Enhanced Multi-Stream Interaction Network for Vehicle Trajectory Prediction, IEEE Transactions on Fuzzy Systems. [[paper](https://ieeexplore.ieee.org/stamp/stamp.jsp?tp=&arnumber=10418557)]
* Social Force Embedded Mixed Graph Convolutional Network for Multi-class Trajectory Prediction, TIV. [[paper](https://ieeexplore.ieee.org/stamp/stamp.jsp?tp=&arnumber=10415371)]
* Context-Aware Timewise VAEs for Real-Time Vehicle Trajectory Prediction, RAL. [[paper](https://arxiv.org/pdf/2302.10873.pdf)] [[code](https://github.com/xupei0610/ContextVAE)]
* Learning Autoencoder Diffusion Models of Pedestrian Group Relationships for Multimodal Trajectory Prediction, IEEE Transactions on Instrumentation and Measurement (TIM). [[paper](https://ieeexplore.ieee.org/abstract/document/10466609)]
* DSTCNN: Deformable Spatial-Temporal Convolutional Neural Network for Pedestrian Trajectory Prediction, Information Sciences. [[paper](https://www.sciencedirect.com/science/article/pii/S0020025524003682)]
* Heterogeneous graph social pooling for interaction-aware vehicle trajectory prediction, Transportation Research Part E: Logistics and Transportation Review. [[paper](https://www.sciencedirect.com/science/article/abs/pii/S1366554524003399)]
## Others 2024
* Controllable Safety-Critical Closed-loop Traffic Simulation via Guided Diffusion, arXiv preprint arXiv:2401.00391, 2024. [[paper](https://arxiv.org/pdf/2401.00391.pdf)] [[website](https://safe-sim.github.io/)]
* Forging Vision Foundation Models for Autonomous Driving: Challenges, Methodologies, and Opportunities, arXiv preprint arXiv:2401.08045, 2024. [[paper](https://arxiv.org/pdf/2401.08045.pdf)] [[code](https://github.com/zhanghm1995/Forge_VFM4AD)]
* Intention-aware Denoising Diffusion Model for Trajectory Prediction, arXiv preprint arXiv:2403.09190, 2024. [[paper](https://arxiv.org/pdf/2403.09190.pdf)]
* LG-Traj: LLM Guided Pedestrian Trajectory Prediction, arXiv preprint arXiv:2403.08032, 2024. [[paper](https://arxiv.org/pdf/2403.08032.pdf)]
* Traj-LLM: A New Exploration for Empowering Trajectory Prediction with Pre-trained Large Language Models, arXiv preprint arXiv:2405.04909, 2024. [[paper](https://arxiv.org/pdf/2405.04909)]
* UniTraj: A Unified Framework for Scalable Vehicle Trajectory Prediction, arXiv preprint arXiv:2403.15098, 2024. [[paper](https://arxiv.org/pdf/2403.15098.pdf)] [[code](https://github.com/vita-epfl/UniTraj)]
* Versatile Scene-Consistent Traffic Scenario Generation as Optimization with Diffusion, arXiv preprint arXiv:2404.02524, 2024. [[paper](https://arxiv.org/pdf/2404.02524.pdf)] [[code](https://sites.google.com/view/versatile-behavior-diffusion)]
* ControlTraj: Controllable Trajectory Generation with Topology-Constrained Diffusion Model, arXiv preprint arXiv:2404.15380, 2024. [[paper](https://arxiv.org/pdf/2404.15380)]
* Diffusion-Based Environment-Aware Trajectory Prediction, arXiv preprint arXiv:2403.11643, 2024. [[paper](https://arxiv.org/abs/2403.11643)]
* A Preprocessing and Evaluation Toolbox for Trajectory Prediction Research on the Drone Datasets, arXiv preprint arXiv:2405.00604, 2024. [[paper](https://arxiv.org/abs/2405.00604)] [[code](https://github.com/westny/dronalize)]
* BehaviorGPT: Smart Agent Simulation for Autonomous Driving with Next-Patch Prediction, arXiv preprint arXiv:2405.17372, 2024. [[paper](https://arxiv.org/pdf/2405.17372)]
* Vista: A Generalizable Driving World Model with High Fidelity and Versatile Controllability, arXiv preprint arXiv:2405.17398, 2024. [[paper](https://arxiv.org/pdf/2405.17398)] [[code](https://github.com/OpenDriveLab/Vista)]
* UrbanGPT: Spatio-Temporal Large Language Models, arXiv preprint arXiv:2403.00813, 2024. [[paper](https://arxiv.org/pdf/2403.00813)] [[code](https://github.com/HKUDS/UrbanGPT)]
* Continuously Learning, Adapting, and, Improving: A Dual-Process Approach to Autonomous Driving, arXiv preprint arXiv:2405.15324, 2024. [[paper](https://arxiv.org/pdf/2405.15324)] [[code](https://github.com/PJLab-ADG/LeapAD)]
* DriveVLM: The Convergence of Autonomous Driving and Large Vision-Language Models, arXiv preprint arXiv:2402.12289, 2024. [[paper](https://arxiv.org/pdf/2402.12289)] [[website](https://tsinghua-mars-lab.github.io/DriveVLM/)]
* NAVSIM: Data-Driven Non-Reactive Autonomous Vehicle Simulation and Benchmarking, arXiv preprint arXiv:2406.15349, 2024. [[paper](https://arxiv.org/pdf/2406.15349)] [[code](https://github.com/autonomousvision/navsim)]
* SimGen: Simulator-conditioned Driving Scene Generation, arXiv preprint arXiv:2406.09386, 2024. [[paper](https://arxiv.org/pdf/2406.09386)] [[code](https://metadriverse.github.io/simgen/)]
* GenAD: Generative End-to-End Autonomous Driving, arXiv preprint arXiv:2402.11502, 2024. [[paper](https://arxiv.org/pdf/2402.11502)] [[code](https://github.com/wzzheng/GenAD)]
* LCSim: A Large-Scale Controllable Traffic Simulator, arXiv preprint arXiv:2406.19781, 2024. [[paper](https://arxiv.org/pdf/2406.19781)] [[code](https://github.com/tsinghua-fib-lab/LCSim)]
# 📚 Related Review Papers
* Summary and Reflections on Pedestrian Trajectory Prediction in the Field of Autonomous Driving, TIV 2024. [[paper](https://ieeexplore.ieee.org/stamp/stamp.jsp?tp=&arnumber=10528911)]
* A Review of Trajectory Prediction Methods for the Vulnerable Road User, Robotics 2023. [[paper](https://www.mdpi.com/2218-6581/13/1/1)]
* A Survey of Generative AI for Intelligent Transportation Systems, arXiv preprint arXiv:2312.08248, 2023. [[paper](https://arxiv.org/pdf/2312.08248.pdf)]
* Pedestrian and vehicle behaviour prediction in autonomous vehicle system — A review, Expert Systems With Applications 2023. [[paper](https://www.sciencedirect.com/science/article/pii/S0957417423024855)]
* Data-driven Traffic Simulation: A Comprehensive Review, arXiv preprint arXiv:2310.15975, 2023. [[paper](https://arxiv.org/ftp/arxiv/papers/2310/2310.15975.pdf)]
* Pedestrian Trajectory Prediction in Pedestrian-Vehicle Mixed Environments: A Systematic Review, TITS 2023. [[paper](https://ieeexplore.ieee.org/stamp/stamp.jsp?tp=&arnumber=10181234)]
* Machine Learning for Autonomous Vehicle’s Trajectory Prediction: A comprehensive survey, Challenges, and Future Research Directions, arXiv preprint arXiv:2307.07527, 2023. [[paper](https://arxiv.org/pdf/2307.07527.pdf)]
* Incorporating Driving Knowledge in Deep Learning Based Vehicle Trajectory Prediction: A Survey, IEEE Transactions on Intelligent Vehicles 2023. [[paper](https://ieeexplore.ieee.org/stamp/stamp.jsp?tp=&arnumber=10100881)]
* A Survey of Vehicle Trajectory Prediction Based on Deep Learning Models, International Conference on Sustainable Expert Systems: ICSES 2022. [[paper](https://link.springer.com/chapter/10.1007/978-981-19-7874-6_48)]
* A Survey on Trajectory-Prediction Methods for Autonomous Driving, IEEE Transactions on Intelligent Vehicles 2022. [[paper](https://ieeexplore.ieee.org/stamp/stamp.jsp?tp=&arnumber=9756903)]
* Generative Adversarial Networks for Spatio-temporal Data: A Survey, ACM Transactions on Intelligent Systems and Technology 2022. [[paper](https://dl.acm.org/doi/pdf/10.1145/3474838)]
* Scenario Understanding and Motion Prediction for Autonomous Vehicles – Review and Comparison, TITS 2022. [[paper](https://ieeexplore.ieee.org/stamp/stamp.jsp?tp=&arnumber=9733973)]
* Deep Reinforcement Learning for Autonomous Driving: A Survey, TITS 2022. [[paper](https://ieeexplore.ieee.org/stamp/stamp.jsp?tp=&arnumber=9351818)]
* Social Interactions for Autonomous Driving: A Review and Perspective, arXiv preprint arXiv:2208.07541, 2022. [[paper](https://arxiv.org/pdf/2208.07541.pdf)]
* Behavioral Intention Prediction in Driving Scenes: A Survey, arXiv preprint arXiv:2211.00385, 2022. [[paper](https://arxiv.org/pdf/2211.00385.pdf)]
* Multi-modal Fusion Technology based on Vehicle Information: A Survey, arXiv preprint arXiv:2211.06080, 2022. [[paper](https://arxiv.org/pdf/2211.06080.pdf)]
* Pedestrian Behavior Prediction for Automated Driving: Requirements, Metrics, and Relevant Features, TITS 2021. [[paper](https://ieeexplore.ieee.org/stamp/stamp.jsp?tp=&arnumber=9660784)]
* A Review of Deep Learning-Based Methods for Pedestrian Trajectory Prediction, Sensors 2021. [[paper](https://www.mdpi.com/1424-8220/21/22/7543/pdf)]
* A Survey on Deep-Learning Approaches for Vehicle Trajectory Prediction in Autonomous Driving, IEEE International Conference on Robotics and Biomimetics (ROBIO 2021). [[paper](https://arxiv.org/pdf/2110.10436.pdf)] [[code](https://github.com/Henry1iu/TNT-Trajectory-Predition)]
* Review of Pedestrian Trajectory Prediction Methods: Comparing Deep Learning and Knowledge-based Approaches, arXiv preprint arXiv:2111.06740, 2021. [[paper](https://arxiv.org/pdf/2111.06740.pdf)]
* A Survey on Trajectory Data Management, Analytics, and Learning, ACM Computing Surveys (CSUR 2021). [[paper](https://dl.acm.org/doi/pdf/10.1145/3440207)]
* A Survey on Motion Prediction of Pedestrians and Vehicles for Autonomous Driving, IEEE Access 2021. [[paper](https://ieeexplore.ieee.org/stamp/stamp.jsp?tp=&arnumber=9559998)]
* Autonomous Driving with Deep Learning: A Survey of State-of-Art Technologies, arXiv preprint arXiv:2006.06091, 2020. [[paper](https://arxiv.org/ftp/arxiv/papers/2006/2006.06091.pdf)]
* A Survey on Visual Traffic Simulation: Models, Evaluations, and Applications in Autonomous Driving, Computer Graphics Forum 2020. [[paper](https://onlinelibrary.wiley.com/doi/epdf/10.1111/cgf.13803?saml_referrer)]
* A Survey of Deep Learning Techniques for Autonomous Driving, Journal of Field Robotics 2020. [[paper](https://onlinelibrary.wiley.com/doi/epdf/10.1002/rob.21918?saml_referrer)]
* Human Motion Trajectory Prediction: A Survey, International Journal of Robotics Research 2020. [[paper](http://sage.cnpereading.com/paragraph/download/?doi=10.1177/0278364920917446)]
* Vehicle Trajectory Similarity: Models, Methods, and Applications, ACM Computing Surveys (CSUR 2020). [[paper](https://dl.acm.org/doi/pdf/10.1145/3406096)]
* Deep Learning-Based Vehicle Behavior Prediction for Autonomous Driving Applications: A Review, TITS 2020. [[paper](https://ieeexplore.ieee.org/stamp/stamp.jsp?tp=&arnumber=9158529)]
* Survey of Deep Reinforcement Learning for Motion Planning of Autonomous Vehicles, TITS 2020. [[paper](https://ieeexplore.ieee.org/stamp/stamp.jsp?tp=&arnumber=9210154)]
* Overview of Tools Supporting Planning for Automated Driving, ITSC 2020. [[paper](https://ieeexplore.ieee.org/stamp/stamp.jsp?tp=&arnumber=9294512)]
* Autonomous Vehicles that Interact with Pedestrians: A Survey of Theory and Practice, TITS 2019. [[paper](https://ieeexplore.ieee.org/stamp/stamp.jsp?tp=&arnumber=8667866)]
* A Survey on Path Prediction Techniques for Vulnerable Road Users: From Traditional to Deep-Learning Approaches, ITSC 2019. [[paper](https://ieeexplore.ieee.org/stamp/stamp.jsp?tp=&arnumber=8917053)]
* Spatio-Temporal Data Mining: A Survey of Problems and Methods, ACM Computing Surveys 2018. [[paper](https://dl.acm.org/doi/pdf/10.1145/3161602)]
* Survey on Vision-Based Path Prediction, International Conference on Distributed, Ambient, and Pervasive Interactions (DAPI 2018). [[paper](https://link.springer.com/content/pdf/10.1007/978-3-319-91131-1_4.pdf)]
* Moving Objects Analytics: Survey on Future Location & Trajectory Prediction Methods, arXiv preprint arXiv:1807.04639, 2018. [[paper](https://arxiv.org/ftp/arxiv/papers/1807/1807.04639.pdf)]
* A Survey on Trajectory Data Mining: Techniques and Applications, IEEE Access 2016. [[paper](https://ieeexplore.ieee.org/stamp/stamp.jsp?arnumber=7452339)]
* Trajectory Data Mining: An Overview, ACM Transactions on Intelligent Systems and Technology 2015. [[paper](http://urban-computing.com/pdf/TrajectoryDataMining-tist-yuzheng.pdf)]
* A survey on motion prediction and risk assessment for intelligent vehicles, ROBOMECH Journal 2014. [[paper](https://robomechjournal.springeropen.com/track/pdf/10.1186/s40648-014-0001-z.pdf)]
# 📚 Datasets
## Reviews about Datasets
* A Survey on Autonomous Driving Datasets: Data Statistic, Annotation, and Outlook, arXiv preprint arXiv:2401.01454, 2024. [[paper](https://arxiv.org/pdf/2401.01454.pdf)]
* Open-sourced Data Ecosystem in Autonomous Driving: the Present and Future, arXiv preprint arXiv:2312.03408, 2023. [[paper](https://arxiv.org/pdf/2312.03408.pdf)] [[Chinese](https://opendrivelab.com/Dataset_Survey_Chinese.pdf)] [[code](https://github.com/OpenDriveLab/DriveAGI)]
## Vehicles Publicly Available Datasets
* [Porto](https://www.kaggle.com/c/pkdd-15-predict-taxi-service-trajectory-i/data), [website](https://archive.ics.uci.edu/ml/datasets/Taxi+Service+Trajectory+-+Prediction+Challenge,+ECML+PKDD+2015)
* [NGSIM](https://data.transportation.gov/Automobiles/Next-Generation-Simulation-NGSIM-Vehicle-Trajector/8ect-6jqj)
* [NYC](https://www1.nyc.gov/site/tlc/about/tlc-trip-record-data.page)
* [T-drive](https://www.microsoft.com/en-us/research/publication/t-drive-trajectory-data-sample/)
* [Greek Trucks](http://www.chorochronos.org/)
* [highD](https://www.highd-dataset.com/)
* [inD](https://www.ind-dataset.com/)
* [rounD](https://www.round-dataset.com/)
* [uniD](https://www.unid-dataset.com/)
* [exiD](https://www.exid-dataset.com/)
* [Dronalize](https://github.com/westny/dronalize)
* [Mirror-Traffic](http://www.scenarios.cn/html/dataset.html)
* [Argoverse Website](https://www.argoverse.org/), [Argoverse 1](https://openaccess.thecvf.com/content_CVPR_2019/papers/Chang_Argoverse_3D_Tracking_and_Forecasting_With_Rich_Maps_CVPR_2019_paper.pdf), [Argoverse 2](https://arxiv.org/pdf/2301.00493.pdf)
* [ApolloScape](http://apolloscape.auto/trajectory.html)
* [INTERACTION](https://interaction-dataset.com/)
* [Waymo Open Dataset](https://waymo.com/open/)
* [Cityscapes](https://www.cityscapes-dataset.com/)
* [KITTI](http://www.cvlibs.net/datasets/kitti/)
* [nuScenes](https://www.nuscenes.org/)
* [TRAF](https://gamma.umd.edu/researchdirections/autonomousdriving/trafdataset)
* [Lyft Level 5](https://level-5.global/)
* [METEOR](https://gamma.umd.edu/researchdirections/autonomousdriving/meteor/)
* [DiDi GAIA](https://outreach.didichuxing.com/research/opendata/), [D²-City](https://www.scidb.cn/en/detail?dataSetId=804399692560465920&dataSetType=personal), [paper](https://arxiv.org/pdf/1904.01975)
* [Shanghai & Hangzhou](https://dl.acm.org/doi/abs/10.1145/2700478)
* [Beijing](https://dl.acm.org/doi/10.1145/2525314.2525343)
* [VMT](https://ieeexplore.ieee.org/document/6482546)
* [TRAFFIC](https://ieeexplore.ieee.org/document/7565640), [website](https://min.sjtu.edu.cn/lwydemo/Trajectory%20analysis.htm)
* [CROSS](https://cvrr-nas.ucsd.edu/publications/2011/Morris_PAMI2011.pdf), [website](http://cvrr.ucsd.edu/bmorris/datasets/)
* [Ubiquitous Traffic Eyes (UTE)](http://seutraffic.com/#/home)
## Pedestrians Publicly Available Datasets
* [GeoLife](https://www.microsoft.com/en-us/research/publication/geolife-gps-trajectory-dataset-user-guide/)
* [UCY](https://graphics.cs.ucy.ac.cy/research/downloads/crowd-data)
* [ETH](https://icu.ee.ethz.ch/research/datsets.html), [paper](https://ethz.ch/content/dam/ethz/special-interest/baug/igp/photogrammetry-remote-sensing-dam/documents/pdf/pellegrini09iccv.pdf)
* [Stanford Drone Dataset](https://cvgl.stanford.edu/projects/uav_data/)
* [TrajNet](http://trajnet.stanford.edu/)
* [Oxford Town Center](https://exposing.ai/oxford_town_centre/)
* [New York Grand Central Station](https://www.ee.cuhk.edu.hk/~xgwang/grandcentral.html), [paper](https://ieeexplore.ieee.org/abstract/document/5995459), [paper](https://people.csail.mit.edu/bzhou/project/cvpr2012/zhoucvpr2012.pdf), [paper](https://openaccess.thecvf.com/content_cvpr_2015/papers/Yi_Understanding_Pedestrian_Behaviors_2015_CVPR_paper.pdf)
* [PIE](https://data.nvision2.eecs.yorku.ca/PIE_dataset/)
* [JAAD](https://data.nvision2.eecs.yorku.ca/JAAD_dataset/)
* [DS4C-PPP](https://www.kaggle.com/datasets/kimjihoo/coronavirusdataset)
* [BDBC COVID-19](https://github.com/BDBC-KG-NLP/COVID-19-tracker)
* [Vi-Fi](https://sites.google.com/winlab.rutgers.edu/vi-fidataset/home)
## Others Agents Datasets
### Aircraft
* [LocaRDS](https://atmdata.github.io/)
* [ZUMAVD](https://rpg.ifi.uzh.ch/zurichmavdataset.html)
### Ship
* [Ushant](https://figshare.com/articles/dataset/Ushant_AIS_dataset/8966273)
* [Cargo](https://link.springer.com/article/10.1007/s10707-020-00421-y)
### Hurricane and Animal
* [HURDAT2](https://www.nhc.noaa.gov/data/)
* [Movebank](https://www.movebank.org/cms/movebank-main)
# 🌹 Acknowledgments
We are grateful to the authors and developers who provided the papers, the open-source code, and the project website! Thank you for your positive contributions to the agent trajectory prediction community. Your thoughts and contributions are a green signal for us. If you have suggestions or additional insights, feel free to open an issue or submit a pull request.
# 🌟 Star History
[](https://star-history.com/#Psychic-DL/Awesome-Traffic-Agent-Trajectory-Prediction&Date)