Ecosyste.ms: Awesome
An open API service indexing awesome lists of open source software.
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
Last synced: about 12 hours ago
JSON representation
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Conference Papers 2021
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- [paper - yuan.com/agentformer/)]
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- [paper - Aware-Motion-Prediction)]
- [paper - ri.com/loki)]
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- [paper - Neighborhood-Interaction-for-Heterogeneous-Trajectory-Prediction)]
- [paper - epfl/social-nce)]
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- [paper - ai.github.io/adapt/)]
- [paper - ADG/LimSim)]
- [paper - THU/DAIR-V2X-Seq)]
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- [paper - mars-lab.github.io/ViP3D/)]
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- [paper - ADG/LimSim)]
- [paper - THU/DAIR-V2X-Seq)]
- [paper - DISCOVER/INT2)]
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Journal Papers 2023
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Others 2023
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- [paper - WM)] [[website](https://drive-wm.github.io/)]
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- [paper - driver.github.io/)]
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- [paper - handsome/Joint-Multipathpp)]
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- [paper - machine-learning/pedestrianpathprediction)]
- [paper - epfl/social-transmotion)]
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- [paper - hub.github.io/SceneDM/)]
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- [paper - gaia-1/)]
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- [paper - ADG/DriveLikeAHuman)]
- [paper - adg.github.io/DiLu/)]
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- [paper - with-LLMs)]
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- [paper - research/waymax)] [[website](https://waymo.com/intl/zh-cn/research/waymax/)]
- [paper
- [paper - Driver)]
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- [paper - gvl.github.io/Agent-Driver/)]
- [paper
- [paper - SJTU/Awesome-LLM4AD)]
- [paper
- [paper - ADG/GPT4V-AD-Exploration)]
- [paper - WM)] [[website](https://drive-wm.github.io/)]
- [paper - Multimodal-LLM-Autonomous-Driving)]
- [paper - ad.github.io/)] [[code](https://github.com/wenyuqing/panacea)]
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- [paper - ADG/DriveLikeAHuman)]
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- [paper - THU/V2X-Graph)]
- [paper - epfl/social-transmotion)]
- [paper - hub.github.io/SceneDM/)]
- [paper
- [paper - mpc)]
- [paper
- [paper - research/waymax)] [[website](https://waymo.com/intl/zh-cn/research/waymax/)]
- [paper - gvl.github.io/Agent-Driver/)]
- [paper - SJTU/Awesome-LLM4AD)]
- [paper
- [paper - Driver)]
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Conference Papers 2024
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- [paper - Behavior-aware-Model)]
- [paper - QA)]
- [paper - Behavior-aware-Model)]
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- [paper - chao-Zhang/OOSTraj)]
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- [paper - QA)]
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Others 2024
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- [paper - ADG/LeapAD)]
- [paper - mars-lab.github.io/DriveVLM/)]
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- [paper - Trajectory-Predition)]
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- [paper - sim.github.io/)]
- [paper - behavior-diffusion)]
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- [paper - fib-lab/LCSim)]
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- [paper - behavior-diffusion)]
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- [paper - epfl/UniTraj)]
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Others Agents Datasets
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Ship
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Aircraft
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Hurricane and Animal
- HURDAT2
- Movebank
- ![Star History Chart - history.com/#Psychic-DL/Awesome-Traffic-Agent-Trajectory-Prediction&Date)
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Journal Papers
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Others 2021
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Conference Papers 2019
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- [paper
- [paper
- [paper
- [paper
- [paper
- [paper
- [paper - programmatic-supervision)]
- [paper
- [paper - xx/TrafficPredict)]
- [paper
- [paper - architecture-details)]
- [paper - prediction)] [[website](https://next.cs.cmu.edu/)]
- [paper
- [paper - LSTM)]
- [paper
- [paper - freiburg/Multimodal-Future-Prediction)]
- [paper - api)]
- [paper
- [paper
- [paper
- [paper
- [paper
- [paper - xx/STGAT)]
- [paper
- [paper
- [paper
- [paper
- [paper
- [paper
- [paper
- [paper
- [paper
- [paper
- [paper
- [paper
- [paper
- [paper - multiple-futures-prediction)]
- [paper
- [paper
- [paper
- [paper
- [paper
- [paper
- [paper
- [paper - 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)]
- [paper
- [paper
- [paper
- [paper
- [paper - epfl/AdversarialLoss-SGAN)]
- [paper
- [paper
- [paper - 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)]
- [paper
- [paper
- [paper
- [paper
- [paper
- [paper
- [paper
- [paper
- [paper
- [paper
- [paper
- [paper
- [paper
- [paper
- [paper
- [paper
- [paper - 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)]
- [paper
- [paper
- [paper - xx/TrafficPredict)]
- [paper
- [paper
- [paper
- [paper
- [paper
- [paper
- [paper
- [paper
- [paper
- [paper
- [paper
- [paper
- [paper
- [paper
- [paper - 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)]
- [paper
- [paper
- [paper
- [paper
- [paper
- [paper
- [paper
- [paper
- [paper
- [paper
- [paper
- [paper
- [paper
- [paper
- [paper
- [paper
- [paper - 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)]
- [paper
- [paper
- [paper
- [paper
- [paper
- [paper
- [paper
- [paper
- [paper
- [paper
- [paper
- [paper
- [paper
- [paper
- [paper
- [paper
- [paper - 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)]
- [paper
- [paper
- [paper - programmatic-supervision)]
- [paper
-
Vehicles Publicly Available Datasets
- Shanghai & Hangzhou
- Beijing
- NGSIM
- NYC
- T-drive
- Greek Trucks
- uniD
- exiD
- Porto - +Prediction+Challenge,+ECML+PKDD+2015)
- Mirror-Traffic
- Argoverse Website
- INTERACTION
- Cityscapes
- nuScenes
- TRAF
- Lyft Level 5
- METEOR
- DiDi GAIA - City](https://www.scidb.cn/en/detail?dataSetId=804399692560465920&dataSetType=personal), [paper](https://arxiv.org/pdf/1904.01975)
- VMT
- TRAFFIC
- CROSS
- Ubiquitous Traffic Eyes (UTE)
- Dronalize
-
Others
-
Journal Papers 2019
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Others 2019
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Conference Papers 2020
- [paper
- [paper
- [paper - SONG/PiP-Planning-informed-Prediction)]
- [paper
- [paper - plus-plus)]
- [paper
- [paper - DATF)]
- [paper - Path-Prediction)]
- [paper
- [paper
- [paper - research/LaneGCN)]
- [paper
- [paper
- [paper
- [paper - Transformer)]
- [paper
- [paper - Trajectory-Predition)]
- [paper - amr/social_vrnn)]
- [paper
- [paper
- [paper
- [paper
- [paper
- [paper
- [paper
- [paper
- [paper
- [paper
- [paper
- [paper
- [paper
- [paper
- [paper
- [paper
- [paper
- [paper
- [paper
- [paper - STGCNN)]
- [paper
- [paper
- [paper
- [paper
- [paper
- [paper
- [paper
- [paper - models)]
- [paper - and-DPP)]
- [paper - GAN)]
- [paper
- [paper
- [paper
- [paper
- [paper
- [paper - Trajectory-Predition)]
- [paper
- [paper - Transformer)]
- [paper
- [paper
- [paper
- [paper
- [paper
- [paper - Transformer)]
- [paper
- [paper
- [paper
- [paper
- [paper
- [paper
- [paper
- [paper - Transformer)]
- [paper
- [paper
- [paper
- [paper
- [paper
- [paper - Transformer)]
- [paper
- [paper
- [paper
- [paper
- [paper
- [paper
- [paper
- [paper - Transformer)]
- [paper
- [paper
- [paper
- [paper
- [paper
- [paper
- [paper
- [paper
- [paper
- [paper - and-DPP)]
-
Journal Papers 2020
- [paper
- [paper
- [paper
- [paper
- [paper
- [paper
- [paper
- [paper
- [paper
- [paper
- [paper
- [paper - trajectory-prediction), [code](https://github.com/tdavchev/Stochastic-Futures-Prediction)]
- [paper
- [paper
- [paper
- [paper
- [paper
- [paper - pred-irl)]
- [paper
- [paper
- [paper
- [paper
- [paper
- [paper
- [paper
- [paper
- [paper - trajectory-prediction), [code](https://github.com/tdavchev/Stochastic-Futures-Prediction)]
- [paper
- [paper
- [paper
- [paper
- [paper
- [paper
- [paper
- [paper
- [paper
- [paper
- [paper
- [paper - trajectory-prediction), [code](https://github.com/tdavchev/Stochastic-Futures-Prediction)]
- [paper
- [paper
- [paper
- [paper
- [paper
- [paper
- [paper
- [paper
- [paper
- [paper
- [paper
- [paper - trajectory-prediction), [code](https://github.com/tdavchev/Stochastic-Futures-Prediction)]
- [paper
- [paper
- [paper
- [paper
- [paper
- [paper
- [paper
- [paper
- [paper
- [paper
- [paper
- [paper - trajectory-prediction), [code](https://github.com/tdavchev/Stochastic-Futures-Prediction)]
- [paper
- [paper
- [paper
- [paper
- [paper
- [paper
- [paper
- [paper
- [paper
- [paper
- [paper
- [paper - trajectory-prediction), [code](https://github.com/tdavchev/Stochastic-Futures-Prediction)]
- [paper
- [paper
- [paper
- [paper
-
Others 2020
-
Journal Papers 2021
- [paper - adv-workshop.github.io/short_paper/s-attack-arow2021.pdf)] [[code](https://s-attack.github.io/)]
- [paper - epfl/RRB)]
- [paper
- [paper
- [paper
- [paper
- [paper
- [paper - research/VTP)]
- [paper
- [paper
- [paper
- [paper
- [paper
- [paper
- [paper - Stage-Gan-in-trajectory-generation)]
- [paper
- [paper
- [paper
- [paper
- [paper - trajectory-prediction)]
- [paper
- [paper
- [paper
- [paper
- [paper
- [paper
- [paper
- [paper
- [paper
- [paper
- [paper
- [paper
- [paper
- [paper
- [paper
- [paper
- [paper - trajectory-prediction)]
- [paper
- [paper
- [paper
- [paper
- [paper
- [paper
- [paper
- [paper
- [paper
- [paper
- [paper
- [paper
- [paper - trajectory-prediction)]
- [paper
- [paper
- [paper
- [paper
- [paper
- [paper
- [paper
- [paper
- [paper
- [paper
- [paper
- [paper - trajectory-prediction)]
- [paper
- [paper
- [paper
- [paper
- [paper
- [paper
- [paper
- [paper
- [paper
- [paper
- [paper
- [paper - trajectory-prediction)]
- [paper
- [paper
- [paper
- [paper
- [paper
- [paper
- [paper
- [paper
- [paper
- [paper
- [paper
- [paper
- [paper
- [paper
- [paper
- [paper
- [paper
- [paper - trajectory-prediction)]
- [paper - adv-workshop.github.io/short_paper/s-attack-arow2021.pdf)] [[code](https://s-attack.github.io/)]
- [paper - epfl/trajnetplusplusbaselines)]
- [paper
- [paper
- [paper
-
Conference Papers 2022
- [paper
- [paper
- [paper
- [paper
- [paper
- [paper - SJTU/MemoNet)]
- [paper
- [paper - attack.github.io/)]
- [paper
- [paper
- [paper
- [paper
- [paper - epfl/causalmotion), [code](https://github.com/sherwinbahmani/ynet_adaptive)]
- [paper
- [paper
- [paper
- [paper - cs/TDOR)]
- [paper - mars-lab.github.io/M2I/)]
- [paper - SJTU/GroupNet)]
- [paper
- [paper
- [paper - MR)]
- [paper
- [paper
- [paper
- [paper
- [paper
- [paper
- [paper
- [paper - attention)]
- [paper
- [paper - traj-gen)]
- [paper
- [paper
- [paper
- [paper
- [paper - TF)]
- [paper
- [paper
- [paper
- [paper - prediction)]
- [paper - ju/crat-pred)]
- [paper
- [paper
- [paper
- [paper
- [paper - ML/HAICU)] [[trajdata](https://github.com/nvr-avg/trajdata)]
- [paper - Aerial-Robotics/DSP)]
- [paper
- [paper - Implicit)] [[website](https://www.abduallahmohamed.com/social-implicit-amdamv-adefde-demo)]
- [paper
- [paper
- [paper
- [paper
- [paper - TL/D2-TPred)]
- [paper - Trajectory-Prediction-via-Neural-Social-Physics)]
- [paper - SSL)]
- [paper - of-the-history)]
- [paper
- [paper
- [paper - P3)]
- [paper
- [paper
- [paper - inf.mpg.de/motion-transformer-with-global-intention-localization-and-local-movement-refinement/)]
- [paper - sun/IMMA)]
- [paper - interactive-predict-plan)]
- [paper
- [paper - planning.github.io/)]
- [paper
- [paper
- [paper
- [paper
- [paper
- [paper - cs/TDOR)]
- [paper - SJTU/GroupNet)]
- [paper
- [paper - P3)]
- [paper - inf.mpg.de/motion-transformer-with-global-intention-localization-and-local-movement-refinement/)]
- [paper
- [paper - attention)]
- [paper - ju/crat-pred)]
- [paper
- [paper
- [paper - attention)]
- [paper - ju/crat-pred)]
- [paper
- [paper
- [paper - attention)]
- [paper - ju/crat-pred)]
- [paper
- [paper
- [paper
- [paper - attention)]
- [paper - ju/crat-pred)]
- [paper
- [paper
- [paper - TL/D2-TPred)]
- [paper
- [paper
- [paper
- [paper
- [paper - attention)]
- [paper - ju/crat-pred)]
- [paper
- [paper
- [paper - SJTU/MemoNet)]
- [paper
- [paper
- [paper - epfl/causalmotion), [code](https://github.com/sherwinbahmani/ynet_adaptive)]
- [paper
- [paper
- [paper
- [paper - mars-lab.github.io/M2I/)]
- [paper
- [paper
- [paper - Aerial-Robotics/DSP)]
- [paper
- [paper - Implicit)] [[website](https://www.abduallahmohamed.com/social-implicit-amdamv-adefde-demo)]
- [paper
- [paper
- [paper - of-the-history)]
- [paper - interactive-predict-plan)]
- [paper
- [paper - Trajectory-Prediction-via-Neural-Social-Physics)]
-
Others 2022
- [paper
- [paper - SAR)]
- [paper
- [paper - motion-prediction-challenge-2022-multipath-plus-plus)]
- [paper
- [paper
- [paper
- [paper
- [paper
- [paper
- [paper
- [paper
- [paper
- [paper - SJTU/Collaborative-Uncertainty)]
- [paper
- [paper
- [paper - ai.github.io/ftgn/)]
- [paper - motion-prediction-challenge-2022-multipath-plus-plus)]
- [paper
- [paper
- [paper
- [paper
- [paper
- [paper - SAR)]
- [paper
- [paper
- [paper
- [paper
- [paper
- [paper
- [paper
- [paper - ai.github.io/ftgn/)]
-
Reviews about Datasets
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Pedestrians Publicly Available Datasets
- GeoLife
- UCY
- ETH - interest/baug/igp/photogrammetry-remote-sensing-dam/documents/pdf/pellegrini09iccv.pdf)
- Stanford Drone Dataset
- TrajNet
- Oxford Town Center
- New York Grand Central Station
- PIE
- JAAD
- DS4C-PPP
- Vi-Fi
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