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Awesome-Interaction-aware-Trajectory-Prediction
A selection of state-of-the-art research materials on trajectory prediction
https://github.com/jiachenli94/Awesome-Interaction-aware-Trajectory-Prediction
Last synced: about 4 hours ago
JSON representation
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**Literature and Codes**
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Intelligent Vehicles & Traffic & Pedestrians
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- [paper - tlabs.github.io/trace-pace/)]
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- [paper - SJTU/LED)]
- [paper - mars-lab.github.io/ViP3D/)]
- [paper - SJTU/EqMotion)]
- [paper - THU/DAIR-V2X-Seq)]
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- [paper - ai.github.io/adapt/)]
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- [paper - TrajGen)]
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- [paper - cloud/SSL-Lanes)]
- [paper - ADG/LimSim)]
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- [paper - Tong/ssagcn_for_path_prediction)]
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- [paper - IDM)]
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- [paper - transformer.github.io/sim-agents/)]
- [paper - handsome/Joint-Multipathpp)]
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- [paper - Planner/)]
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- [paper - SJTU/MemoNet)]
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- [paper - attack.github.io/)]
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- [paper - epfl/causalmotion), [code](https://github.com/sherwinbahmani/ynet_adaptive)]
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- [paper - cs/TDOR)]
- [paper - mars-lab.github.io/M2I/)]
- [paper - SJTU/GroupNet)]
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- [paper - Implicit)] [[website](https://www.abduallahmohamed.com/social-implicit-amdamv-adefde-demo)]
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- [paper - TL/D2-TPred)]
- [paper - Trajectory-Prediction-via-Neural-Social-Physics)]
- [paper - SSL)]
- [paper - of-the-history)]
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- [paper - P3)]
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- [paper - inf.mpg.de/motion-transformer-with-global-intention-localization-and-local-movement-refinement/)]
- [paper - sun/IMMA)]
- [paper - interactive-predict-plan)]
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- [paper - TF)]
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- [paper - prediction)]
- [paper - ju/crat-pred)]
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- [paper - ML/HAICU)] [[trajdata](https://github.com/nvr-avg/trajdata)]
- [paper - Aerial-Robotics/DSP)]
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- [paper - planning.github.io/)]
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- [paper - Zhang/AI-TP)]
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- [paper - motion-prediction-challenge-2022-multipath-plus-plus)]
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- [paper - SJTU/Collaborative-Uncertainty)]
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- [paper - motion-prediction-challenge-2022-multipath-plus-plus)\]
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- [paper - Transformer-PedTraj)]
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- [paper - yuan.com/agentformer/)]
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- [paper - GAN)]
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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 - inf.mpg.de/departments/computer-vision-and-machine-learning/research/euro-pvi-dataset)]
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- [paper - STL-Lab/ECCO)]
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- [paper - Trajectory-Prediction-for-Pedestrian-Video-Anomaly-Detection)]
- [paper - adv-workshop.github.io/short_paper/s-attack-arow2021.pdf)] [[code](https://s-attack.github.io/)]
- [paper - epfl/RRB)]
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- [paper - epfl/trajnetplusplusbaselines)]
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- [paper - research/VTP)]
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- [paper - Stage-Gan-in-trajectory-generation)]
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- [paper - epfl/AdversarialLoss-SGAN)\]
- [*Precognition Workshop* - Modal_Distributions_of_Pedestrian_Trajectories_With_GANs_CVPRW_2019_paper.pdf)\], \[[code](<https://github.com/amiryanj/socialways>)\]
- [paper - prediction)\]
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- [paper - gan-zoo/blob/master/README.md)\]
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Survey Papers
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Physics Systems with Interaction
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Mobile Robots
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Sport Players
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Benchmark and Evaluation Metrics
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Others
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**Datasets**
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Pedestrians
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Vehicles and Traffic
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Sport Players
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