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Top AI Repos tracks AI repositories on GitHub and answers two different questions about each one: is it moving right now, and would you bet a product on it.
Top AI Repos tracks AI repositories on GitHub and answers two different questions about each one: is it moving right now, and would you bet a product on it.
A selection of state-of-the-art research materials on trajectory prediction
| Date | Stars |
|---|---|
| 2026-07-24 | 1685 |
| 2026-07-25 | 1685 |
| 2026-07-28 | 1685 |
| 2026-07-30 | 1685 |
| 2026-08-06 | 1685 |
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# Awesome Interaction-Aware Behavior and Trajectory Prediction     This is a checklist of state-of-the-art research materials (datasets, blogs, papers and public codes) related to trajectory prediction. Wish it could be helpful for both academia and industry. (Still updating) **Maintainers**: [**Jiachen Li**](https://jiachenli94.github.io) (Stanford University); [**Hengbo Ma**](https://www.linkedin.com/in/hengboma/), [**Jinning Li**](https://www.linkedin.com/in/jinningli/) (University of California, Berkeley) **Emails**: [email protected]; {hengbo_ma, jinning_li}@berkeley.edu Please feel free to pull request to add new resources or send emails to us for questions, discussion and collaborations. **Note**: [**Here**](https://github.com/jiachenli94/Awesome-Decision-Making-Reinforcement-Learning) is also a collection of materials for reinforcement learning, decision making and motion planning. Please consider citing our work if you found this repo useful: ``` @inproceedings{li2020evolvegraph, title={EvolveGraph: Multi-Agent Trajectory Prediction with Dynamic Relational Reasoning}, author={Li, Jiachen and Yang, Fan and Tomizuka, Masayoshi and Choi, Chiho}, booktitle={2020 Advances in Neural Information Processing Systems (NeurIPS)}, year={2020} } @inproceedings{li2019conditional, title={Conditional Generative Neural System for Probabilistic Trajectory Prediction}, author={Li, Jiachen and Ma, Hengbo and Tomizuka, Masayoshi}, booktitle={2019 IEEE/RSJ International Conference on Intelligent Robots and Systems (IROS)}, pages={6150--6156}, year={2019}, organization={IEEE} } ``` ### Table of Contents <!-- TOC depthFrom:1 depthTo:6 withLinks:1 updateOnSave:1 orderedList:0 --> - [**Datasets**](#datasets) - [Vehicles and Traffic](#vehicles-and-traffic) - [Pedestrians](#pedestrians) - [Sport Players](#sport-players) - [**Literature and Codes**](#literature-and-codes) - [Survey Papers](#survey-papers) - [Physics Systems with Interaction](#physics-systems-with-interaction) - [Intelligent Vehicles and Pedestrians](#intelligent-vehicles-and-pedestrians) - [Mobile Robots](#mobile-robots) - [Sport Players](#sport-players) - [Benchmark and Evaluation Metrics](#benchmark-and-evaluation-metrics) - [Others](#others) <!-- /TOC --> ## **Datasets** ### Vehicles and Traffic | Dataset | Agents | Scenarios | Sensors | | :----------------------------------------------------------: | :--------------------------: | :-----------------------: | :--------------------: | | [Waymo Open Dataset](https://waymo.com/open/) | vehicles / cyclists / people | urban / highway | LiDAR / camera / Radar | | [Argoverse](https://www.argoverse.org/) | vehicles / cyclists / people | urban / highway | LiDAR / camera / Radar | | [nuScenes](https://www.nuscenes.org/) | vehicles | urban | camera / LiDAR / Radar | | [highD](https://www.highd-dataset.com/) | vehicles | highway | camera | | [inD](https://www.ind-dataset.com/) | vehicles | highway | camera | | [roundD](https://www.round-dataset.com/) | vehicles | highway | camera | | [BDD100k](https://bdd-data.berkeley.edu/) | vehicles / cyclists / people | highway / urban | camera | | [KITTI](http://www.cvlibs.net/datasets/kitti/) | v
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Would you bet a product on this? Bounded 0–100 and slow moving.
matched fp:29816f538d4128e3, topic:autonomous-driving, readme:motion planning
matched fp:29816f538d4128e3, topic:deep-learning
matched fp:29816f538d4128e3, topic:computer-vision