TrafficBots V15: Traffic Simulation via Conditional VAEs and Transformers with Relative Pose Encoding
<|reference_start|>TrafficBots V15: Traffic Simulation via Conditional VAEs and Transformers with Relative Pose Encoding: In this technical report we present TrafficBots V1.5, a baseline method for the closed-loop simulation of traffic agents. TrafficBots V1.5 achieves baseline-level performance and a 3rd place ranking in the Waymo Open Sim Agents Challenge (WOSAC) 2024. It is a simple baseline that combines TrafficBots, a CVAE-based multi-agent policy conditioned on each agent's individual destination and personality, and HPTR, the heterogeneous polyline transformer with relative pose encoding. To improve the performance on the WOSAC leaderboard, we apply scheduled teacher-forcing at the training time and we filter the sampled scenarios at the inference time. The code is available at https://github.com/zhejz/TrafficBotsV1.5.<|reference_end|>
@article{zhang2024trafficbots,
    title={TrafficBots V1.5: Traffic Simulation via Conditional VAEs and
  Transformers with Relative Pose Encoding},
    author={Zhejun Zhang, Christos Sakaridis, Luc Van Gool},
    journal={arXiv preprint arXiv:2406.10898},
    year={2024},
    archivePrefix={arXiv},
    eprint={2406.10898},
    primaryClass={cs.RO cs.CV}
}