TrafficBots V1.5: Traffic Simulation via Conditional VAEs and Transformers with Relative Pose Encoding
This is an incremental improvement for traffic simulation in autonomous driving, providing a baseline method for researchers and practitioners.
The paper tackles the problem of closed-loop simulation of traffic agents by introducing TrafficBots V1.5, a baseline method that combines a CVAE-based policy and a transformer with relative pose encoding, achieving baseline-level performance and a 3rd place ranking in the Waymo Open Sim Agents Challenge 2024.
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.