The 2nd Place Solution for 2023 Waymo Open Sim Agents Challenge
This work addresses the challenge of realistic multi-agent simulation for autonomous driving systems, representing an incremental improvement over existing methods.
The authors tackled the problem of simulating multi-agent behaviors in autonomous driving by proposing an autoregressive method based on the Motion Transformer framework, achieving a Realism Meta metric score of 0.4697 for 2nd place in the 2023 Waymo Open Sim Agents Challenge, with a later modified model scoring 0.4911 for 3rd place.
In this technical report, we present the 2nd place solution of 2023 Waymo Open Sim Agents Challenge (WOSAC)[4]. We propose a simple yet effective autoregressive method for simulating multi-agent behaviors, which is built upon a well-known multimodal motion forecasting framework called Motion Transformer (MTR)[5] with postprocessing algorithms applied. Our submission named MTR+++ achieves 0.4697 on the Realism Meta metric in 2023 WOSAC. Besides, a modified model based on MTR named MTR_E is proposed after the challenge, which has a better score 0.4911 and is ranked the 3rd on the leaderboard of WOSAC as of June 25, 2023.