The 2nd Place Solution for 2023 Waymo Open Sim Agents Challenge

TL;DR

This paper presents the second-place solution for the 2023 Waymo Open Sim Agents Challenge, utilizing an autoregressive approach based on Motion Transformer to simulate multi-agent behaviors with high realism.

Contribution

Introduces MTR+++ and MTR_E, effective autoregressive models built on Motion Transformer for multi-agent behavior simulation in autonomous driving.

Findings

MTR+++ achieved 0.4697 on the Realism Meta metric.

MTR_E scored 0.4911, ranking 3rd on the leaderboard.

The proposed methods outperform previous approaches in realism and accuracy.

Abstract

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.