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.

在这篇技术报告中，我们介绍了 2023 Waymo 开放式模拟智能体挑战赛 (WOSAC) 第二名的解决方案。我们提出了一种简单而有效的自回归方法来模拟多智能体行为，该方法建立在一种被称为运动变换器 (Motion Transformer) 的知名多模态动作预测框架上，并应用了后处理算法。我们的提交名称为 MTR+++，在 2023 WOSAC 的真实性元指标上取得了 0.4697。此外，在挑战赛后提出了一种基于 MTR 的修改模型 MTR_E，它有更好的得分 0.4911，并在 2023 年 6 月 25 日排名 WOSAC 的第三名。

2023 Waymo Open Sim Agents Challenge 第二名解决方案

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

Simulation has the potential to massively scale evaluation of self-driving
systems enabling rapid development as well as safe deployment. To close the gap
between simulation and the real world, we need to simulate realistic
multi-agent behaviors. Existing simulation environments rely on heuristic-based
models that directly encode traffic rules, which cannot capture irregular
maneuvers (e.g., nudging, U-turns) and complex interactions (e.g., yielding,
merging). In contrast, we leverage real-world data to learn directly from human
demonstration and thus capture a more diverse set of actor behaviors. To this
end, we propose TrafficSim, a multi-agent behavior model for realistic traffic
simulation. In particular, we leverage an implicit latent variable model to
parameterize a joint actor policy that generates socially-consistent plans for
all actors in the scene jointly. To learn a robust policy amenable for long
horizon simulation, we unroll the policy in training and optimize through the
fully differentiable simulation across time. Our learning objective
incorporates both human demonstrations as well as common sense. We show
TrafficSim generates significantly more realistic and diverse traffic scenarios
as compared to a diverse set of baselines. Notably, we can exploit trajectories
generated by TrafficSim as effective data augmentation for training better
motion planner.

利用 TrafficSim 模拟真实的自动驾驶系统中的多智能体交通模型，以更多样化的人类演示数据为基础，生成人类社交一致的行驶计划，生成更真实、更多样化的交通场景，可作为训练更好的运动规划员的有效数据增强。