Recent research in pedestrian simulation often aims to develop realistic behaviors in various situations, but it is challenging for existing algorithms to generate behaviors that identify weaknesses in automated vehicles' performance in extreme and unlikely scenarios and edge cases. To address this, specialized pedestrian behavior algorithms are needed. Current research focuses on realistic trajectories using social force models and reinforcement learning based models. However, we propose a reinforcement learning algorithm that specifically targets collisions and better uncovers unique failure modes of automated vehicle controllers. Our algorithm is efficient and generates more severe collisions, allowing for the identification and correction of weaknesses in autonomous driving algorithms in complex and varied scenarios.

翻译：近年来，行人模拟研究常致力于开发在各种场景下的逼真行为，但现有算法难以生成能够识别自动驾驶车辆在极端、罕见场景及边缘案例中性能缺陷的行为。为此，需要专门的行人行为算法。当前研究多聚焦于通过社会力模型和基于强化学习的模型生成真实轨迹。然而，我们提出一种专门针对碰撞的强化学习算法，能更有效地揭示自动驾驶控制器特有的失效模式。该算法高效且能生成更严重的碰撞，从而在复杂多变的场景中识别并修正自动驾驶算法的缺陷。