Automated Vehicles require exhaustive testing in simulation to detect as many safety-critical failures as possible before deployment on public roads. In this work, we focus on the core decision-making component of autonomous robots: their planning algorithm. We introduce a planner testing framework that leverages recent progress in simulating behaviorally diverse traffic participants. Using large scale search, we generate, detect, and characterize dynamic scenarios leading to collisions. In particular, we propose methods to distinguish between unavoidable and avoidable accidents, focusing especially on automatically finding planner-specific defects that must be corrected before deployment. Through experiments in complex multi-agent intersection scenarios, we show that our method can indeed find a wide range of critical planner failures.

中文翻译：

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在行为多样化仿真中使用反事实分析发现自动驾驶汽车可避免的计划程序故障

自动化车辆需要在模拟中进行详尽的测试，以便在部署在公共道路上之前尽可能多地检测出对安全至关重要的故障。在这项工作中，我们专注于自主机器人的核心决策组件：其规划算法。我们引入了计划程序测试框架，该框架利用了模拟行为多样化的流量参与者的最新进展。使用大规模搜索，我们可以生成，检测和描述导致碰撞的动态场景。特别是，我们提出了一些方法来区分不可避免的事故和避免的事故，尤其着重于自动发现计划人员特定的缺陷，这些缺陷必须在部署之前进行纠正。通过在复杂的多主体交叉路口场景中进行的实验，我们证明了我们的方法确实可以发现各种各样的关键计划程序故障。