论文标题
在行为多样化的模拟中，使用反事实分析发现自动驾驶汽车的可避免的计划者故障
Discovering Avoidable Planner Failures of Autonomous Vehicles using Counterfactual Analysis in Behaviorally Diverse Simulation
论文作者
论文摘要
自动化的车辆需要在模拟中进行详尽的测试，以在公共道路上部署之前检测到尽可能多的安全关键故障。在这项工作中，我们专注于自动机器人的核心决策组成部分：他们的计划算法。我们介绍了一个规划师测试框架，该框架利用了模拟行为多样的交通参与者的最新进展。使用大规模搜索，我们生成，检测和表征导致碰撞的动态场景。特别是，我们提出的方法是区分不可避免的事故和可避免的事故，特别是在部署前必须纠正规划师特定的缺陷。通过在复杂的多代理相交方案中的实验，我们表明我们的方法确实可以找到广泛的关键计划者故障。
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.
