As self-driving systems become better, simulating scenarios where the autonomy stack may fail becomes more important. Traditionally, those scenarios are generated for a few scenes with respect to the planning module that takes ground-truth actor states as input. This does not scale and cannot identify all possible autonomy failures, such as perception failures due to occlusion. In this paper, we propose AdvSim, an adversarial framework to generate safety-critical scenarios for any LiDAR-based autonomy system. Given an initial traffic scenario, AdvSim modifies the actors' trajectories in a physically plausible manner and updates the LiDAR sensor data to match the perturbed world. Importantly, by simulating directly from sensor data, we obtain adversarial scenarios that are safety-critical for the full autonomy stack. Our experiments show that our approach is general and can identify thousands of semantically meaningful safety-critical scenarios for a wide range of modern self-driving systems. Furthermore, we show that the robustness and safety of these systems can be further improved by training them with scenarios generated by AdvSim.

翻译：随着自动驾驶系统的不断进步，模拟自主堆栈可能失效的场景变得愈发重要。传统方法仅针对少数场景生成案例，且其规划模块以真实智能体状态作为输入。此类方法难以扩展，更无法识别所有潜在的自主系统故障——例如由遮挡导致的感知失效。本文提出对抗性框架AdvSim，为任意基于激光雷达的自主系统生成安全关键场景。给定初始交通场景后，AdvSim以物理合理的方式修改智能体运动轨迹，并同步更新激光雷达传感器数据以匹配扰动后的世界。关键之处在于，通过直接基于传感器数据进行模拟，我们能够获得对整个自主堆栈均构成安全威胁的对抗性场景。实验表明，该方法具有通用性，可为多种现代自动驾驶系统识别出数千个具有语义意义的安全关键场景。此外，通过使用AdvSim生成的场景对系统进行训练，可进一步显著提升其鲁棒性与安全性。