一句话：提出 VioHawk，将交通法规形式化为地图危险区域，用确定性变异引导仿真测试，高效触发自动驾驶违规。
交通法规合规是自动驾驶系统商业化的前提。现有基于搜索的仿真测试依赖预定义启发式近似触发违规的可能性，缺乏对“变异哪些配置、如何变异”的确定性指导，在巨大场景空间中效率低下。VioHawk 将每个仿真时刻的交通法规形式化为地图上的危险/非危险区域（如红灯时交叉口标为危险区域），再用确定性变异操作诱导自车驶入法规指定的危险区域，从而可靠触发违规。作者在工业级 ADS Apollo 上评估，并构建包含 42 个真实交通法规违规场景的基准。相比现有工具，VioHawk 在相同时间预算下复现的违规多 3.1~13.3 倍，为已识别违规节省 1.6~8.9 倍复现时间，并在 Apollo 7.0/8.0 上发现 9+8 个未知违规。
As highlighted in authoritative standards (e.g., ISO21448), traffic law compliance is a fundamental prerequisite for the commercialization of autonomous driving systems (ADS). Hence, manufacturers are in severe need of techniques to detect harsh driving situations in which the target ADS would violate traffic laws. To achieve this goal, existing works commonly resort to searching-based simulation testing, which continuously adjusts the scenario configurations (e.g., add new vehicles) of initial simulation scenarios and hunts for critical scenarios. Specifically, they apply pre-defined heuristics on each mutated scenario to approximate the likelihood of triggering ADS traffic violations, and accordingly perform searching scheduling. However, with those comparably more critical scenarios in hand, they fail to offer deterministic guidance on which and how scenario configurations should be further mutated to reliably trigger the target ADS misbehaviors. Hence, they inevitably suffer from meaningless efforts to traverse the huge scenario search space. In this work, we propose VioHawk, a novel simulation-based fuzzer that hunts for scenarios that imply ADS traffic violations. Our key idea is that, traffic law regulations can be formally modeled as hazardous/non-hazardous driving areas on the map at each timestamp during ADS simulation testing (e.g., when the traffic light is red, the intersection is marked as hazardous areas). Following this idea, VioHawk works by inducing the autonomous vehicle to drive into the law-specified hazardous areas with deterministic mutation operations. We evaluated the effectiveness of VioHawk in testing industry-grade ADS (i.e., Apollo). We constructed a benchmark dataset that contains 42 ADS violation scenarios against real-world traffic laws. Compared to existing tools, VioHawk can reproduce 3.1X~13.3X more violations within the same time budget, and save 1.6X~8.9X the reproduction time for those identified violations. Finally, with the help of VioHawk, we identified 9+8 previously unknown violations of real-world traffic laws on Apollo 7.0/8.0.
@inproceedings{paperbot8536,
title = {VioHawk: Detecting Traffic Violations of Autonomous Driving Systems through Criticality-Guided Simulation Testing},
author = {Zhongrui Li and Jiarun Dai and Zongan Huang and Nianhao You and Yuan Zhang and Min Yang},
booktitle = {ISSTA},
year = {2024},
doi = {10.1145/3650212.3680325}
}