一句话：提出 Rocas，通过物理-网络协同变异与差分分析，精确定位自动驾驶事故的触发实体与系统误配置。
自动驾驶系统（ADS）安全日益重要，但事故后的根因分析仍存在明显空白。现有信息物理系统（CPS）根因分析技术主要面向无人机，难以应对 ADS 中更复杂的物理环境与深度学习模型带来的独特挑战。本文形式化定义了 ADS 根因分析问题，并提出 Rocas 框架，其核心是信息物理协同变异：同时利用物理与网络层面的变异，精确识别事故触发实体，并定位导致事故的目标 ADS 误配置。为进一步缩小搜索空间，作者设计了差分分析以识别应负责的模块。在 12 类 ADS 事故上的实验表明，Rocas 能有效且高效地缩小搜索空间并定位误配置；案例研究进一步展示了所识别误配置如何帮助理解事故背后的原因。
As Autonomous driving systems (ADS) have transformed our daily life, safety of ADS is of growing significance. While various testing approaches have emerged to enhance the ADS reliability, a crucial gap remains in understanding the accidents causes. Such post-accident analysis is paramount and beneficial for enhancing ADS safety and reliability. Existing cyber-physical system (CPS) root cause analysis techniques are mainly designed for drones and cannot handle the unique challenges introduced by more complex physical environments and deep learning models deployed in ADS. In this paper, we address the gap by offering a formal definition of ADS root cause analysis problem and introducing Rocas, a novel ADS root cause analysis framework featuring cyber-physical co-mutation. Our technique uniquely leverages both physical and cyber mutation that can precisely identify the accident-trigger entity and pinpoint the misconfiguration of the target ADS responsible for an accident. We further design a differential analysis to identify the responsible module to reduce search space for the misconfiguration. We study 12 categories of ADS accidents and demonstrate the effectiveness and efficiency of Rocas in narrowing down search space and pinpointing the misconfiguration. We also show detailed case studies on how the identified misconfiguration helps understand rationale behind accidents.
@inproceedings{paperbot8328,
title = {ROCAS: Root Cause Analysis of Autonomous Driving Accidents via Cyber-Physical Co-mutation},
author = {Shiwei Feng and Yapeng Ye and Qingkai Shi and Zhiyuan Cheng and Xiangzhe Xu and Siyuan Cheng and Hongjun Choi and Xiangyu Zhang},
booktitle = {ASE},
year = {2024},
doi = {10.1145/3691620.3695530}
}