MoDitector: Module-Directed Testing for Autonomous Driving Systems

作者：Renzhi Wang, Mingfei Cheng, Xiaofei Xie, Yuan Zhou, Lei Ma · 发表于：Proceedings of the ACM on software engineering. · 年份：2025 · DOI：10.1145/3728876 · 被引用次数：3 · 研究领域：Software Testing and Debugging Techniques、Software Reliability and Analysis Research、Adversarial Robustness in Machine Learning

Testing Autonomous Driving Systems (ADSs) is crucial for ensuring their safety, reliability, and performance. Despite numerous testing methods available that can generate diverse and challenging scenarios to uncover potential vulnerabilities, these methods often treat ADS as a black-box, primarily focusing on identifying system-level failures like collisions or near-misses without pinpointing the specific modules responsible for these failures. This lack of root causes understanding for the failures hinders effective debugging and subsequent system repair. Furthermore, current approaches often fall short in generating violations that adequately test the individual modules of an ADS from a system-level perspective, such as perception, prediction, planning, and control. To bridge this gap, we introduce MoDitector , a root-cause-aware testing method for ADS that generates safety-critical scenarios specifically designed to expose weaknesses in targeted ADS modules. Unlike existing approaches, MoDitector not only produces scenarios that lead to violations but also pinpoints the specific module responsible for each failure. Specifically, our approach introduces Module-Specific Oracles to automatically detect module-level errors and identify the root-cause module responsible for system-level violations. To effectively generate module-specific failures, we propose a module-directed testing strategy that integrates Module-Specific Feedback and Adaptive Scenario Generation to guide the...