Publication Type

Conference Proceeding Article

Version

publishedVersion

Publication Date

6-2025

Abstract

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 testing process. We evaluated MoDitector across four critical ADS modules and four representative testing scenarios. The results demonstrate that MoDitector can effectively and efficiently generate scenarios in which failures can be attributed to specific targeted modules. In total, MoDitector generated 216.7 expected scenarios, significantly outperforming the best baseline, which identified only 79.0 scenarios. Our approach represents a significant innovation in ADS testing by focusing on the identification and rectification of module-specific errors within the system, moving beyond conventional black-box failure detection.

Keywords

Module-Specific Failure, Autonomous Driving System Testing

Discipline

Software Engineering

Research Areas

Intelligent Systems and Optimization

Areas of Excellence

Digital transformation

Publication

Proceedings of the 34th ACM SIGSOFT International Symposium on Software Testing and Analysis, Trondheim, Norway, 2025 June 25-28

First Page

137

Last Page

158

Identifier

10.1145/3728876

Publisher

ACM

City or Country

New York

Additional URL

https://doi.org/10.1145/3728876

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