MoDitector: Module-Directed Testing for Autonomous Driving Systems
Why this work is in the frame
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Bibliographic record
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.
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Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.000 | 0.031 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.003 | 0.001 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.000 | 0.000 |
Machine scores (provisional)
The two teacher heads of the student model, read on this work. A score orders the frame for review; it never asserts a category, and the validation status ships verbatim with every row.
Baseline scores from an immature model (maturity gate not passed, 7 training rounds). Scores rank; they never assert a category.
score_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from it