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SimADFuzz: Simulation-Feedback Fuzz Testing for Autonomous Driving Systems
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AI
DOI：10.1145/3744242.png)
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Abstract
En 中文
Autonomous driving systems (ADSs) have achieved remarkable progress in recent years. However, ensuring their safety and reliability remains a critical challenge due to the complexity and uncertainty of driving scenarios. In this article, we focus on simulation testing for ADS, where generating diverse and effective testing scenarios is a central task. Existing fuzz testing methods face limitations, such as overlooking the temporal and spatial dynamics of scenarios and failing to leverage simulation feedback (e.g., speed, acceleration and heading) to guide scenario selection and mutation. To address these issues, we propose SiMADFuzz, a novel framework designed to generate high-quality scenarios that reveal violations in ADS behavior. Specifically, SiMADFuzz employs violation prediction models, which evaluate the likelihood of ADS violations, to optimize scenario selection. Moreover, SiMADFuzz proposes distance-guided mutation strategies to enhance interactions among vehicles in offspring scenarios, thereby triggering more edge-case behaviors of vehicles. Comprehensive experiments demonstrate that SimADFuzz outperforms state-of-the-art fuzzers by identifying 73 more unique violations, including 5 reproducible cases of vehicle-vehicle, vehicle-pedestrian, and vehicle-roadside collisions. These results demonstrate SiMADFuzz's effectiveness in enhancing the robustness and safety of ADSs.
Keywords:
Autonomous Driving Systems
Fuzz Testing
Simulation-based Testing
Journal
A
IF:
6.2
Papers:
1.2K
Citations:
3.4K