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Hierarchical Fault Localization for Autonomous Driving Systems with Hypothesis Validation and Intent Analysis
Rui Zheng, Changwen Li, Yi Ji +1
Comprehensive testing is essential for the safety and reliability of Autonomous Driving Systems (ADS). Existing techniques can detect system-level failures or attribute them to coa…
Testing Autonomous Driving Systems -- What Really Matters and What Doesn't
Changwen Li, Joseph Sifakis, Rongjie Yan +1
Despite extensive research, the testing of autonomous driving systems (ADS) landscape remains fragmented, and there is currently no basis for an informed technical assessment of th…
A Comprehensive Evaluation of Four End-to-End AI Autopilots Using CCTest and the Carla Leaderboard
Changwen Li, Joseph Sifakis, Rongjie Yan +1
End-to-end AI autopilots for autonomous driving systems have emerged as a promising alternative to traditional modular autopilots, offering the potential to reduce development cost…
Rigorous Simulation-based Testing for Autonomous Driving Systems -- Targeting the Achilles' Heel of Four Open Autopilots
Changwen Li, Joseph Sifakis, Rongjie Yan +1
Simulation-based testing remains the main approach for validating Autonomous Driving Systems. We propose a rigorous test method based on breaking down scenarios into simple ones, t…
Simulation-based Validation for Autonomous Driving Systems
Changwen Li, Joseph Sifakis, Qiang Wang +2
Simulation is essential to validate autonomous driving systems. However, a simple simulation, even for an extremely high number of simulated miles or hours, is not sufficient. We n…