MG-Fuzz: Model-Guided Fuzzing for Unsafe Scenario Discovery in Autonomous Driving Systems

Abstract

As autonomous driving systems (ADS) are increasingly deployed in real-world environments, discovering diverse unsafe driving scenarios remains a fundamental yet difficult problem. Existing scenario generation and testing approaches often rely on black-box exploration or externally-observed heuristic feedback, which struggle to effectively guide the search toward high-risk scenarios induced by complex decision-making behaviors. A key difficulty stems from the fact that unsafe behaviors in ADS often arise from internal decision-making logic, which can induce structured and discontinuous responses that are hard to effectively explore using purely black-box guidance. Consequently, current tools tend to repeatedly discover a narrow set of similar unsafe scenario types, limiting their ability to expose diverse and previously unseen failure modes. In this paper, we propose MG-Fuzz, a model-guided, multi-objective fuzzing framework for unsafe scenario discovery in autonomous driving systems. Our approach extracts an automaton model that captures the core control logic of the ADS decision-making component, and leverages this model as structured guidance for search-based scenario exploration. To systematically drive the exploration process, MG-Fuzz integrates model-based metrics derived from the automaton with complementary safety metrics, enabling effective evaluation and prioritization of generated driving scenarios across diverse unsafe behavior types. MG-Fuzz has been developed and thoroughly evaluated through extensive experiments on autonomous driving systems. Experimental evidence indicates that MG-Fuzz successfully detects 18 distinct types of unsafe driving scenarios, marking a substantial improvement in detection breadth relative to current state-of-the-art tools.