LawBreaker: An Approach for Specifying Traffic Laws and Fuzzing Autonomous Vehicles
arXiv:2208.14656 · doi:10.1145/3551349.3556897
Abstract
Autonomous driving systems (ADSs) must be tested thoroughly before they can be deployed in autonomous vehicles. High-fidelity simulators allow them to be tested against diverse scenarios, including those that are difficult to recreate in real-world testing grounds. While previous approaches have shown that test cases can be generated automatically, they tend to focus on weak oracles (e.g. reaching the destination without collisions) without assessing whether the journey itself was undertaken safely and satisfied the law. In this work, we propose LawBreaker, an automated framework for testing ADSs against real-world traffic laws, which is designed to be compatible with different scenario description languages. LawBreaker provides a rich driver-oriented specification language for describing traffic laws, and a fuzzing engine that searches for different ways of violating them by maximising specification coverage. To evaluate our approach, we implemented it for Apollo+LGSVL and specified the traffic laws of China. LawBreaker was able to find 14 violations of these laws, including 173 test cases that caused accidents.
Accepted by the 37th IEEE/ACM International Conference on Automated Software Engineering (ASE 2022)
References in corpus (1)
Cited by in corpus (7)
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- HIFuzz: Human Interaction Fuzzing for small Unmanned Aerial Vehicles
- TARGET: Automated Scenario Generation from Traffic Rules for Testing Autonomous Vehicles via Validated LLM-Guided Knowledge Extraction
- FixDrive: Automatically Repairing Autonomous Vehicle Driving Behaviour for $0.08 per Violation
- Multi-modal Traffic Scenario Generation for Autonomous Driving System Testing