Facilitating Logical Flaw Detection for Autonomous Driving Systems Through LLM-Empowered Oracle Generation

Autonomous driving systems (ADS, self-driving car software) often have logical flaws (errors in decision-making rather than traditional bugs) that cause safety accidents and traffic violations, but there are no reliable automated tools to detect them. This paper introduces LawSentry, a framework that uses large language models (LLMs, AI systems trained to understand and generate text) to automatically convert traffic laws into executable test cases called violation oracles that can detect when an ADS behaves illegally, achieving 95.5% accuracy at identifying traffic violations across simulation platforms.