You Take the High Road, and I’ll Take the Low Road

Autonomous vehicles (AVs) must comply with regulatory frameworks to ensure road safety and predictability. Current proposed AV systems predominantly rely on machine learning models that lack explicit, computable representations of traffic laws, raising concerns about accountability and robustness in complex scenarios. This study proposes a novel pipeline that embeds formal logic rules in the autonomous agent to ensure legal compliance. To address the knowledge acquisition bottleneck, we propose using Large Language Models (LLMs), robust prompt engineering, and Logical English (LE) to translate traffic rules from natural language into a human-readable, executable rule-based framework. The pipeline includes an error correction phase to refine the process of extracting legal rules, which are then integrated into a simulation environment. Our approach successfully performed the translation of legal text into a structured, computable format, improving the transparency and interpretability of the high level decision making. The error correction phase improves rule accuracy, while simulations further validate rule compliance and performance in dynamic traffic scenarios.