Towards Lawful Autonomous Driving: Deriving Scenario-Aware Driving Requirements from Traffic Laws and Regulations

2026-04-27

AI Summary

This study addresses the challenge of ensuring precise compliance with traffic regulations by autonomous driving systems in complex real-world scenarios, where traditional formal methods suffer from high manual effort and poor scalability. The authors propose a novel approach grounded in a structured traffic scene ontology, incorporating a node-anchor mechanism to explicitly align the reasoning process of large language models with hierarchical scene semantics. This enables automatic extraction of context-aware regulatory requirements directly from legal texts. The method integrates an onboard real-time compliance monitoring and navigation module, demonstrating significant improvements on the OnSite dataset (5,897 scenes): a 29.1% increase in legal-scene alignment accuracy, along with 36.9% and 38.2% gains in precision for extracting mandatory and prohibitive compliance rules, respectively. The system has been successfully deployed in a real vehicle platform.

Technology Category

• Natural Language Processing: Safety and Robustness
• Planning, Routing, and Scheduling: Planning with Language Models
• Application Domains: Mobility, Driving & Flight

Application Category

• Systems and Infrastructure for Web, Mobile and WoT: Applied ML and AI for Web-based mobile applications
• Responsible Web: Machine-in-the-loop, human agency and autonomy
• Semantics and Knowledge: Data modeling to support human-machine intelligence, including LLMs agents, intelligent system behavior, explanations, and user-friendly interactions

Abstract

Driving in compliance with traffic laws and regulations is a basic requirement for human drivers, yet autonomous vehicles (AVs) can violate these requirements in diverse real-world scenarios. To encode law compliance into AV systems, conventional approaches use formal logic languages to explicitly specify behavioral constraints, but this process is labor-intensive, hard to scale, and costly to maintain. With recent advances in artificial intelligence, it is promising to leverage large language models (LLMs) to derive legal requirements from traffic laws and regulations. However, without explicitly grounding and reasoning in structured traffic scenarios, LLMs often retrieve irrelevant provisions or miss applicable ones, yielding imprecise requirements. To address this, we propose a novel pipeline that grounds LLM reasoning in a traffic scenario taxonomy through node-wise anchors that encode hierarchical semantics. On Chinese traffic laws and OnSite dataset (5,897 scenarios), our method improves law-scenario matching by 29.1\% and increases the accuracy of derived mandatory and prohibitive requirements by 36.9\% and 38.2\%, respectively. We further demonstrate real-world applicability by constructing a law-compliance layer for AV navigation and developing an onboard, real-time compliance monitor for in-field testing, providing a solid foundation for future AV development, deployment, and regulatory oversight.

Problem

• autonomous driving
• traffic laws
• law compliance
• scenario-aware requirements
• legal reasoning

Innovation

• large language models
• traffic law compliance
• scenario-aware reasoning
• autonomous driving
• structured scenario taxonomy