Enhancing legal driving for autonomous vehicles through law-compliance potential fields

With the advent of the mixed traffic era, ensuring autonomous vehicles' compliance with traffic laws alongside human drivers has become increasingly critical. Existing decision-making methods predominantly emphasize safety, inadequately addressing systematic compliance with traffic laws, leading to potential legal violations in complex driving scenarios. To bridge this gap, this paper proposes a comprehensive Law-Compliance Potential Fields-based method. Traffic law constraints are systematically categorized into four potential fields, which explicitly encode vehicle states, static and dynamic elements, and compliance thresholds. A novel fusion strategy is further designed to effectively resolve field-overlap distortions. Finally, the constructed law-compliance potential fields are integrated into a model predictive control-based decision-making framework, and five representative scenarios are designed for experimental validation, including a critical scenario of safety-compliance conflict. The evaluated results of scenario tests demonstrate that the proposed method markedly enhances autonomous vehicles' compliance capabilities, effectively balancing safety considerations even under challenging driving conditions.