Traffic rule-embedded decision-making for automated vehicles in pedestrian-vehicle conflicts via deep reinforcement...

In pedestrian-vehicle crossing conflicts, automated vehicles (AVs) should avoid collisions and follow pedestrian-related traffic rules and social expectations. However, many existing decision-making models mainly optimize safety or efficiency. The behavioral effects of specific pedestrian-related rules remain insufficiently examined. Thus, actions that appear safe under surrogate metrics may still violate normative expectations in dynamic traffic. This study proposes a Traffic Rule-Embedded Decision-Making framework (TRE-DM) for ego-vehicle control in pedestrian-vehicle conflicts. 1) Pedestrian-related provisions from traffic laws and standards are decomposed into yielding, slowing, and braking. 2) These rules are formalized using MTL-informed triggering conditions and embedded into reinforcement learning through soft rule-shaped rewards. 3) The framework is evaluated using pedestrian-crossing conflicts reconstructed from the Shanghai Naturalistic Driving Study. Perturbation-based stress tests and external evaluations on three public datasets are also conducted. Results show that TRE-DM achieves balanced performance in safety, compliance-related behavior, comfort, and efficiency. Compared with human-driver trajectories, it reduces TIT (time-integrated TTC) by 37.5% and decreases yielding violations from 102 to 0. Compared with the agent using only collision-avoidance objectives, it reduces TIT by 32.2% and decreases collision events from 7 to 0. Ablation results further show that yielding is essential for reducing safety-critical failures, slowing supports earlier risk anticipation, and braking improves control smoothness. Trajectory reconstruction further shows earlier deceleration and safer lateral clearance under rule guidance, suggesting more rule-consistent and risk-aware evasive behavior. Overall, this study provides an interpretable rule-modeling and rule-embedding framework for improving AV behavior in pedestrian-vehicle conflicts.