考虑行人过街安全的交叉口车路协同控制方法OACSCD

Addressing frequent jaywalking,pedestrian-vehicle conflict risk,and heavy congestion at signalized inter-sections under mixed traffic,this study develops a cooperative control framework for traffic signals,connected au-tonomous vehicle(CAV),and pedestrians.In Stage 1,a protect/prohibit right-turning(PPRT)strategy is integrated into a pedestrian-oriented deep reinforcement learning(DRL)controller.The state encodes spatial-temporal condi-tions using matrices of vehicle and pedestrian positions and speeds.Actions split phases into pedestrian-through and vehicle left/right turns to achieve temporal-spatial separation of key conflicts.The reward is based on the difference in cumulative waiting time with passenger-load weighting to reflect social efficiency,and the optimal policy is learned with a dueling double deep Q-network.In Stage 2,coordinated speed planning for pedestrians and CAV is designed to further reduce interactions and delay.Pedestrian speeds are bounded by feasible ranges derived from crossing distance and remaining green time with acceleration and speed constraints and with compliance and sto-chastic perturbations considered.CAV adjust to safety-feasible speeds when high-risk situations are detected and re-ceive speed guidance for left-turn and through movements to pass the intersection smoothly.Using a Changsha inter-section and local traffic data,an intelligent connected intersection and mixed-traffic scenario and implement simula-tions are built in SUMO.Results show that the proposed method yields 897 pedestrian-vehicle conflicts and 272 jay-walking events at 50%CAV penetration,reductions of 43.37%and 53.7%compared with PPRT.Average per-capita delay is 11.61 s,which is 39.15%,55.03%,and 13.62%lower than actuated control,PPRT,and DRL-based signal control,and the number of stops decreases to 3279.Performance improves with higher CAV penetration,with con-flicts reduced by 16.60%from 0%to 25%,and overall metrics reaching the best level at 100%.