Preemptive-Level-Based Cooperative Autonomous Vehicle Trajectory Optimization for Unsignalized Intersection with Mixed Traffic

Buses constitute a crucial component of public transportation systems in numerous urban centers. Integrating autonomous driving technology into the bus transportation ecosystem has the potential to enhance overall urban mobility. The management of mixed traffic at intersections, involving both private vehicles and buses, particularly in the presence of bus lanes, presents several formidable challenges. This study proposes a preemptive-level-based cooperative autonomous vehicle (AV) trajectory optimization for intersections with mixed traffic. It takes into account dynamic changes in the intersection’s passing sequence, trajectory selection, and adherence to traffic regulations, including the different status of bus lanes. Based on the spatio–temporal coupling constraints of each vehicle trajectory at intersections, a preemptive-level-based AV passing order optimization method is proposed. Subsequently, a speed control mechanism is introduced to decouple these constraints, thereby preventing vehicle conflicts and reducing unnecessary braking. Ultimately, trajectory routes for multi-exit roads are selected, prioritizing traffic efficiency. In simulated validations, two representative types of intersections from the actual road network were selected, and eight typical scenarios established, including the operation status of bus lanes and different percentages of buses. The results indicate that the proposed method improves intersection traffic efficiency by a minimum of 12.55%, accompanied significantly by reduction of fuel consumption by 8.93%. This study verified that the proposed method significantly enhances intersection efficiency and reduces energy consumption while ensuring safety.