Collaborative Traffic Control Strategy at Intersections for Autonomous Vehicle Based on Preemptive Level

With the promotion and application of technologies such as the Internet of Things, big data, and artificial intelligence in the transportation field, intelligent vehicle road collaborative systems have become an important means to improve the efficiency of transportation systems. This study aims to leverage the characteristics of autonomous vehicles and propose a collaborative traffic control strategy for autonomous vehicle intersections based on preemptive level, in order to provide reference for the improvement of safety and efficiency in future intersection operations. According to the intersection environment information, generate the track routes of automatic driving vehicles at different exit lanes to pass through the intersection, study the coupling space-time constraints of different track routes at the intersection, judge whether there is conflict according to the minimum safe headway of vehicle traffic, and obtain the conflicting point sequence; Calculate the preemptive level based on the time it takes for autonomous vehicles to reach each conflict point on each trajectory route, determine the vehicle traffic order based on the preemptive level, and resolve conflicts one by one for the conflict points that exist. Finally, select trajectory routes facing multiple exits based on traffic efficiency; Using Sumo and Python to build intersection scenarios for simulation, under high and low input flow conditions, the simulation results of collaborative traffic control for autonomous vehicle intersections based on preemptive level are significantly better than those without control strategies in the seven evaluation indicators determined in the dimensions of safety, green, and high efficiency. The average travel time has increased by 71.52% and 63.66%, the average standard deviation of speed has decreased by 60.84% and 43.37%, and the average fuel consumption decreased by 56.20% and 37.16% respectively, verifying the effectiveness of the motion optimization strategy.