Optimization Strategy of Vehicle Trajectory at Intersection in Connected and Autonomous Vehicle Environment

Abstract:

Connected and automated vehicle technologies offer new opportunities to improve the capacity of signalized intersections. This paper proposes a trajectory guidance strategy for CAVs at signalized intersections to enhance traffic efficiency and reduce travel costs. Firstly， a 0-1 integer programming model is established based on vehicle paths and potential car-following and overtaking costs， allocating the traffic flow into multiple platoons. Secondly， longitudinal and lateral trajectory control models are developed： the longitudinal model incorporates both delay and fuel consumption， while the lateral model employs a Stackelberg game theory approach to achieve cooperative vehicle platooning. Finally， the effectiveness of the proposed strategy is validated through simulations in SUMO. The results show that under a traffic volume of 350 per hour per lane， the proposed strategy reduces travel time and fuel consumption by 34.3% and 30.1%， respectively， and decreases stop time and number of stops by 38.7% and 81.9%， compared to a speed-only guidance strategy.