Abstract: In order to solve the problem that two-way lanes are limited by road conditions and traffic characteristics, a reverse overtaking control strategy based on graph search and model predictive control (MPC) is proposed. The strategy obtains global information such as speed and acceleration of the environment vehicles with the help of telematics and in-vehicle sensors, and incorporate the impact of each entity in the multi-vehicle scenario into the overtaking decision. Firstly, based on the global information obtained from vehicle-vehicle communication, combined with a non-cooperative game, each vehicle is predicted within the action of the entire time period, and each area of the road is evaluated for safety based on the prediction, and the evaluation is based on the probability of a vehicle appearing in that area at the next moment. After completing the assessment of the road, the collision probability hot zone map is obtained, and then the safe path is searched by the A* algorithm, and the trajectory planning of the main vehicle is completed according to the safe path. After that, the model prediction controller is designed to control the main vehicle in real time so that the vehicle follows the established trajectory. Finally, the proposed algorithm is verified by building a joint simulation platform with the help of Carsim and MATLAB/Simulink. The simulation test results show that the maximum control error of the model does not exceed 0.15 m, and the average error rate is about 1.7%, which can realize the accurate control of the vehicle and ensure the controlled vehicle to complete the reverse overtaking safely.

Key words: reverse overtaking, directed graph, A* algorithm, Vehicle-vehicle communication, game theory, model predictive control