The vehicle traffic scheduling problem at unsignalized intersections is the research focus in the field of intelligent transportation. Because the solution space of traffic sequence increases exponentially with the increase of the number of vehicles, finding the optimal traffic sequence while ensuring the real-time performance has become a major problem for traffic scheduling at unsignalized intersections. To solve this problem, a vehicle traffic scheduling method at unsignalized intersections based on Adaptive Monte Carlo tree search algorithm is proposed, which uses a hierarchical framework, upper level centralized sequential decision-making, and lower level distributed trajectory planning. Firstly, the intersection model based on the conflict point is established, the connected vehicles are added to the queue to be searched, the Monte Carlo tree search process of the traffic sequence is designed according to the vehicle traffic characteristics in the intersection, the evaluation function is established with the total traffic time as the index, and then the adaptive exploration coefficient and other super parameters are designed for different traffic situations, so that the algorithm can maintain the best state in solving different vehicle numbers and different search periods. In the trajectory planning process, the acceleration two norm is taken as the objective function, and the speed, acceleration and the position of the starting point are taken as constraints to establish the optimal control proposition to solve the vehicle trajectory. Finally, experiments are carried out, and the results show that compared with other algorithms, the maximum optimization amplitude of this algorithm in numerical simulation and micro platform experiment is 33.42% and 38.04%, respectively, which provides an effective solution for vehicle traffic scheduling at unsignalized intersections.

[2024-01-23] Accepted Paper /doc/2023.11.0544/v1