| The rapid development of the automobile industry not only brings great convenience to people’s lives,but also causes problems such as urban road congestion,environmental pollution,and energy consumption.Electric,intelligent and networked unmanned public transport has become the strategic direction of the current public transport development.Many cities in China have piloted driverless buses,but there is little research on unmanned stations.Realizing the orderly parking of vehicles in the domain,and improving the dispatching adaptability,and overall traffic efficiency have become the urgent problems to be solved at present.On the basis of analyzing the research status of scene construction methods and vehicle scheduling strategies at home and abroad,this paper firstly classifies the scene elements in the unmanned station.The scene was constructed based on Prescan for an unmanned station.Taking Yutong U10 intelligent bus as the research object,the vehicle model was constructed based on the Truck Sim simulation platform.Then,in view of the low utilization rate and low traffic efficiency of the current unmanned station,the differences and characteristics of the unmanned station and the traditional bus station are compared and analyzed.An improved scheduling strategy based on Dijkstra’s algorithm is proposed,and the optimal parking and charging paths for buses at unmanned stations are obtained.In order to verify the reliability of the construction platform,a real vehicle road test was carried out at the Zhengzhou No.1bus unmanned station.Based on the test process,the simulation comparison was carried out on the Truck Sim/Prescan/Simulink co-simulation platform.The results showed that the mean square of the simulation and experimental results The root value error is within 5%,which verifies the correctness of the built scene and vehicle dynamics model.Comparing the traditional scheduling method with the unmanned station scheduling strategy based on the Dijkstra algorithm proposed in this paper,the transit time of the bus system is reduced by 18.78%,which effectively improves the overall operational efficiency.This research can provide a reference for the scene modeling of unmanned station and the scheduling method of vehicles in the station. |
