Research On Path Planning Technology Of Autonomous Vehicle

With the development of computers,machinery,and other technologies,autonomous vehicles(AV)with artificial intelligence are gradually widely used in some fields of transportation,industrial engineering.AV can automatically find an optimal path to the destination while avoiding obstacles,which can help to reduce transportation costs,improve driving efficiency,customer satisfaction,and reduce traffic accidents.The study of AVs is of great significance to promote the development of intelligent transportation.Based on the review of the development and some key technologies of autonomous vehicles,we studied two typical AVS path planning methods.On the one hand,we improve the artificial potential field method by considering some existing defects,such as local minimum,target unreachable,etc.Firstly,we introduced the anti-collision distance to compress the motion space of autonomous vehicles and prevent collisions with obstacles.Secondly,a partitioning strategy is proposed to solve the problem of target unreachable.Finally,we introduced the concept of the early warning range and set the virtual target point at the appropriate position to guide the AV around the local minimum area.The simulation results show that the improved algorithm can effectively solve the problems of the traditional method,such as target unreachable,local minimum,and narrow channel.AV can reach the destination while avoiding obstacles,which can apply to some scenarios,such as logistics distribution,freight yard vehicles.On the other hand,an improved ant colony algorithm is proposed in vehicular ad hoc network(VANET).Information is collected with the help of roadside stations through the vehicle to infrastructure(V2I),weight factors are introduced according to the congestion of roads,and pheromones and heuristic functions are updated at the same time,to find an optimal path with the least delay.The simulation results show that the improved ant colony algorithm proposed in this thesis can improve the global search ability and convergence speed,avoid the old information of pheromones on the path,and realize the global path planning of urban road traffic.