Path Planning For Autonomous Obstacle Avoidance Of Intelligent Vehicles Based On Reinforcement Learning

The development of automobile industry has brought great convenience to people’s life.However,in daily life,traffic accidents caused by driver factors are also common.With the development of computer technology,the intelligent driving technology of automobile has attracted extensive attention.As a key part of intelligent driving technology,path planning determines the reliability and safety of intelligent driving system.In the face of simple driving scenes,the vehicle should make reasonable path planning,which is the most basic requirement for the intelligent system.However,in the complex and changeable traffic environment,the intelligent vehicle should also make corresponding operations to avoid danger.It is necessary to ensure the driving safety and driving stability of the vehicle,which brings higher requirements to the path planning.Bad driving conditions will also bring challenges to the safety of path planning.For example,vehicles are prone to sideslip in the ice and snow environment.Therefore,how to plan a safe driveable path in the ice and snow environment is another research difficulty.In view of the difficulties in the above path planning technology,this paper studies the path planning of autonomous obstacle avoidance of Intelligent Vehicles:Aiming at the problem of safety and stability of path planning,a lane changing overtaking path planning method based on Q-Learning is proposed.Selecting appropriate parameters to define the vehicle state and action simplifies the planning problem,reduces the number of vehicle states and improves the efficiency of the algorithm;A reward function considering vehicle safety,stability and path smoothness is designed to achieve the driving goal of vehicle lane changing and overtaking.The simulation results show that although there are some deviations in the implementation process due to the poor smoothness of the path,the driving safety is guaranteed within a certain error range.Aiming at the problem that the poor smoothness of the planned path leads to the deviation of vehicle execution,a deep Q-learning path planning method considering vehicle dynamics is proposed.Due to the complexity of vehicle dynamics and the increase of vehicle state parameters,the use of Q-learning algorithm will lead to the problem of dimensional disaster.Therefore,the deep Q-learning algorithm based on neural network is adopted to improve the problem of limited q-table storage in Q-learning algorithm;Considering the relatively complex road environment,using the method of artificial potential field,a virtual force field description of road environment including Lane centerline,obstacle vehicles and driving destination is established,and a similar state judgment mechanism is introduced,so that vehicles can make corresponding symmetrical actions in the face of untrained symmetrical States,so as to adapt to more traffic scenes.The simulation results show that this method increases the smoothness of the planned path,reduces the peak value of lateral parameters during driving,improves the lateral stability of the vehicle,reduces the training time of deep Q-learning and reduces the learning cost.Aiming at the problem that the road adhesion coefficient is low under the condition of ice and snow,which leads to the phenomenon of vehicle sideslip,this paper proposes a path planning method considering the sideslip of surrounding obstacle vehicles.The influence of ice and snow environment on vehicle driving safety is analyzed.Considering that the danger degree of vehicle sideslip is higher,the path planning algorithm proposed above is improved,the action range of obstacle vehicle dynamic rectangular virtual repulsion field is expanded,and the repulsion of repulsion field is enhanced,so as to achieve the goal of avoiding dangerous vehicles and collision in time.The simulation results show that whether the vehicle is driving normally on the lane line or changing lanes to avoid obstacles,the vehicle can plan a safe path in time to avoid collision accidents.