| Autonomous driving has a great impact on traffic efficiency and safety,which can give a radical change to the form of transportation in human society.In recent years,autonomous vehicles have attracted extensive attention of the public and researchers around the world.Motion planning system,as a module to determine the way of vehicle motion,determines the safety,comfort and efficiency of autonomous vehicles.However,in complex environment like urban environment,autonomous vehicles face many uncertainties.Many researchers have studied some of them as a separate problem,but how to express the uncertainty in the environment and how to deal with it is still a problem.In this dissertation,the complex environment of autonomous driving is defined as uncertainty problem,and summarized the uncertainty categories of motion planning module according to the hierarchical framework.A model based on Partially Observable Markov Decision Process is constructed to represent the uncertainty faced by motion planning,and a rule-based method is used to determine the action of surrounding traffic participants.Then,deep reinforcement learning is used in motion planning system,and a motion planning method with model checker is proposed.The influence of uncertainty on the motion planning process is analyzed and the effectiveness of the motion planning method is verified through the experiments of motion planning in different conditions in the simulation environment.The specific research contents of this dissertation are summarized as follows:Analyzing the problems faced by the motion planning module in complex environment and summarizing them into four problems: uncertainty of human intention,uncertainty of surrounding vehicles’ path,high traffic density and occlusion.A basic framework of motion planning system with uncertainty is established and a model based on Partially Observable Markov Decision Process is constructed to represent the basic driving environment and motion planning process with uncertainty.Compared with the existing autopilot simulator,the environment constructed in this dissertation can represent a variety of environmental uncertainties.By using the same motion planning method under different conditions,the influence of uncertain environment on motion planning is analyzed.The experiments show that occlusion and intention uncertainty will affect the safety of motion planning,and path uncertainty and high traffic density will affect the traffic efficiency.Deep reinforcement learning is introduced into the motion planning system,and combined with the rule-based motion planning method,which is used as the model detection to improve the learning efficiency of deep reinforcement learning and the security of the planning results.The experiments show that the reinforcement learning can reduce the impact of uncertainty,and the model checker can improve the safety and efficiency of motion planning in uncertain environment. |
