Research On Local Path Planning And Control Algorithm Of Autonomous Vehicles In The Park

In the traditional "people-vehicle-road" transportation system,people are the weakest link and the main cause of traffic accidents.In the autonomous driving environment,because some or all of the driver links have been replaced,autonomous driving technology is considered to be a great solution to traffic safety issues.Among the application scenarios of autonomous driving technology,the park scene has attracted the attention of many enterprises and universities due to its low speed and simple working conditions.It is one of the first scenarios that autonomous driving can be implemented.The autonomous driving in the park has become a research hotspot in recent years.Relying on the key special project "Research and Demonstration Operation of Electric Autonomous Vehicles" by the Ministry of Science and Technology,this paper studies local planning and tracking control technology in the park scene,and performs algorithm verification based on simulation and real vehicle platforms.Aiming at the local planning problem,this paper first introduces the Frenet framework to decompose the local planning problem into two one-dimensional problems,which reduces the planning difficulty.Then considering the dynamic changes of the driving environment of the autonomous vehicle,this article analyzes the driving risk of the traffic environment,and models the driving environment based on the potential field method.By introducing adaptive adjustment factors,the risk influence range of dynamic obstacles can be adjusted automatically with the change of relative speed.Finally,the fifth-order polynomial curve is used to generate candidate trajectories.In this paper,the target states in the state space are reasonably sampled,and the lateral and longitudinal trajectory clusters are separately constructed and combined as a candidate set.This paper designs a multi-objective evaluation function that considers safety,comfort,and efficiency to evaluate candidate trajectories,select the optimal trajectory in this cycle.Repeat the above steps at the next moment,so as to realize the dynamic update of local planning.Aiming at the trajectory tracking problem,this paper adopts decoupling control strategies to build the vehicle lateral and longitudinal controllers respectively.Firstly,a vehicle lateral controller was built based on the model predictive control theory.The vehicle lateral dynamics model was used as a predictive model,and the constraints on control and output were designed.The MPC optimization problem was established and solved by transforming it into a quadratic programming problem.This method takes into account the accuracy and comfort of tracking.For longitudinal control,this paper designs a higher-level controller based on the optimal preview theory to achieve the desired acceleration decision.A lower-level controller is established through the vehicle’s longitudinal inverse dynamics model to achieve the conversion from acceleration to vehicle input,thereby achieving hierarchical longitudinal control Strategy.Finally,this paper validates the planning and control algorithm based on simulation and real vehicle platforms.First,a Prescan & Ros joint simulation platform based on multi-machine communication is built,and typical experimental conditions are designed to verify the function of the planning and control algorithm.Then build a real vehicle test platform based on the Haval H7 experimental vehicle,use the industrial computer as the automatic driving controller,and use RTK-GPS / IMU integrated navigation equipment to obtain the high-precision positioning and motion status information of the vehicle.The vehicle are controlled through CAN bus.The planning and control algorithm is verified in the campus.The simulation and real vehicle experimental results verify the effectiveness and accuracy of the algorithm in this paper.