Research On Trajectory Planning And Tracking Control Of Unmanned Vehicle In Internet Of Vehicles Environment

With the continuous development of intelligent systems,driverless technology has important research significance.In the research of trajectory planning and tracking control of unmanned vehicles,the continuity and safety of trajectory planning,as well as the accuracy and efficiency of trajectory tracking,are the key issues that need to be tackled continuously in the research.In this paper,the improvement and research of trajectory planning and tracking control algorithm for unmanned vehicles is carried out based on the fifth polynomial function and model predictive control method for the actual needs of unmanned vehicles.The main work is as follows:In order to solve the workshop collision problem of unmanned vehicles in the process of lane changing,the lane changing trajectory planning based on the optimized fifth polynomial is proposed.The study of the lane change scenario is carried out in two parts,firstly,the external factors affecting the safety of vehicle lane change are studied,by introducing the influencing factors as lane change safety control parameters;secondly,the trajectory functions of the barrier-free lane change scenario and the overtaking lane change scenario are constructed,the safe lane change under different scenarios are constrained to be modeled,the rectangular extension of the vehicle under the overtaking lane change scenario is carried out,the safety range reference points are added,and the target vehicle is derived to be in close contact with the minimum safety distance between the front and rear vehicles of the current lane and the front and rear vehicles of the target lane is derived for lane change decision preparation;the safety control parameters are set to optimize the five polynomial lane change trajectory planning,and the different functions for constructing the lane change trajectory are compared using Car Sim simulation software,and the trajectories of the sinusoidal function and the optimized five polynomial are compared,and the optimized five polynomial is selected for lane change trajectory design.In order to improve the safety and real-time performance of lane change of unmanned vehicles,an improved trajectory planning algorithm based on the improved five-polynomial vehicle transverse and longitudinal lane change is proposed.By constructing different scenarios of vehicle acceleration lane change,the lane change longitudinal safety model is established to constrain the longitudinal safety of the vehicle during lane change;the tire saturation force coefficient module is added to ensure the lateral safety of the vehicle lane change;the improved five-time polynomial lane change trajectory planning model introduces the transverse longitudinal acceleration and plus acceleration constraint modules to plan twice on the basis of the constraint,and the vehicle arrives at the new transit position after The lane change time is readjusted to complete the lane change along the second planned trajectory.Simulation experiments show that the algorithm has good transverse stability and longitudinal safety,which can effectively improve the comfort and safety of passenger ride and has certain practical application value.A trajectory tracking controller based on steering linear time-varying model predictive control is designed to address the problem of poor real-time trajectory tracking control of driverless vehicles in the connected vehicle environment.The idea of safety potential field theory is introduced,and the vehicle dynamics model is established according to the environmental information and vehicle motion state;the steering linear time-varying model prediction controller is designed and optimized according to the basic principle of MPC,which improves the robustness of the system.Simulation experiments show that the method has good adaptive capability,the average computation time of controller per step is 22.0ms,and the maximum computation time of single-step controller is 35.6ms,which has significantly improved the real-time performance of trajectory tracking control and is of great significance to improve the tracking stability and driving safety of unmanned vehicles.