Research and Analysis on Motion Planning Method of Intelligent Network Connected Vehicles

Abstract

Article Research and Analysis on Motion Planning Method of Intelligent Connected Vehicles Heng Wang 1,2, Xianyi Yang 1,2, Zhengbai Liu 3, and Zhenfeng Wang 1,2, * 1 College of Mechanical and Electrical Engineering, Henan Agricultural University, Zhengzhou 450002, China 2 Henan Provincial Cold Chain Information and Equipment Laboratory for Logistics of Agricultural Products, Zhengzhou 450002, China 3 School of Innovation and Entrepreneurship, Southern University of Science and Technology, Shenzhen 518055, China * Correspondence: zfking@henau.edu.cn Received: 23 September 2022 Accepted: 21 November 2022 Published: 18 December 2022 Abstract: Combined with the tire model, a two-degree-of-freedom model is proposed in this study. At the same time, a turning control model is analyzed, which lays the foundation for the subsequent local path planning, that is, the simulation control of lane changing and obstacle avoidance. The co-simulation of CarSim and Simulink is used to realize the tracking control of trajectories, and the given reference trajectory can be tracked with high precision in the serpentine rod penetration test, so as to verify the stability of the control algorithm control. A set of optimal trajectories of straight lane change and curved lane change are selected as the reference trajectories of the controller, and the target speed of the lane change is given in the CarSim speed control module. The simulation results show that the motion planning error is extremely small that completes the high-precision automatic control of steering, and this helps realize the tracking of a given trajectory, reflecting the good trackability of the planned lane-changing trajectory from the side. Aiming at the automatic lane changing process of the intelligent connected vehicle on different straight roads and curved roads, the V2V method is used to obtain the status information of the surrounding traffic vehicles where the multi-objective optimization method is used to determine the optimal trajectory. Driving with reference to the trajectory realizes the coordination and unity of planning and control.