Model Predictive Control Of Soft Constraints For Autonomous Vehicle Major Lane-Changing Behavior With Time Variable Model

Changing lane must not only ensure the safety of the vehicle itself, but also ensure the patency of the traffic flow of the original lane and the target lane. Therefore, successful lane-changing is a key technology for autonomous vehicle control. In order to avoid collisions and ensure the smooth flow of traffic, in this paper a vehicle dynamics state model with time variable is established as plant, and the lateral force of the steering wheel is further optimized through Model Predictive Control(MPC), and then the steering wheel angle is obtained to complete the lane-changing operation. The longitudinal and lateral logic controllers designed through soft constraints can better achieve the results of successful lane-changing and unsuccessful return to the original lane, and the lane-changing characteristics within the safety corridor are analyzed in several ways. The simulation analysis of lane-changing strategy at different vehicle velocities provides helpful guidance for the design of autonomous vehicle controllers.