Research on Real-time Obstacle Avoidance by Deceleration and Lane-changing Trajectory Planning and Tracking Strategy Based on Dual Algorithm Control

Aiming at the problem of poor robustness after the combination of lateral kinematics control and lateral dynamics control when an autonomous vehicle decelerates and changes lanes at a certain distance. This paper proposes a trajectory determination and tracking control method based on a PI-MPC dual algorithm controller. In order to describe the longitudinal deceleration that satisfies the lateral acceleration limit during a certain distance of lane change, firstly, a fifth-order polynomial and a uniform deceleration motion formula are established to express the lateral and longitudinal displacements, and a model prediction (MPC) controller is used to output the front wheel rotation angle. Through the dynamic formula and the speed proportional integral (PI) controller to control and adjust the brake pressure. Based on Simulink and Carsim co-simulation to optimize the best lane change completion time at different longitudinal lane change speeds, the relationship between the vehicle collision avoidance stable lane change time and the real-time vehicle speed and deceleration is obtained, so as to avoid the vehicle collision avoidance and deceleration change Unstable performance such as rollover occurred during the road. Finally, the simulation verification of the deceleration and lane changing conditions at a certain initial vehicle speed shows that the maximum lateral acceleration is 3.03m/s2, and the error from the maximum allowable acceleration is 1%. Under the premise of ensuring lateral stability, The accuracy of the horizontal and vertical lane change is 99.67% and 99.95%, respectively, which verifies the effectiveness of the algorithm. Therefore, in the study of trajectory planning and tracking control of autonomous vehicles, the controller can improve the control robustness of decelerating and changing lanes.