Motion Planning With Velocity Prediction and Composite Nonlinear Feedback Tracking Control for Lane-Change Strategy of Autonomous Vehicles

The lane-change strategy of autonomous vehicles is affected by its leading vehicle. It is necessary and challenging to simultaneously conduct the lane-change maneuvers while avoiding collisions with the leading vehicle. This article proposes a lane-change strategy considering the leading vehicle by synthesizing vehicle velocity prediction, motion planning, and trajectory tracking control. A scenario-based velocity prediction method using the input-output hidden Markov model (HMM) is proposed to predict the leading vehicle velocity. Then a motion planner integrating the predicted velocity is developed to generate the optimal trajectories for the lane-change maneuvers. The generated trajectories are tracked by a trajectory tracking controller. An improved composite nonlinear feedback (CNF) control algorithm is proposed to obtain smooth transient performances and fast responses. Human driver tests on a driving simulator show the leading vehicle velocity can be predicted by the proposed method. The motion planner and the trajectory tracking controller are validated in the CarSim simulations. The collision-free optimal trajectories are generated and tracked by the motion planner and the improved CNF controller. A systematic lane-change strategy considering the leading vehicle is effectively developed in this study.