Safe Trajectory Optimization and Efficient-Offline Robust Model Predictive Control for Autonomous Vehicle Lane Change

Driving autonomous vehicles through diverse road conditions at various high speeds poses a significant challenge. To address this challenge, we propose a hierarchical control strategy consisting of an optimization-based trajectory planner in the first layer and an efficient-offline robust method employing path tracking in the second layer. Considering vehicle parametric uncertainties, the proposed hierarchical structure addresses multiple scenarios with varying road surface conditions and velocities. In the first layer, using the Pontryagin maximum principle (PMP) flexibly with the time-to-collision (TTC) method, the motion planner generates a safe-optimal lane-change trajectory when interacting with forward vehicles and adjacent-lane vehicles to improve ride comfort while maintaining safe distances. In the second layer, the efficient offline robust model predictive control (RMPC) with terminal constraints is applied to a linear parameter varying (LPV) system. Utilizing linear matrix inequality (LMI) techniques, the optimization problem accommodates parametric uncertainties while robustly satisfying input and output constraints. To emphasize superior performance, we have considered comparing our proposed approach with several state-of-the-art methods. Therefore, comparative simulation results have shown that our safe-optimal trajectory generation is better than the Spatio-Temporal Corridors method regarding path smoothy. The proposed approach (i.e., efficient-offline RMPC) then outperforms the offline MPC method in terms of path-tracking performance and parametric uncertainty handling while outperforming the online RMPC method in terms of computational complexity reduction. Further, all methods are verified using a co-simulation and testing platform via a high-fidelity dynamics testing vehicle control software (i.e., CarSim).