LQR control of intelligent vehicles via deep reinforcement learning with a human–vehicle motion sickness comfort coupled model

To address the motion sickness comfort issues of occupants caused by the path-tracking accuracy and stability control of autonomous vehicles under varying steering conditions, a multi-objective control strategy is proposed based on a deep reinforcement learning approach for adaptive parameter tuning of a linear quadratic regulator (LQR) controller. First, an integrated vehicle dynamics model and a tracking error model with additional yaw moment were established. On this basis, a human–vehicle coupled motion sickness comfort model was developed by combining the six-degree-of-freedom subjective vertical conflict (6DOF-SVC) model with the Toyota finite element human body model. Second, an LQR controller was designed based on reinforcement learning. With the integration of a deep Q-network (DQN) agent, a reward function considering occupant motion sickness comfort, stability, and path-tracking accuracy was constructed to guide the agent in achieving trade-offs among multiple performance objectives. Finally, joint simulations using CarSim and Simulink were conducted under two varying operating conditions, namely, variable road curvature and variable road adhesion coefficients. The simulation results demonstrate that, under two variable operating conditions, the proposed strategy achieves notable improvements compared with the LQR parameter tuning control strategies based on genetic algorithms and fuzzy algorithms. In terms of tracking accuracy, the maximum lateral deviation is reduced by 39%, 31%, and 41%, 19%, while the average lateral deviation decreases by 20%, 18%, and 18%, 10%, respectively. Regarding lateral stability, the maximum sideslip angle is reduced by 27%, 16%, and 28%, 13%, whereas the maximum front wheel steering angle decreases by 34%, 27%, and 47%, 26%, respectively. In terms of ride comfort, the incidence rate of motion sickness among passengers is reduced by 34%, 19%, and 33%, 20%, respectively. Finally, the real-vehicle experiments further validate the effectiveness of the proposed deep reinforcement learning–based LQR controller parameter tuning strategy.