The field of autonomous vehicle technology has experienced remarkable growth. A pivotal trend in this development is the enhancement of tracking performance and stability under high-speed conditions. Model predictive control (MPC), as a prevalent motion control method, necessitates an extended prediction horizon as vehicle speed increases and will lead to heightened online computational demands. To address this, a path preview strategy is integrated into the MPC framework that temporarily freezes the vehicle state within the prediction horizon. This approach assumes that the vehicle state will remain consistent for a specified preview distance and duration, effectively extending the prediction horizon for the MPC controller. In addition, a stability controller is designed to maintain handling stability under high-speed conditions, in which a square-root cubature Kalman filter (SRCKF) estimator is employed to predict tire forces to facilitate the cornering stiffness estimation of vehicle tires. The double lane change maneuver under high-speed conditions is conducted through the Carsim/Simulink co-simulation. The outcomes demonstrate that the SRCKF estimator could provide a reasonably accurate estimation of lateral tire forces throughout the whole traveling process and facilitates the stability controller to guarantee the handling stability. On the premise of ensuring handling stability, integrating the preview strategy could nearly double the prediction horizon for MPC, resulting in the limited increase of online computation burden brought while maintaining path tracking accuracy.

In the realm of autonomous driving, motion control generally involves two critical aspects—path following and stability control—and an inevitable mutual interference exists between them under extreme conditions. To tackle this challenge, this study proposes a collaborative approach of path tracking and stability control. When designing the path tracking control module, the effects of vertical tire load transfer and road surface adhesion coefficients on tire force calculations were taken into account to mitigate vehicle dynamics model mismatch. Leveraging the receding horizon optimization characteristic of nonlinear model predictive control (NMPC), a cascaded discretization approach was utilized to realize a balance between precision and real-time performance in numerical solutions. Then, a stability controller, which employs rear wheel steering, was designed to prevent excessive increases in the vehicle’s sideslip angle, thereby ensuring the vehicle’s lateral stability. The effectiveness of the proposed strategy is validated through CarSim 8.0/Simulink cosimulation. The outcomes demonstrate that the stability controller significantly enhances vehicle stability under high-speed and low-adhesion conditions. On the premise of stability, the proposed path tracking controller has exhibited significant enhancements in real-time performance, without compromising the accuracy of path tracking.

Motion control is one of the three core modules of autonomous driving, and nonlinear model predictive control (NMPC) has recently attracted widespread attention in the field of motion control. Vehicle dynamics equations, as a widely used model, have a significant impact on the solution efficiency of NMPC due to their stiffness. This paper first theoretically analyzes the limitations on the discretized time step caused by the stiffness of the vehicle dynamics model equations when using existing common numerical methods to solve NMPC, thereby revealing the reasons for the low computational efficiency of NMPC. Then, an A-stable controller based on the finite element orthogonal collocation method is proposed, which greatly expands the stable domain range of the numerical solution process of NMPC, thus achieving the purpose of relaxing the discretized time step restrictions and improving the real-time performance of NMPC. Finally, through CarSim 8.0/Simulink 2021a co-simulation, it is verified that the vehicle dynamics model equations are with great stiffness when the vehicle speed is low, and the proposed controller can enhance the real-time performance of NMPC. As the vehicle speed increases, the stiffness of the vehicle dynamics model equation decreases. In addition to the superior capability in addressing the integration stability issues arising from the stiffness nature of the vehicle dynamics equations, the proposed NMPC controller also demonstrates higher accuracy across a broad range of vehicle speeds.

Path tracking for autonomous vehicles may encounter vehicle stability problem under extreme conditions. To tackle this challenge, this paper proposes a control strategy considering the path tracking and handling stability comprehensively. An NMPC controller is designed for path tracking, in which the tire vertical load transfer is taken into consideration to reduce model mismatch. Orthogonal collocation discretization method is adopted to enhance the solving of NMPC problem. In the stability controller, active rear-wheel steering is applied to avoid the excessive increase of sideslip angle of vehicle and ensure handling stability. The proposed controller is verified in a high-speed and low-adhesion road scene by CarSim/Simulink co-simulation. The results demonstrate the effectiveness of the proposed controller in enhancing path tracking performance and maintaining handling stability even under extreme conditions.