This article proposes an adaptive strong tracking Kalman filter (STKF) to enhance the estimation performance of the tire–road friction coefficient (TRFC), a critical factor in vehicle control and safety. First, a vehicle dynamics model based on a quasi-steady-state tire model is introduced to improve the accuracy of modeling the vehicle response to continuous acceleration fluctuations. Second, an adaptive STKF is developed using the vehicle dynamics model, incorporating a fading factor and an excitation factor. The fading factor, determined from the residual sequence of the observation variable, is utilized to correct the prediction error variance, thereby enhancing the tracking capability and estimation accuracy of the algorithm. The excitation factor, calculated based on real-time vehicle state, adjusts the fading factor to prevent significant prediction error variance under low excitation intensity and to avoid divergence of the estimation algorithm. Experimental results under various road conditions demonstrate the effectiveness of the proposed method in improving the convergence speed and stability of TRFC estimation. The root-mean-square error of the estimated values is improved by more than 18%, and the mean absolute error is improved by over 34% compared to the benchmark method.

In vehicle motion control, handling, stability, and anti-slip performance are critical for vehicle safety and driver experience. Most existing studies address these requirements using separate controllers, but vehicle dynamics are coupled, which can lead to conflicting control objectives, especially under extreme conditions. To address this, an integrated control architecture is proposed that combines longitudinal, lateral, yaw, and wheel rotation dynamics. A nonlinear model predictive control (NMPC) strategy is used to coordinate these dynamics and meet the control objectives. A fast iteration architecture is introduced for real-time optimization, with the original problem reformulated using Pontryagin’s minimum principle. The effectiveness of the proposed strategy is validated through real vehicle experiments. Compared with the subsystem coordination method, a better closed-loop control performance can be demonstrated with the experiment results under complex road conditions. The mean value of computation time can also be reduced by 7–34 times with the proposed fast iteration architecture under different prediction horizon conditions.

For distributed-drive electric vehicles, torque vectoring control based on model predictive control (MPC) has emerged as a preferred strategy to achieve superior performance across diverse driving conditions. However, traditional methods heavily rely on high-order nonlinear models, which require extensive tuning to achieve optimal performance. This article proposes a gradient-based automatic hyperparameter tuning algorithm to enhance vehicle maneuverability and stability by optimizing the MPC objective function. First, a data-driven predictive motion control scheme is introduced, enabling accurate modeling of the real vehicle through a data-driven approach. Subsequently, a differentiable alternating direction method of multipliers (ADMM) is proposed to compute the gradients of state and control input trajectories with respect to MPC weights during optimization. Real vehicle experiments demonstrate that the method effectively captures the characteristics of various driving conditions and automatically adjusts parameters for adaptation. Compared to manual parameter tuning, the strategy improves maneuverability by 33.02%, lateral stability by 27.19%, and ride comfort by 20.78%. Furthermore, in comparison with existing gradient-based tuning methods, the differentiable ADMM achieves more stable online parameter tuning. These metrics are further enhanced by 19.62%, 5.23%, and 34.5%, respectively.

Multi-objective reinforcement learning strategy employs vehicle reachable set optimization are proposed to address two prevalent problems in autonomous vehicle testing: the lack of critical scenarios and the sameness in scenarios generated by traditional methods.The framework prioritizes vehicle dynamics to construct relevant and varied testing scenarios, with a focus on risk triggering states. To streamline the navigation of safety states, we implement a distance-based reward function. Simultaneously, other reward functions balance the frequency and distribution of critical events, drawing on historical trends and Kullback-Leibler divergence for fine-tuning. Our model's efficacy is underpinned by stringent evaluation, showcasing a synergy between training efficiency and scenario variety. Further validation is provided through advanced hardware-in-the-loop simulations, confirming the robustness of our scenario design components in real-world conditions. The proposed method exhibits strong adaptability compared to Reinforcement Learning and Diversity-Driven Exploration, ensuring that the actual probability distribution of accident occurrences closely aligns with the expected distribution (KL < 0.5) . Furthermore, it achieves a coverage rate of 98.77% for the environmental states that may lead to accident scenarios, effectively preventing the occurrence of a single dominant critical scenario type in testing.

The pronounced nonlinearity of vehicle dynamics under extreme conditions presents a substantial challenge for motion control. The complexity of nonlinear modeling, parameter inaccuracies, and the high computational cost associated with high-order nonlinear control systems significantly hinder improvements in control stability and performance. To address these challenges, this article proposes an innovative data-driven model predictive control (DMPC) approach. A data-driven modeling (DM) technique is first introduced to construct a linear model based on step response input/output (I/O) data, with real-time compensation for model deviations using online data to more accurately capture the vehicle’s lateral and longitudinal dynamics. The stability of the model estimation error is rigorously analyzed, and a data-driven vehicle model with theoretical guarantees is established. A hierarchical stability control framework based on DM is then developed. The upper-level controller evaluates vehicle maneuverability and lateral stability to determine the required additional yaw moment, while the lower-level controller assesses longitudinal stability and traction, and incorporates lateral–longitudinal coupling to optimize motor torque distribution across the wheels. DM is validated using more than 25 000 s of low-adhesion road data, demonstrating significantly higher modeling accuracy than traditional nonlinear models (NLMs). Compared with the NLM-based controller, DMPC improves vehicle handling and lateral stability by 28.98% and 66.92%, respectively, while reducing driver workload by 33.72% in lateral tests. In longitudinal tests, the strategy increases vehicle acceleration by 58.65% and reduces yaw fluctuation by 79.5%. Finally, the robustness of DMPC is evaluated through parameter sensitivity analysis and variations in tire pressure.