Considering the problems that the inner wheel load decreases due to centrifugal force during the steering of differential steering autonomous vehicles, which may result in differential steering failure or even vehicle rollover in severe cases, a path-tracking strategy for differential steering autonomous vehicles considering active body inward tilt is proposed. Aiming at the problems of fixed parameters and insufficient adaptability of model predictive control in the path-tracking process of autonomous vehicles, this paper proposes a collaborative adaptive model predictive controller (MPC) with preview time and weight matrix based on fuzzy inference as the upper control, so as to realize the tracking control of the reference path by conventionally steered autonomous vehicles. In the lower control, an H_∞/H₂ hybrid controller with particle swarm optimization (PSO)-based parameter self-tuning is employed to control the differential steering autonomous vehicle (DSAV) to track the reference model, achieving differential steering and active body inward tilt simultaneously. Co-simulation results of CarSim and Simulink show that the proposed method outperforms the fixed-preview-time MPC and the manually tuned H_∞/H₂ hybrid controller. Compared with the latter, the maximum absolute values of lateral deviation and yaw angle deviation are reduced by 17.9% and 14.5%, respectively; the maximum deviation in the reference yaw rate is decreased by 21.2%; the maximum absolute value of the inward tilt angle is reduced by 53.4%; and the maximum values of LTR and occupant-perceived lateral acceleration are lowered by 57.1% and 44.2%, respectively.

To address smoothness and safety issues in driving authority switching for human–machine co-driving intelligent vehicles, a hierarchical control strategy is proposed. The upper-layer controller utilizes a two-point preview model for human driving and a radial basis function neural network-based nonsingular fast terminal sliding mode controller with a heuristic gain adaptive tuning mechanism (RBFNN-HGATM-NFTSMC) for autonomous tracking. The lower-layer authority switching controller employs a driving risk-based finite state machine, integrating a risk-switching strategy and a cost function-based authority return mechanism. CarSim/Simulink co-simulations show that, compared to model predictive control, the upper-layer controller reduces maximum lateral errors by 77.7% and 66.2% under medium- and high-speed conditions, respectively. Compared with standard RBFNN-NFTSMC, it reduces lateral and heading errors by 56.2% and 11.9% at high speeds. With the decision logic held fixed, the risk-mapped transition lowers the takeover jerk peak by 35.75% and the return-phase jerk by 91.4% relative to an instantaneous transfer, at the cost of a larger transient lateral error during the handover. Under high-speed sudden distraction, the maximum lateral tracking error is 1.17 m. Under extreme parameter mismatch conditions, lateral and heading errors are reduced by 88.5% and 87.1% compared to standard NFTSMC, indicating robustness of the proposed controller under the tested parameter mismatch.