Abstract: In certain emergency maneuver scenarios, such as high-speed lane changes or collision avoidance, the trajectory-tracking controller must guarantee strict vehicle stability and maintain high control accuracy to prevent safety hazards. The strongly coupled dynamics and pronounced nonlinearities of a vehicle pose significant challenges in achieving both objectives. However, the four-wheel independently driven or steered, distributed electric-drive intelligent vehicle chassis provides a versatile platform for active safety technologies. In addition, the inherent strengths of model predictive control (MPC) in handling linear, multi-objective constraints offer theoretical support for achieving high-precision stability control. The prediction horizon determines both the step length of MPC’s receding-horizon optimization and extent of the predicted future vehicle state space, such that a longer horizon enhances control smoothness, whereas a shorter horizon improves the vehicle’s dynamic responsiveness to path-curvature variations and mitigates the control-accuracy degradation caused by accumulated model-prediction errors. To date, discussions on adaptive prediction-horizon optimization in high-speed stability MPC trajectory tracking controllers remain scarce, making it difficult to strike an optimal balance between curvature-response speed and vehicle stability. To this end, this study builds upon an integrated vehicle stability and trajectory tracking control framework, to propose an adaptive prediction horizon nonlinear model predictive control (NMPC) strategy that incorporates previewed curvature information. By leveraging a preview-based reference path curvature point sequence, the control parameters are dynamically adjusted. The proposed method enhances the controller’s responsiveness to path curvature variations and mitigates the tracking accuracy degradation caused by accumulated errors in fixed-horizon strategies during high-curvature trajectory tracking. A state-coordination optimization mechanism designed via optimization sub-objective, explicitly couples the controller to the vehicle state of the previous control cycle. This effectively suppresses the decoupling effects in multistep optimization problems induced by prediction horizon variations and minimizes the discontinuities in control inputs. Finally, the proposed algorithm was validated in a co-simulation environment built using MATLAB/Simulink and CarSim. Representative high-speed maneuvering control scenarios were selected to quantitatively assess its performance. Comparative evaluations against other methods demonstrated the superiority of the proposed algorithm: in high-speed single lane-change scenarios. The method reduced average/peak lateral deviations by 36.17%/15.25%, average/peak longitudinal deviations by 11.55%/38.58%, and average/peak heading deviations by 6.13%/25.27% compared to fixed-horizon NMPC. In high-speed double lane-change scenarios, it achieved reductions of 30.28%/29.77% (lateral), 25.07%/3.85% (longitudinal), and 11.02%/32.68% (heading). Under high-speed low-adhesion conditions (μ=0.4), the method maintained robust precision and stability with peak lateral deviation of 0.2017 m, peak longitudinal deviation of 0.9744 km/h, peak heading deviation of 1.1936°, and peak centroid sideslip angle of 1.9074°. These quantitative metrics demonstrate that adaptive predictive horizon optimization, which leverages preview curvature information and state coordination, can further improve vehicle trajectory tracking accuracy while maintaining adequate stability margins.