Abstract: Building upon an integrated vehicle stability and trajectory tracking control framework, this study proposes an adaptive prediction horizon nonlinear model predictive control (NMPC) strategy incorporating previewed curvature information. By leveraging a preview-based reference path curvature point sequence to dynamically adjust control parameters, the proposed method enhances the controller’s responsiveness to path curvature variations and mitigates tracking accuracy degradation caused by accumulated errors in fixed-horizon strategies during high-curvature trajectory tracking. A state coordination optimization mechanism is introduced to explicitly couple the controller with the vehicle state space from the previous control cycle, effectively suppressing decoupling effects in multi-step optimization problems induced by prediction horizon variations and minimizing control input discontinuities. Validation via MATLAB/Simulink-CarSim co-simulation demonstrates significant improvements: in high-speed single lane-change scenarios, the method reduces 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 achieves 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 maintains robust precision and stability with peak lateral deviation at 0.2017 m, peak longitudinal deviation at 0.9744 km/h, peak heading deviation at 1.1936°, and peak centroid sideslip angle at 1.9074°。