Objective Aiming at the problems of limited application scenarios poor real-time performance and low accuracy of traditional single-control methods in the field of intelligent vehicle trajectory tracking control a laterallongitudinal collaborative control strategy based on a fuzzy neural network algorithm ANFIS - LQR/ PID is proposed. The aim is to ensure the accuracy of trajectory tracking of intelligent vehicles under different road conditions and the comfort of drivers and passengers. Methods The motion of the vehicle is analyzed and a model is constructed. This model is projected onto the Frenet coordinate system and a tracking error model is built with the deviation values between the desired and current coordinates as state variables. The designed adaptive fuzzy neural network adjustment strategy is integrated with the vehicle?? s lateral-longitudinal collaborative controller to achieve real-time adjustment of the weight coefficients of lateral linear quadratic regulation LQR and longitudinal proportional-integral-derivative PID so as to effectively solve the problem of tracking control under different vehicle speeds. Results The PreScan-CarSim / Simulink software is used to build a joint simulation platform. The platform is used to simulate three working conditions continuous curve following two-lane variable-speed overtaking and large-curvature curve acceleration following involving different lateral-longitudinal control problems for verification. Conclusion The simulation results show that the lateral-longitudinalcollaborative controller based on the fuzzy neural network algorithm designed in this paper can stably control the vehicle?? s trajectory tracking error within the specified range under various working conditions. Additionally it ensures the comfort of drivers and passengers under optimal control.