Abstract: An adaptive fixed-time sliding mode control method based on a radial basis function neural network is proposed for the unmanned vehicle system with external disturbances and input saturation. Firstly, the RBF neural network is used to adaptively compensate for external disturbances, and a input saturation compensation system is designed to achieve rapid compensation of control inputs. Secondly, to improve the tracking performance, the fixed-time terminal sliding mode control laws are developed to guarantee that tracking errors converge to zero within a fixed time, thereby satisfying the desired convergence speed and tracking accuracy. Finally, co-simulations experiments using Carsim/MATLAB are conducted in both single and double lane change maneuver scenarios compared with existing sliding mode active disturbance rejection control method. Experimental results show that the proposed method provides higher tracking accuracy and smoother control input, with chattering effectively suppressed. Furthermore, it demonstrates strong robustness to initial errors, leading to faster error convergence.