A vehicle control method, characterized by comprising: acquiring, by an adaptive control module, an acceleration error between a desired acceleration at a current moment and a real acceleration at a previous moment of a vehicle, wherein the real acceleration at the previous moment is output by a chassis system of the vehicle, the desired acceleration at the current moment is obtained by the vehicle controller according to a reference signal and the real acceleration at the previous moment and output by the vehicle controller; determining, by the adaptive control module, a control quantity of the vehicle at the current moment based on the acceleration error and a mapping relationship between the control quantity of the vehicle and the acceleration error, and outputting the control quantity of the vehicle at the current moment to the chassis system; and controlling, by the chassis system, the driving behavior of the vehicle based on the control quantity at the current moment to obtain the real acceleration at the current moment, wherein the mapping relationship is a neural network model, and the determining the control quantity of the vehicle at the current moment based on the acceleration error and the mapping relationship between the control quantity of the vehicle and the acceleration error comprises: determining a filtering error and input data based on the acceleration error; processing the input data using the neural network model at the current moment to obtain output data; and determining the control quantity of the vehicle at the current moment based on the filtering error and the output data; updating the neural network model at the previous moment based on the filtering error to determine the neural network model at the current moment, wherein model parameters of the neural network model comprise a first parameter and a second parameter, the first parameter is a fixed value, and the second parameter is an updatable parameter; and the updating the neural network model at the previous moment based on the filtering error to determine the neural network model at the current moment comprises: updating the second parameter at the previous moment based on the filtering error to determine the second parameter at the current moment. acquiring, by an adaptive control module, an acceleration error between a desired acceleration at a current moment and a real acceleration at a previous moment of a vehicle, wherein the real acceleration at the previous moment is output by a chassis system of the vehicle, the desired acceleration at the current moment is obtained by the vehicle controller according to a reference signal and the real acceleration at the previous moment and output by the vehicle controller; determining, by the adaptive control module, a control quantity of the vehicle at the current moment based on the acceleration error and a mapping relationship between the control quantity of the vehicle and the acceleration error, and outputting the control quantity of the vehicle at the current moment to the chassis system; and controlling, by the chassis system, the driving behavior of the vehicle based on the control quantity at the current moment to obtain the real acceleration at the current moment, wherein the mapping relationship is a neural network model, and the determining the control quantity of the vehicle at the current moment based on the acceleration error and the mapping relationship between the control quantity of the vehicle and the acceleration error comprises: determining a filtering error and input data based on the acceleration error; processing the input data using the neural network model at the current moment to obtain output data; and determining the control quantity of the vehicle at the current moment based on the filtering error and the output data; updating the neural network model at the previous moment based on the filtering error to determine the neural network model at the current moment, wherein model parameters of the neural network model comprise a first parameter and a second parameter, the first parameter is a fixed value, and the second parameter is an updatable parameter; and the updating the neural network model at the previous moment based on the filtering error to determine the neural network model at the current moment comprises: updating the second parameter at the previous moment based on the filtering error to determine the second parameter at the current moment. determining a filtering error and input data based on the acceleration error; processing the input data using the neural network model at the current moment to obtain output data; and determining the control quantity of the vehicle at the current moment based on the filtering error and the output data; updating the neural network model at the previous moment based on the filtering error to determine the neural network model at the current moment, wherein model parameters of the neural network model comprise a first parameter and a second parameter, the first parameter is a fixed value, and the second parameter is an updatable parameter; and the updating the neural network model at the previous moment based on the filtering error to determine the neural network model at the current moment comprises: updating the second parameter at the previous moment based on the filtering error to determine the second parameter at the current moment.