Abstract: The present disclosure provides a self-learning collaborative control method for active steering and yaw moment for a motor vehicle, including a first step of constructing fundamental formulas which are stored in a vehicle ECU, and a second step of calculating an active steering angle ?C and a yaw moment Mc on line by the vehicle ECU according to following sub-steps during a driving process of the motor vehicle, and controlling a driving state of the motor vehicle according to ?C and Mc. The second step includes a first sub-step of collecting raw real-time parameter values, a second sub-step of performing calculation by the identifier and the control target reference model, a third sub-step of calculating ?C and Mc. The present disclosure can realize the self-learning collaborative control of active steering and yaw moment without requiring a system control model and correct a driver's steering operation.

Abstract: An autonomous vehicle longitudinal-and-lateral control method for preventing motion sickness is disclosed. Firstly, a vehicle dynamics model is established, and the current vehicle travelling state and road surface information are determined according to the vehicle dynamics model. After the vehicle travelling state and road surface information are acquired, the desired velocity, desired acceleration, and desired wheel turning angle are output according to a driver model for reducing occurrence of motion sickness while ensuring safety and efficiency, to carry out longitudinal-and-lateral control of the vehicle.

Abstract: The present disclosure provides a self-learning collaborative control method for active steering and yaw moment for a motor vehicle, including a first step of constructing fundamental formulas which are stored in a vehicle ECU, and a second step of calculating an active steering angle ?C and a yaw moment Mc on line by the vehicle ECU according to following sub-steps during a driving process of the motor vehicle, and controlling a driving state of the motor vehicle according to ?C and Mc. The second step includes a first sub-step of collecting raw real-time parameter values, a second sub-step of performing calculation by the identifier and the control target reference model, a third sub-step of calculating ?C and Mc. The present disclosure can realize the self-learning collaborative control of active steering and yaw moment without requiring a system control model and correct a driver's steering operation.