Lateral stability control of autonomous vehicles with joint connected internet of vehicles technology

In order to overcome the problems of high lateral position error, heading angle error, and long control completion time in traditional methods, a lateral stability control method of autonomous vehicles with joint connected Internet of Vehicles technology is proposed. Collect relevant traffic data through Internet of Vehicles technology and use the unscented Kalman filter algorithm to accurately estimate key vehicle state variables. Based on the established nonlinear model of vehicle lateral dynamics, a fuzzy neural network controller is designed with yaw rate and center of mass lateral deviation angle as inputs, and a differential braking strategy is formulated according to the road adhesion coefficient to achieve lateral stability control of autonomous vehicles. The experimental results show that the maximum lateral position error of the proposed method is 0.19m, the maximum heading angle error is 1.76º, and the maximum control completion time is 0.43s, effectively improving the vehicle handling stability and driving safety.