Longitudinal speed control of autonomous vehicle based on a self-adaptive PID of radial basis function neural network

The tracking accuracy of speed plays a significant role in the autonomous vehicle's control and safety management. In this study, we presented a novel method called self-adaptive proportional integral derivative (PID) of radial basis function neural network (RBFNN-PID) which is shown with improved longitudinal speed tracking accuracy for autonomous vehicles. A forward simulation model of longitudinal speed control for autonomous vehicles is established based on the driver model of self-adaptive RBFNN-PID and the vehicle dynamics model. Based on that, we used the traditional PID and fuzzy control methods as benchmarks to demonstrate the edge of the self-adaptive RBFNN-PID control under the new European driving cycle. Simulation results show the RBFNN-PID method is significantly more precise than the comparing groups, with a reduced error in the range of [-0.369, 0.203]m/s. The vehicle performance gives better ride comfort as well. In all, self-adaptive RBFNN-PID is proven to be effective in longitudinal speed control of autonomous vehicles and significantly outperforms the other two methods.