Machine Learning-Based Steering Control for Automated Vehicles Utilizing V2X Communication

A neural network-based controller is trained on data collected from connected human-driven vehicles in order to steer a connected automated vehicle on multi-lane roads. The obtained controller is evaluated using model-based simulations and its performance is compared to that of a traditional nonlinear feedback controller. The comparison of the control laws obtained by the two different approaches provides information about the naturalistic nonlinearities in human steering, and this can benefit the controller development of automated vehicles. The effects of time delay emerging from vehicle-to-everything (V2X) communication, computation, and actuation are also highlighted.