Optimal Path Generator Embedded Model Predictive Control for Automated Vehicles

This paper investigates real-time optimal control for an automated vehicle. The model predictive control that generates the optimal trajectories has found wide applications in recent years due to increased computational performance. Numerical simulations of the full-vehicle model investigate the applicability of a path generator embedded model predictive control for the vehicle in the general shape road. The path generator is constructed by deep learning using the multiple open-loop optimal control problem solutions as the training dataset. Results demonstrate that the sequentially calculating optimal control command has the potential for real-time optimal control in the presence of the obstacle.