Autonomous car following: A learning-based approach

Abstract

We propose a learning-based method for the longitudinal control of an autonomous vehicle on the highway. We use a driver model to generate acceleration inputs which are used as a reference by a model predictive controller. The driver model is trained using real driving data, so that it can reproduce the driver's behavior. We show the system's ability to reproduce different driving styles from different drivers. By solving a constrained optimization problem, the model predictive controller ensures that the control inputs applied to the vehicle satisfy some safety criteria. This is demonstrated on a vehicle by artificially creating potentially dangerous situations with virtual obstacles.