Human-Tailored Data-Driven Control System of Autonomous Vehicles

Path Tracking Controller (PTC) plays a key role in achieving improved dynamic behaviour of Autonomous Vehicle (AV) while it is mainly responsible for implementing the planned paths. In this work, a learning-based Model Predictive Controller (MPC) is proposed to integrate a data-driven approach to the path tracking task using human driving demonstrations. The objective is to reproduce customised vehicle motion profile that is statistically comparable to a human driver with a proper trade-off between vehicle speed-acceleration profile and tracking accuracy. A combined longitudinal and lateral MPC controller with a novel feature-based parametric cost function is designed to perform the path tracking task under different road conditions. A high precision data logging system is developed to collect human driving demonstration data. From the analysis of the collected data, relevant features are chosen based on their applicability to produce a customised motion profile while performing AV’s path tracking task. Next, a bi-level optimisation-based Inverse Optimal Controller (IOC) is designed to learn the cost function’s parameters using the human demonstration data. The outcomes of the study show that the proposed controller can effectively regenerate some characteristics of human driving, including speed, longitudinal and lateral accelerations, and yaw rate.