Precise Longitudinal Control of Automated Vehicles without Complex Modeling Based on Physical Data

Precise controls of vehicle states are crucial to automated vehicles (AVs). Traditional model-based AV control algorithms require complex modeling and controller design, and their accuracy is still affected a lot by various uncertainties. Latest data-driven controls such as artificial neural network (ANN)-based controls can reduce modeling efforts but are usually subject to robustness issues in unseen scenarios. This article proposes to combine a data-driven control and a typical analytical model-formed control to achieve a better AV longitudinal control performance with fewer modeling efforts. The data-driven control can handle the complex modeling, calibration, and controller design, and the analytical model-formed control can guide the direction of the control with better predictability and robustness in unseen scenarios. The proposed controller is experimentally implemented and validated using a real AV. The performance is compared to the standalone data-driven controller and analytic model-formed controller, and the experimental results demonstrate the effectiveness and advantages of the proposed approach.