Nemeth, Balazs (SZTAKI), Antal, Zoltan (SZTAKI Institute for Computer Science and Control), Marosi, Attila Csaba (SZTAKI Institute for Computer Science and Control), Lovas, Robert (SZTAKI Institute for Computer Science and Control), Fazekas, Mate (Hungarian Academy of Sciences Institute for Computer Science and), Gaspar, Peter (SZTAKI)

Vehicle Control with Cloud-aided Learning Feature: an Implementation on Indoor Platform

Scheduled for presentation during the Regular Session "Vehicle Dynamics and Control" (TuAT4), Tuesday, August 30, 2022, 10:20−10:40, Ballroom

10th IFAC International Symposium on Advances in Automotive Control, August 28-31, 2022, Columbus, Ohio, USA

This information is tentative and subject to change. Compiled on April 26, 2024

Abstract

Safe motion together with improved economy and traveling performance levels are important requirements against automated vehicles. Thus, the design of enhanced control systems is requested, which contain conventional model-based controllers and the use of unconventional approaches, e.g., learning features and cloud-based methods. This paper proposes a hierarchical vehicle control design method with learning functions, which incorporates control in two levels, such as in cloud level and in vehicle level. The control on the cloud level is designed by using reinforcement learning, with which the maximum speed for the vehicle is achieved. The vehicle level contains a robust controller and a supervisor, with which the collision avoidance of the vehicle is guaranteed. The hierarchical control guarantees performance requirement of safe motion, i.e., collision avoidance in all scenarios, even if the connection with the cloud is lost. The proposed control on indoor Hardware-in-the-Loop platform is implemented. The effectiveness of the control and the safe motion of the vehicle under various scenarios with and without cloud connection are demonstrated.