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

Németh, Balázs and Antal, Zoltán and Marosi, Attila Csaba and Lovas, Róbert and Fazekas, Máté and Gáspár, Péter (2022) Vehicle Control with Cloud-aided Learning Feature: an Implementation on Indoor Platform. IFAC PAPERSONLINE, 55 (24). pp. 227-232. ISSN 2405-8963 10.1016/j.ifacol.2022.10.289


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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. Copyright (c) 2022 The Authors. This is an open access article under the CC BY-NC-ND license

Item Type: Article
Uncontrolled Keywords: automated vehicles; vehicle-cloud connection; learning features; roundabouts;
Subjects: Q Science > QA Mathematics and Computer Science > QA75 Electronic computers. Computer science / számítástechnika, számítógéptudomány
Divisions: Laboratory of Parallel and Distributed Systems
Systems and Control Lab
SWORD Depositor: MTMT Injector
Depositing User: MTMT Injector
Date Deposited: 26 Jan 2023 12:02
Last Modified: 11 Sep 2023 15:01

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