Tóth, Szilárd Hunor and Viharos, Zsolt János and Bárdos, Ádám and Szalay, Zsolt (2024) Sim-to-Real Application of Reinforcement Learning Agents for Autonomous, Real Vehicle Drifting. VEHICLES, 6 (2). pp. 781-798. ISSN 2624-8921 10.3390/vehicles6020037

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Abstract

Enhancing the safety of passengers by venturing beyond the limits of a human driver is one of the main ideas behind autonomous vehicles. While drifting is mostly witnessed in motorsports as an advanced driving technique, it could provide many possibilities for improving traffic safety by avoiding accidents in extreme traffic situations. The purpose of the research presented in this article is to provide a machine learning-based solution to autonomous drifting as a proof of concept for vehicle control at the limits of handling. To achieve this, reinforcement learning (RL) agents were trained for the task in a MATLAB/Simulink-based simulation environment, using the state-of-the-art Soft Actor–Critic (SAC) algorithm. The trained agents were tested in reality at the ZalaZONE proving ground on a series production sports car with zero-shot transfer. Based on the test results, the simulation environment was improved through domain randomization, until the agent could perform the task both in simulation and in reality on a real test car.

Item Type:	Article
Subjects:	Q Science > QA Mathematics and Computer Science > QA75 Electronic computers. Computer science / számítástechnika, számítógéptudomány
Divisions:	Research Laboratory on Engineering & Management Intelligence
SWORD Depositor:	MTMT Injector
Depositing User:	MTMT Injector
Date Deposited:	10 Jul 2024 07:38
Last Modified:	10 Jul 2024 07:38
URI:	https://eprints.sztaki.hu/id/eprint/10757

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