Communication Dans Un Congrès Année : 2022

Intelligent Control Switching for Autonomous Vehicles based on Reinforcement Learning

Résumé

This paper presents the design and implementation of an intelligent switched control for lateral control of autonomous vehicles. The switched control is designed based on Linear Parameter-Varying (LPV) and Youla-Kucera (YK) parameterization. The proposed intelligent system aims to optimize the control switching performance using a Reinforcement Learning (RL) model. The presented approach studies the critical problem of initial or sudden large lateral errors in lane-tracking or lane-changing. It ensures stable and smooth switching performance to provide a smooth vehicle response regardless of the lateral error. The proposed RL-based switching strategy is validated using a RENAULT simulator on MATLAB, and compared to another modeled switching strategy with encouraging results.

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Dates et versions

hal-04892247 , version 1 (16-01-2025)

Identifiants

Citer

Hussam Atoui, Olivier Sename, Vicente Milanes, John Jairo Martinez Molina. Intelligent Control Switching for Autonomous Vehicles based on Reinforcement Learning. IV 2022 - 33rd IEEE Intelligent Vehicles Symposium (IV 2022), Jun 2022, Aachen, Germany. pp.792-797, ⟨10.1109/IV51971.2022.9827384⟩. ⟨hal-04892247⟩
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