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Personalized lane change decision algorithm using deep reinforcement learning approach

Personalized lane change decision algorithm using deep reinforcement learning approach To develop driving automation technologies for humans, a human-centered methodology should be adopted for safety and satisfactory user experience. Automated lane change decision in dense highway traffic is challenging, especially when considering different driver preferences. This paper proposes a personalized lane change decision algorithm based on deep reinforcement learning. Firstly, driving experiments are carried out on a moving-base simulator. Based on the analysis of the experiment data, three personalization indicators are selected to describe the driver preferences in lane-change decisions. Then, a deep reinforcement learning (RL) approach is applied to design human-like agents for automated lane change decisions to capture the driver preferences, with refined rewards using the three personalization indicators. Finally, the trained RL agents and benchmark agents are tested in a two-lane highway driving scenario. Results show that the proposed algorithm can achieve higher consistency of lane change decision preferences than the comparison algorithm. http://www.deepdyve.com/assets/images/DeepDyve-Logo-lg.png Applied Intelligence Springer Journals

Personalized lane change decision algorithm using deep reinforcement learning approach

Li, Daofei; Liu, Ao
Applied Intelligence , Volume 53 (11) – Jun 1, 2023
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References (45)

Publisher
Springer Journals
Copyright
Copyright © The Author(s), under exclusive licence to Springer Science+Business Media, LLC, part of Springer Nature 2022. Springer Nature or its licensor holds exclusive rights to this article under a publishing agreement with the author(s) or other rightsholder(s); author self-archiving of the accepted manuscript version of this article is solely governed by the terms of such publishing agreement and applicable law.
ISSN
0924-669X
eISSN
1573-7497
DOI
10.1007/s10489-022-04172-1
Publisher site
See Article on Publisher Site

Abstract

To develop driving automation technologies for humans, a human-centered methodology should be adopted for safety and satisfactory user experience. Automated lane change decision in dense highway traffic is challenging, especially when considering different driver preferences. This paper proposes a personalized lane change decision algorithm based on deep reinforcement learning. Firstly, driving experiments are carried out on a moving-base simulator. Based on the analysis of the experiment data, three personalization indicators are selected to describe the driver preferences in lane-change decisions. Then, a deep reinforcement learning (RL) approach is applied to design human-like agents for automated lane change decisions to capture the driver preferences, with refined rewards using the three personalization indicators. Finally, the trained RL agents and benchmark agents are tested in a two-lane highway driving scenario. Results show that the proposed algorithm can achieve higher consistency of lane change decision preferences than the comparison algorithm.

Journal

Applied Intelligence – Springer Journals

Published: Jun 1, 2023

Keywords: Reinforcement learning; Deep Q-Network; Automated driving; Lane change decision; Driver-in-the-loop experiment; Driving style

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