Generalized Single-Vehicle-Based Graph Reinforcement Learning for Decision-Making in Autonomous Driving

In the autonomous driving process, the decision-making system is mainly used to provide macro-control instructions based on the information captured by the sensing system. Learning-based algorithms have apparent advantages in information processing and understanding for an increasingly complex drivi...

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Autores principales: Yang, Fan, Li, Xueyuan, Liu, Qi, Li, Zirui, Gao, Xin
Formato: Online Artículo Texto
Lenguaje:English
Publicado: MDPI 2022
Materias:
Acceso en línea:https://www.ncbi.nlm.nih.gov/pmc/articles/PMC9269790/
https://www.ncbi.nlm.nih.gov/pubmed/35808428
http://dx.doi.org/10.3390/s22134935
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author Yang, Fan
Li, Xueyuan
Liu, Qi
Li, Zirui
Gao, Xin
author_facet Yang, Fan
Li, Xueyuan
Liu, Qi
Li, Zirui
Gao, Xin
author_sort Yang, Fan
collection PubMed
description In the autonomous driving process, the decision-making system is mainly used to provide macro-control instructions based on the information captured by the sensing system. Learning-based algorithms have apparent advantages in information processing and understanding for an increasingly complex driving environment. To incorporate the interactive information between agents in the environment into the decision-making process, this paper proposes a generalized single-vehicle-based graph neural network reinforcement learning algorithm (SGRL algorithm). The SGRL algorithm introduces graph convolution into the traditional deep neural network (DQN) algorithm, adopts the training method for a single agent, designs a more explicit incentive reward function, and significantly improves the dimension of the action space. The SGRL algorithm is compared with the traditional DQN algorithm (NGRL) and the multi-agent training algorithm (MGRL) in the highway ramp scenario. Results show that the SGRL algorithm has outstanding advantages in network convergence, decision-making effect, and training efficiency.
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spelling pubmed-92697902022-07-09 Generalized Single-Vehicle-Based Graph Reinforcement Learning for Decision-Making in Autonomous Driving Yang, Fan Li, Xueyuan Liu, Qi Li, Zirui Gao, Xin Sensors (Basel) Article In the autonomous driving process, the decision-making system is mainly used to provide macro-control instructions based on the information captured by the sensing system. Learning-based algorithms have apparent advantages in information processing and understanding for an increasingly complex driving environment. To incorporate the interactive information between agents in the environment into the decision-making process, this paper proposes a generalized single-vehicle-based graph neural network reinforcement learning algorithm (SGRL algorithm). The SGRL algorithm introduces graph convolution into the traditional deep neural network (DQN) algorithm, adopts the training method for a single agent, designs a more explicit incentive reward function, and significantly improves the dimension of the action space. The SGRL algorithm is compared with the traditional DQN algorithm (NGRL) and the multi-agent training algorithm (MGRL) in the highway ramp scenario. Results show that the SGRL algorithm has outstanding advantages in network convergence, decision-making effect, and training efficiency. MDPI 2022-06-29 /pmc/articles/PMC9269790/ /pubmed/35808428 http://dx.doi.org/10.3390/s22134935 Text en © 2022 by the authors. https://creativecommons.org/licenses/by/4.0/Licensee MDPI, Basel, Switzerland. This article is an open access article distributed under the terms and conditions of the Creative Commons Attribution (CC BY) license (https://creativecommons.org/licenses/by/4.0/).
spellingShingle Article
Yang, Fan
Li, Xueyuan
Liu, Qi
Li, Zirui
Gao, Xin
Generalized Single-Vehicle-Based Graph Reinforcement Learning for Decision-Making in Autonomous Driving
title Generalized Single-Vehicle-Based Graph Reinforcement Learning for Decision-Making in Autonomous Driving
title_full Generalized Single-Vehicle-Based Graph Reinforcement Learning for Decision-Making in Autonomous Driving
title_fullStr Generalized Single-Vehicle-Based Graph Reinforcement Learning for Decision-Making in Autonomous Driving
title_full_unstemmed Generalized Single-Vehicle-Based Graph Reinforcement Learning for Decision-Making in Autonomous Driving
title_short Generalized Single-Vehicle-Based Graph Reinforcement Learning for Decision-Making in Autonomous Driving
title_sort generalized single-vehicle-based graph reinforcement learning for decision-making in autonomous driving
topic Article
url https://www.ncbi.nlm.nih.gov/pmc/articles/PMC9269790/
https://www.ncbi.nlm.nih.gov/pubmed/35808428
http://dx.doi.org/10.3390/s22134935
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