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...
Autores principales: | , , , , |
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Formato: | Online Artículo Texto |
Lenguaje: | English |
Publicado: |
MDPI
2022
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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. |
format | Online Article Text |
id | pubmed-9269790 |
institution | National Center for Biotechnology Information |
language | English |
publishDate | 2022 |
publisher | MDPI |
record_format | MEDLINE/PubMed |
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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