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Research on Behavioral Decision at an Unsignalized Roundabout for Automatic Driving Based on Proximal Policy Optimization Algorithm

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Titel: Research on Behavioral Decision at an Unsignalized Roundabout for Automatic Driving Based on Proximal Policy Optimization Algorithm
Autoren: Jingpeng Gan, Jiancheng Zhang, Yuansheng Liu
Quelle: Applied Sciences, Vol 14, Iss 7, p 2889 (2024)
Verlagsinformationen: MDPI AG
Publikationsjahr: 2024
Bestand: Directory of Open Access Journals: DOAJ Articles
Schlagwörter: autonomous vehicle, deep reinforcement learning, optimized PPO algorithm, unsignalized roundabout, gap acceptance theory, Technology, Engineering (General). Civil engineering (General), TA1-2040, Biology (General), QH301-705.5, Physics, QC1-999, Chemistry, QD1-999
Beschreibung: Unsignalized roundabouts have a significant impact on traffic flow and vehicle safety. To address the challenge of autonomous vehicles passing through roundabouts with low penetration, improve their efficiency, and ensure safety and stability, we propose the proximal policy optimization (PPO) algorithm to enhance decision-making behavior. We develop an optimization-based behavioral choice model for autonomous vehicles that incorporates gap acceptance theory and deep reinforcement learning using the PPO algorithm. Additionally, we employ the CoordConv network to establish an aerial view for spatial perception information gathering. Furthermore, a dynamic multi-objective reward mechanism is introduced to maximize the PPO algorithm’s reward pool function while quantifying environmental factors. Through simulation experiments, we demonstrate that our optimized PPO algorithm significantly improves training efficiency by enhancing the reward value function by 2.85%, 7.17%, and 19.58% in scenarios with 20, 100, and 200 social vehicles, respectively, compared to the PPO+CCMR algorithm. The effectiveness of simulation training also increases by 11.1%, 13.8%, and 7.4%. Moreover, there is a reduction in crossing time by 2.37%, 2.62%, and 13.96%. Our optimized PPO algorithm enhances path selection during autonomous vehicle simulation training as they tend to drive in the inner ring over time; however, the influence of social vehicles on path selection diminishes as their quantity increases. The safety of autonomous vehicles remains largely unaffected by our optimized PPO algorithm.
Publikationsart: article in journal/newspaper
Sprache: English
Relation: https://www.mdpi.com/2076-3417/14/7/2889; https://doaj.org/toc/2076-3417; https://doaj.org/article/3147b64605354ccd9cc86e06039bdec7
DOI: 10.3390/app14072889
Verfügbarkeit: https://doi.org/10.3390/app14072889
https://doaj.org/article/3147b64605354ccd9cc86e06039bdec7
Dokumentencode: edsbas.12A5C5E2
Datenbank: BASE
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  Label: Title
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  Data: Research on Behavioral Decision at an Unsignalized Roundabout for Automatic Driving Based on Proximal Policy Optimization Algorithm
– Name: Author
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  Data: <searchLink fieldCode="AR" term="%22Jingpeng+Gan%22">Jingpeng Gan</searchLink><br /><searchLink fieldCode="AR" term="%22Jiancheng+Zhang%22">Jiancheng Zhang</searchLink><br /><searchLink fieldCode="AR" term="%22Yuansheng+Liu%22">Yuansheng Liu</searchLink>
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  Data: Applied Sciences, Vol 14, Iss 7, p 2889 (2024)
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  Data: MDPI AG
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  Data: 2024
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  Data: Directory of Open Access Journals: DOAJ Articles
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  Data: <searchLink fieldCode="DE" term="%22autonomous+vehicle%22">autonomous vehicle</searchLink><br /><searchLink fieldCode="DE" term="%22deep+reinforcement+learning%22">deep reinforcement learning</searchLink><br /><searchLink fieldCode="DE" term="%22optimized+PPO+algorithm%22">optimized PPO algorithm</searchLink><br /><searchLink fieldCode="DE" term="%22unsignalized+roundabout%22">unsignalized roundabout</searchLink><br /><searchLink fieldCode="DE" term="%22gap+acceptance+theory%22">gap acceptance theory</searchLink><br /><searchLink fieldCode="DE" term="%22Technology%22">Technology</searchLink><br /><searchLink fieldCode="DE" term="%22Engineering+%28General%29%2E+Civil+engineering+%28General%29%22">Engineering (General). Civil engineering (General)</searchLink><br /><searchLink fieldCode="DE" term="%22TA1-2040%22">TA1-2040</searchLink><br /><searchLink fieldCode="DE" term="%22Biology+%28General%29%22">Biology (General)</searchLink><br /><searchLink fieldCode="DE" term="%22QH301-705%2E5%22">QH301-705.5</searchLink><br /><searchLink fieldCode="DE" term="%22Physics%22">Physics</searchLink><br /><searchLink fieldCode="DE" term="%22QC1-999%22">QC1-999</searchLink><br /><searchLink fieldCode="DE" term="%22Chemistry%22">Chemistry</searchLink><br /><searchLink fieldCode="DE" term="%22QD1-999%22">QD1-999</searchLink>
– Name: Abstract
  Label: Description
  Group: Ab
  Data: Unsignalized roundabouts have a significant impact on traffic flow and vehicle safety. To address the challenge of autonomous vehicles passing through roundabouts with low penetration, improve their efficiency, and ensure safety and stability, we propose the proximal policy optimization (PPO) algorithm to enhance decision-making behavior. We develop an optimization-based behavioral choice model for autonomous vehicles that incorporates gap acceptance theory and deep reinforcement learning using the PPO algorithm. Additionally, we employ the CoordConv network to establish an aerial view for spatial perception information gathering. Furthermore, a dynamic multi-objective reward mechanism is introduced to maximize the PPO algorithm’s reward pool function while quantifying environmental factors. Through simulation experiments, we demonstrate that our optimized PPO algorithm significantly improves training efficiency by enhancing the reward value function by 2.85%, 7.17%, and 19.58% in scenarios with 20, 100, and 200 social vehicles, respectively, compared to the PPO+CCMR algorithm. The effectiveness of simulation training also increases by 11.1%, 13.8%, and 7.4%. Moreover, there is a reduction in crossing time by 2.37%, 2.62%, and 13.96%. Our optimized PPO algorithm enhances path selection during autonomous vehicle simulation training as they tend to drive in the inner ring over time; however, the influence of social vehicles on path selection diminishes as their quantity increases. The safety of autonomous vehicles remains largely unaffected by our optimized PPO algorithm.
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  Data: article in journal/newspaper
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  Data: 10.3390/app14072889
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  Data: https://doi.org/10.3390/app14072889<br />https://doaj.org/article/3147b64605354ccd9cc86e06039bdec7
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RecordInfo BibRecord:
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    Identifiers:
      – Type: doi
        Value: 10.3390/app14072889
    Languages:
      – Text: English
    Subjects:
      – SubjectFull: autonomous vehicle
        Type: general
      – SubjectFull: deep reinforcement learning
        Type: general
      – SubjectFull: optimized PPO algorithm
        Type: general
      – SubjectFull: unsignalized roundabout
        Type: general
      – SubjectFull: gap acceptance theory
        Type: general
      – SubjectFull: Technology
        Type: general
      – SubjectFull: Engineering (General). Civil engineering (General)
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      – SubjectFull: TA1-2040
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      – SubjectFull: Biology (General)
        Type: general
      – SubjectFull: QH301-705.5
        Type: general
      – SubjectFull: Physics
        Type: general
      – SubjectFull: QC1-999
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      – SubjectFull: Chemistry
        Type: general
      – SubjectFull: QD1-999
        Type: general
    Titles:
      – TitleFull: Research on Behavioral Decision at an Unsignalized Roundabout for Automatic Driving Based on Proximal Policy Optimization Algorithm
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            NameFull: Jingpeng Gan
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            NameFull: Jiancheng Zhang
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            NameFull: Yuansheng Liu
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            – D: 01
              M: 01
              Type: published
              Y: 2024
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            – TitleFull: Applied Sciences, Vol 14, Iss 7, p 2889 (2024
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