Cited by 308Autonomous Vehicle Technology and SafetyReinforcement Learning in RoboticsTraffic and Road Safety

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Decision making of autonomous vehicles in lane change scenarios: Deep reinforcement learning approaches with risk awareness

Li, Guofa, Yang, Yifan, Li, Shen, Qu, Xingda, Lyu, Nengchao, Li, Shengbo Eben·2022·Transportation Research Part C: Emerging Technologies

DOI 10.1016/j.trc.2021.103452

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Authors: Li, G., Yang, Y., Li, S., Qu, X., Lyu, N., & Li, S. E.
Affiliations: Shenzhen University (CN)
Tsinghua University (CN)
Wuhan University of Technology (CN)
Corresponding author: Qu, Xingda
ORCID: Li, Guofa
Yang, Yifan
Li, Shen
Qu, Xingda
Lyu, Nengchao
Li, Shengbo Eben
Venue: Transportation Research Part C: Emerging Technologies
Publisher: Elsevier BV
Published: 2022-01
Volume: 134
Pages: 103452
ISSN: 0968-090X (ISSN-L)
DOI: 10.1016/j.trc.2021.103452
OpenAlex: W3211345831
Cited by: 308
References: 65
Citations by year: 2026: 39 · 2025: 86 · 2024: 85 · 2023: 64 · 2022: 32
FWCI: 14.86
Type: journal article
Language: en
Open access: closed
Licence: www.elsevier.com/tdm/userlicense/1.0/ (tdm)
www.elsevier.com/legal/tdmrep-license (tdm)
doi.org/10.15223/policy-017 (stm-asf)
doi.org/10.15223/policy-037 (stm-asf)
doi.org/10.15223/policy-012 (stm-asf)
doi.org/10.15223/policy-029 (stm-asf)
doi.org/10.15223/policy-004 (stm-asf)
Locations: Transportation Research Part C Emerging Technologies (publishedVersion)
Funders: National Natural Science Foundation of China (51805332)
Shenzhen Fundamental Research and Discipline Layout project (JCYJ20190808142613246)
Topics: Physical Sciences › Engineering › Automotive Engineering › Autonomous Vehicle Technology and Safety
Physical Sciences › Computer Science › Artificial Intelligence › Reinforcement Learning in Robotics
Physical Sciences › Engineering › Safety, Risk, Reliability and Quality › Traffic and Road Safety
Keywords: autonomous vehicles, deep reinforcement learning, driving risk assessment, probabilistic risk model, position uncertainty, expected risk minimization, driving safety, CARLA simulator

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Decision making of autonomous vehicles in lane change scenarios: Deep reinforcement learning approaches with risk awareness

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