The DOI matches its Crossref record; 64 of the 65 references it cites were found in a registry or an index.
Decision making of autonomous vehicles in lane change scenarios: Deep reinforcement learning approaches with risk awareness
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“However, several studies have been published on the performance of ADS-mounted sensors, but research analyzing the level of impact on the driving safety of AVs by ODD is still insufficient [16][17][18][19].”
From Evaluation of Autonomous Driving Safety by Operational Design Domains (ODD) in Mixed Traffic (2024) · cites this paper as [17]
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1 of the 65 references couldn’t be matched to a known paper.
Research works printed but not linked: 6
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Metadata
- 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
- Venue
- Transportation Research Part C: Emerging Technologies
- Publisher
- Elsevier BV
- Published
- 2022-01
- Volume
- 134
- Pages
- 103452
- ISSN
- 0968-090X (ISSN-L)
- 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
- 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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