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

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

Integrity signals

Registry

Crossref · 10.1016/j.trc.2021.103452

Cites

63 of 63 references verified0 citation errors · 0 not checkable

Statistics

Recomputing p-values needs the paper’s full text.

Claims the paper makes

We couldn’t retrieve this paper’s full text, so its claims and self-check weren’t run. The registry and citation checks still apply.

Claims other papers make about this one

Each is checked against this paper’s own text: does it say what the citing paper says it does?

  • Research analyzing the level of impact on the driving safety of AVs by ODD is still insufficient.

    “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].”

Cites

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2 of the 65 references couldn’t be matched to a known paper.

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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
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 · 2021: 2
FWCI
14.86
Type
journal article
Language
en
Open access
closed
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

About these checks

Registry, retraction, Cites and statistics results are computed, not judged, with one exception: a printed reference that no index lists is read once by a model before it counts as a citation error. Claims are extracted by a model and kept only when their quote appears verbatim in the text; each cited source is then checked, with a model’s help, for whether it says what the claim relies on. Those verdicts can be wrong — read the claim’s quote and the cited source before relying on one.

Statistics and arithmetic are recomputed, not judged.

Confidence bands are shown only for fields calibrated on our annotated test set. None is calibrated yet, so no band is shown.

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