Deep learning serves traffic safety analysis: A forward‐looking review-Reference-Cited by-同舟云学术

Deep learning serves traffic safety analysis: A forward‐looking review

Published:2022-08-27 Issue:1 Volume:17 Page:22-71
ISSN:1751-956X
Container-title:IET Intelligent Transport Systems
language:en
Short-container-title:IET Intelligent Trans Sys

Author:

Razi Abolfazl¹,Chen Xiwen¹^ORCID,Li Huayu²,Wang Hao¹,Russo Brendan³,Chen Yan⁴,Yu Hongbin⁵

Affiliation:

1. School of Computing Clemson University Clemson SC U. S. A.

2. Department of Electrical and Computer Engineering University of Arizona Tucson AZ U. S. A.

3. Department of Civil Engineering Northern Arizona University Flagstaff AZ U. S. A.

4. The Polytechnic School Arizona State University Mesa AZ U. S. A.

5. School of Electrical Computer and Energy Engineering Arizona State University Tempe AZ U. S. A.

Funder

National Science Foundation

Publisher

Institution of Engineering and Technology (IET)

Subject

Law,Mechanical Engineering,General Environmental Science,Transportation

Link

https://onlinelibrary.wiley.com/doi/pdf/10.1049/itr2.12257

Reference620 articles.

1. Global Status Report on Road Safety.World Health Organization(2018).https://apps.who.int/iris/rest/bitstreams/1164010/retrieve. Accessed 22 August 2022

2. https://www.statefarm.com/simple‐insights/auto‐and‐vehicles/latest‐car‐safety‐features‐becoming‐musthaves. Accessed 17 January 2021

3. Dinita M.:Best road design software for PC [2020 Guide](2019).https://windowsreport.com/road‐design‐software/. Accessed 17 January 2021

4. https://www.bentley.com/en/solutions/road‐design‐and‐analysis. Accessed 17 January 2021

5. Kirkland G.:How new technologies have changed the automotive industry(2019).https://www.oponeo.co.uk/blog/how‐new‐technologies‐have‐changed‐the‐automotive‐industry. Accessed 17 January 2021

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