Vision-Language Models Can Identify Distracted Driver Behavior From Naturalistic Videos-Reference-Cited by-同舟云学术

Vision-Language Models Can Identify Distracted Driver Behavior From Naturalistic Videos

Published:2024-09 Issue:9 Volume:25 Page:11602-11616
ISSN:1524-9050
Container-title:IEEE Transactions on Intelligent Transportation Systems
language:
Short-container-title:IEEE Trans. Intell. Transport. Syst.

Author:

Hasan Md. Zahid¹^ORCID,Chen Jiajing²^ORCID,Wang Jiyang²^ORCID,Rahman Mohammed Shaiqur³,Joshi Ameya⁴^ORCID,Velipasalar Senem²^ORCID,Hegde Chinmay⁴^ORCID,Sharma Anuj⁵,Sarkar Soumik⁶^ORCID

Affiliation:

1. Department of Electrical and Computer Engineering, Iowa State University, Ames, IA, USA

2. Department of Electrical Engineering and Computer Science, Syracuse University, Syracuse, NY, USA

3. Department of Computer Science, Iowa State University, Ames, IA, USA

4. Department of Electrical and Computer Engineering, New York University, Brooklyn, NY, USA

5. Department of Civil Construction and Environmental Engineering, Iowa State University, Ames, IA, USA

6. Department of Mechanical Engineering, Iowa State University, Ames, IA, USA

Funder

Federal Highway Administration Exploratory Advanced Research Program

Publisher

Institute of Electrical and Electronics Engineers (IEEE)

Link

http://xplorestaging.ieee.org/ielx7/6979/10659279/10492662.pdf?arnumber=10492662

Reference80 articles.

1. Overview of the National Highway Traffic Safety Administration's Driver Distraction Program

2. Driver crash risk factors and prevalence evaluation using naturalistic driving data

3. The Impact of Driver Inattention on Near-Crash/Crash Risk: An Analysis Using the 100-Car Naturalistic Driving Study Data

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