Traffic Sign Retroreflectivity Condition Assessment and Deterioration Analysis Using Lidar Technology-Reference-Cited by-同舟云学术

Traffic Sign Retroreflectivity Condition Assessment and Deterioration Analysis Using Lidar Technology

Published:2023-05 Issue:5 Volume:149 Page:
ISSN:2473-2907
Container-title:Journal of Transportation Engineering, Part A: Systems
language:en
Short-container-title:J. Transp. Eng., Part A: Systems

Author:

Steele Ariel¹,Pranav Cibi²^ORCID,Tsai Yi-Chang³

Affiliation:

1. Graduate Research Assistant, School of Civil and Environmental Engineering, Georgia Institute of Technology, 788 Atlantic Dr., Atlanta, GA 30332 (corresponding author).

2. Graduate Research Assistant, School of Civil and Environmental Engineering, Georgia Institute of Technology, 788 Atlantic Dr., Atlanta, GA 30332. ORCID: .

3. Professor, School of Civil and Environmental Engineering, Georgia Institute of Technology, 788 Atlantic Dr., Atlanta, GA 30332.

Publisher

American Society of Civil Engineers (ASCE)

Subject

Transportation,Civil and Structural Engineering

Link

https://ascelibrary.org/doi/pdf/10.1061/JTEPBS.TEENG-6921

Reference17 articles.

1. Ai C. 2013. “A sensing methodology for an intelligent traffic sign inventory and condition assessment using GPS/GIS computer vision and mobile lidar technologies.” Doctoral dissertation School of Civil and Environmental Engineering Georgia Institute of Technology.

2. Ai C. and Y. Tsai. 2012. “Critical assessment of an enhanced traffic sign detection using 3D lidar point cloud data.” In Proc. 91st Transportation Research Board Annual Meeting. Washington DC: Transportation Research Board.

3. Critical Assessment of an Enhanced Traffic Sign Detection Method Using Mobile LiDAR and INS Technologies

4. Automated sidewalk assessment method for Americans with disabilities act compliance using three-dimensional mobile lidar;Ai C.;Transp. Res. Rec.,2016

5. An automated sign retroreflectivity condition evaluation methodology using mobile LIDAR and computer vision

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