FARSA: Fully automated roadway safety assessment

Weilian Song, Scott Workman, Armin Hadzic, Xu Zhang, Eric Green, Mei Chen, Reginald Souleyrette, Nathan Jacobs

Research output: Chapter in Book/Report/Conference proceedingConference contributionpeer-review

8 Scopus citations

Abstract

This paper addresses the task of road safety assessment. An emerging approach for conducting such assessments in the United States is through the US Road Assessment Program (usRAP), which rates roads from highest risk (1 star) to lowest (5 stars). Obtaining these ratings requires manual, fine-grained labeling of roadway features in streetlevel panoramas, a slow and costly process. We propose to automate this process using a deep convolutional neural network that directly estimates the star rating from a street-level panorama, requiring milliseconds per image at test time. Our network also estimates many other roadlevel attributes, including curvature, roadside hazards, and the type of median. To support this, we incorporate taskspecific attention layers so the network can focus on the panorama regions that are most useful for a particular task. We evaluated our approach on a large dataset of real-world images from two US states. We found that incorporating additional tasks, and using a semi-supervised training approach, significantly reduced overfitting problems, allowed us to optimize more layers of the network, and resulted in higher accuracy.

Original languageEnglish
Title of host publicationProceedings - 2018 IEEE Winter Conference on Applications of Computer Vision, WACV 2018
Pages521-529
Number of pages9
ISBN (Electronic)9781538648865
DOIs
StatePublished - May 3 2018
Event18th IEEE Winter Conference on Applications of Computer Vision, WACV 2018 - Lake Tahoe, United States
Duration: Mar 12 2018Mar 15 2018

Publication series

NameProceedings - 2018 IEEE Winter Conference on Applications of Computer Vision, WACV 2018
Volume2018-January

Conference

Conference18th IEEE Winter Conference on Applications of Computer Vision, WACV 2018
Country/TerritoryUnited States
CityLake Tahoe
Period3/12/183/15/18

Bibliographical note

Publisher Copyright:
© 2018 IEEE.

ASJC Scopus subject areas

  • Computer Vision and Pattern Recognition
  • Computer Science Applications

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