Author ORCID Identifier

https://orcid.org/0000-0002-8357-4995

Year of Publication

2019

Degree Name

Master of Science (MS)

Document Type

Master's Thesis

College

Engineering

Department

Computer Science

First Advisor

Dr. Nathan Jacobs

Abstract

Road crashes are one of the main causes of death in the United States. To reduce the number of accidents, roadway assessment programs take a proactive approach, collecting data and identifying high-risk roads before crashes occur. However, the cost of data acquisition and manual annotation has restricted the effect of these programs. In this thesis, we propose methods to automate the task of roadway safety assessment using deep learning. Specifically, we trained convolutional neural networks on publicly available roadway images to predict safety-related metrics: the star rating score and free-flow speed. Inference speeds for our methods are mere milliseconds, enabling large-scale roadway study at a fraction of the cost of manual approaches.

Digital Object Identifier (DOI)

https://doi.org/10.13023/etd.2019.136

Funding Information

This research was supported by NSF CAREER grant IIS-1553116 and computing resources provided by the University of Kentucky Center for Computational Sciences, including a hardware donation from IBM.

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