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Crash Prediction and Avoidance by Identifying and Evaluating Risk Factors from Onboard Cameras

Qin, Ruwen
2020
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Description

Abstract: This project seamlessly integrates data analytics, deep learning, and computer vision technology to achieve the goal. Specifically, the project creates a crash report dataset trimmed from FARS, and analyzes the data to identify a set of risk factors that contribute to crashes and assess their significance levels.

Details

Title
Crash Prediction and Avoidance by Identifying and Evaluating Risk Factors from Onboard Cameras
Creator
Qin, Ruwen (Mid-America Transportation Center)
Contributor
Qin, Ruwen Contact Person (Mid-America Transportation Center)
Date
2020-10-09
Publisher
University of Nebraska-Lincoln
Subject
Keyword
Resource Type General
Dataset
Resource Type
Image
Record Created
2024-11-21
Record ID
35
System Control Number
667009
DOI
https://doi.org/10.32873/unl.dr.20201009
File Format
JPG
Version
1
Language
English
Relevant Dates
Submitted: 2020-10-09
Updated: 2026-02-10
Grant Information
U.S. Department of Transportation, 69A3551747107
Usage Statement
Creative Commons Attribution (CC BY) 4.0 International, (https://creativecommons.org/licenses/by/4.0/)
Record Appears in
Mid-America Transportation Center
All Records
Version
1
Division
Engineering
Department
Civil and Environmental Engineering
Center or Institute or Core
Nebraska Transportation Center
Mid-America Transportation Center
Citation Note
Qin, Ruwen (2020): Crash Prediction and Avoidance by Identifying and Evaluating Risk Factors from Onboard Cameras. University of Nebraska-Lincoln. (dataset). https://doi.org/10.32873/unl.dr.20201009

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