Advancing Crash Investigation With Connected and Automated Vehicle Data

Clamann, Michael; Khattak, Asad J.; Clark, Kinzee; University of North Carolina at Chapel Hill; University of Tennessee, Knoxville. Center for Transportation Research

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Advancing Crash Investigation With Connected and Automated Vehicle Data

2021-07-07
Datasets Available
By Clamann, Michael ; Khattak, Asad J. ; Clark, Kinzee

[PDF-1.07 MB]

English

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Details:

Creators:

Clamann, Michael ; Khattak, Asad J. ; Clark, Kinzee

Clamann, Michael ; Khattak, Asad J. ; Clark, Kinzee Less -
Corporate Creators:

University of North Carolina at Chapel Hill ; University of Tennessee, Knoxville. Center for Transportation Research

University of North Carolina at Chapel Hill ; University of Tennessee, Knoxville. Center for Transportation Research Less -
Corporate Contributors:

United States. Department of Transportation. University Transportation Centers (UTC) Program ; United States. Department of Transportation. Office of the Assistant Secretary for Research and Technology ; United States. Department of Transportation. Federal Highway Administration

United States. Department of Transportation. University Transportation Centers (UTC) Program ; United States. Department of Transportation. Office of the Assistant Secretary for Research and Technology ; United States. Department of Transportation. Federal Highway Administration Less -
Subject/TRT Terms:

[+]

Accident Investigation Advanced Driver Assistance Systems Autonomous Vehicles Connected And Automated Vehicles Connected Vehicles Crash Data Data Collection Driver Support Systems Event Data Recorders Law Enforcement Text Analysis
Publication/ Report Number:

CSCRS-R25.1
Resource Type:

Other
Geographical Coverage:

United States
Edition:

Final Report: May 2019-Aug 2020; Slide Deck, Research Brief
Corporate Publisher:

Collaborative Sciences Center for Road Safety
Abstract:

Understanding the contributing factors in more than 6 million vehicle crashes that occur annually in the U.S. is very challenging, and police officers investigating crashes need all the tools they can use to reconstruct the crash. Given that the Connected and Automated Vehicle (CAV) era is rapidly unfolding, this study seeks to leverage newly available CAV data to improve crash investigation procedures and obtain input from stakeholders, specifically law enforcement. In particular, law enforcement use of existing Event Data Recorders (EDRs), which store vehicle kinematics during a crash, is explored. Crash investigations are currently aided by EDRs, but this aid could be expanded to include the information gathered by Automated Driving System (ADS) technologies such as radar, cameras, LiDAR, infrared, and ultrasonic. This detailed data could improve the fidelity of future crash investigations, with potential new information such as driver/operator state, vehicle automation capabilities, location, objects and people in the immediate area, performance and diagnostic data, and environmental factors. Through text mining analysis of CAV and sensor-related literature and interviews with law enforcement, this study contributes by gathering evidence about crash investigations to pinpoint the contributing factors of a crash. Further we explore law enforcement involvement in the design of the current EDR retrieval process and their knowledge about using ADS data. Broadly, the project applies the safe systems approach by suggesting a framework that integrates CAV data in the new crash investigation procedures.
Format:

PDF
Funding:

Collaborative Sciences Center for Road Safety (Grant: 69A3551747113)
Collection(s):

University Transportation Centers
Main Document Checksum:

[+]

urn:sha256:d1fbdc51981da6173033deb17e9be0238ffb0ceb2bf613a0311d474bd1049792
Download URL:

https://rosap.ntl.bts.gov/view/dot/63322/dot_63322_DS1.pdf
File Type:

	Slide Deck	pdf
	Brief	pdf

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