Estimating the Effects of Vehicle Automation and Vehicle Weight and Size on Crash Frequency and Severity: Phase 1

Harper, Corey; Hendrickson, Chris; Yang, Haoming

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Estimating the Effects of Vehicle Automation and Vehicle Weight and Size on Crash Frequency and Severity: Phase 1

2024-07-31
By Harper, Corey ; Hendrickson, Chris ; Yang, Haoming

File Language:

English

Details

Creators:

Harper, Corey ; Hendrickson, Chris ; Yang, Haoming
Corporate Creators:

Carnegie Mellon University ; Carnegie Mellon University. Traffic21 Institute. Safety21 University Transportation Center (UTC)
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
Subject/TRT Terms:

Automatic Braking Automatic Emergency Braking Automation Autonomous Vehicles Crash Avoidance Systems Crash Rates Electric Vehicles Electric Vehicle Safety Intelligent Vehicles Market Penetration Safety Vehicle Size Vehicle Weight Automated Vehicle Safety
Resource Type:

Tech Report
Geographical Coverage:

United States
Edition:

Final Report July 1, 2023 – June 30, 2024
Corporate Publisher:

Carnegie Mellon University. Traffic21 Institute. Safety21 University Transportation Center (UTC)
Abstract:

Most light-duty vehicle (LDV) crashes occur due to human error. The National Highway Safety Administration (NHTSA) reports that eight percent of fatal crashes in 2018 were distraction-affected crashes, while close to ninety-four percent of all crashes occur in part due to human error. Crash avoidance features could reduce both the frequency and severity of light and heavy-duty vehicle crashes, primarily caused by distracted driving behaviors and/or human error by assisting in maintaining control or issuing alerts if a potentially dangerous situation is detected. As the automobile industry transitions to partial vehicle automation, newer crash avoidance technologies are beginning to appear more frequently in non-luxury vehicles such as the Honda Accord and Mazda CX-9. Additionally, the market penetration of electric vehicles (EVs) is increasing, in turn increasing the weight and size of vehicles on the road. This project develops a replicable, open, deployable model that can: 1) estimate the upper-bound crash avoidance potential that could be achieved as the effectiveness of warning and partial automation systems improve and adoption increases, 2) estimate the societal costs and benefits of fleet-wide deployment of crash avoidance technologies considering technology costs and benefits from avoided and less severe crashes, 3) estimate the number of lives that have been saved by forward collision warning, lane departure warning, and blind spot monitoring, and 4) estimate the effects of vehicle weight and size on crash frequency and severity.
Format:

PDF
Funding:

69A3552344811;69A3552348316
Collection(s):

University Transportation Centers Autonomous Vehicles
Main Document Checksum:

urn:sha-512:9f1cc817f4a08a37f17ca673de660c6702f41021d5fb26e4a849b13e1c85c959d6a44fda565ebdc534ea1f795123ae4c47338ad18fe1aacca27f2488c6c9624b
Download URL:

https://rosap.ntl.bts.gov/view/dot/77504/dot_77504_DS1.pdf
File Type:

[PDF - 1.02 MB ]

File Language:

English

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