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

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. The hypothesis is that crash avoidance features are becoming more effective over time and helping to reduce the severity and frequency of crashes. However, there are not many frameworks and tools to help state and local agencies assess the private and societal cost and benefits of increased market adoption and how many lives have been saved by these technologies. The research team also hypothesizes that the trend of increasing vehicle weight and size increases the severity of crashes. This paper builds off previous UTC research by starting with a method similar to Harper et al. (2016) and Khan et al. (2019) but uses more recent insurance and crash data, contributes estimates of lives saved in addition to private net benefits and overall societal net benefits, and conducts exploratory analysis on the role that EVs and heavier vehicles play in crash safety. In the first part of this project, the team will compile insurance institute data on crashes and crash severity, helping us to better understand observed changes in crash frequency and severity in vehicles that are equipped with warning and partial automation systems. Second, the team will conduct a cost-benefit analysis to estimate the net-private and net-societal benefits of fleet-wide deployment of existing partial automation and warning systems. Third, the team will develop a method to estimate the number of lives saved by crash avoidance technologies. Finally, the team will conduct exploratory analysis on the role that EVs and heavier vehicles play in crash safety. To do this analysis, the team will utilize several publicly available datasets such as 2022 Fatality Analysis Reporting System and observed insurance data from the Insurance Institute for Highway Safety. In this phase of this project, the team will mainly focus on automation and its effects on crash severity and frequency and will use similar datasets to conduct a more in-depth analysis on how EVs and heavier vehicles affect crash safety in future iterations. By focusing on both technologies independently (i.e., non-EVs with partial automation and EVs without partial automation), we can better understand the different ways they are impacting crash safety and how to mitigate any negative effects through policy. References Harper, C. D., Hendrickson, C. T., & Samaras, C. (2016). Cost and benefit estimates of partially-automated vehicle collision avoidance technologies. Accident Analysis & Prevention, 95, 104-115. Khan, A., Harper, C. D., Hendrickson, C. T., & Samaras, C. (2019). Net-societal and net-private benefits of some existing vehicle crash avoidance technologies. Accident Analysis & Prevention, 125, 207-216.

• Record URL:
  https://ppms.cit.cmu.edu/projects/detail/426

Language

• English

Project

• Status: Active
• Funding: $200000
• Contract Numbers:

  69A3552344811

• Sponsor Organizations:

  Carnegie Mellon University

  Safety21 National UTC for Promoting Safety
  Pittsburgh, PA  United States  15213

  Office of the Assistant Secretary for Research and Technology

  University Transportation Center Program
  ,    
• Managing Organizations:

  Carnegie Mellon University

  Safety21 National UTC for Promoting Safety
  Pittsburgh, PA  United States  15213
• Project Managers:

  Stearns, Amy

• Performing Organizations:

  Carnegie Mellon University

  ,    
• Principal Investigators:

  Harper, Corey

• Start Date: 20230701
• Expected Completion Date: 20240630
• Actual Completion Date: 0
• USDOT Program: University Transportation Centers

Subject/Index Terms

• TRT Terms: Automation; Crash avoidance systems; Crash rates; Crash severity; Electric vehicles; Market penetration; Vehicle size; Vehicle weight
• Subject Areas: Highways; Safety and Human Factors; Vehicles and Equipment;

Filing Info

• Accession Number: 01900238
• Record Type: Research project
• Source Agency: Safety21
• Contract Numbers: 69A3552344811
• Files: UTC, RIP
• Created Date: Nov 20 2023 8:20PM