Analysis of Contributing Factors in Crashes Involving Electric Vehicles and Vehicles with Automated Features

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. However, the patterns and characteristics of crashes involving EVs or vehicles automated features have not been explored in much detail. This project develops a replicable, open, deployable model that can: (1) assess the distribution of crashes with automated features across factors such as weather conditions, vehicle speed, crash severity, pre-crash movement, facility type, and time of day, (2) estimate the relationship between contributing factors and the severity of crashes involving vehicles with automated features using regression analysis, and (3) assess the patterns and characteristics of crashes involving EVs. The hypothesis is that current and past automated vehicles (AV) perform well in certain driving scenarios, facility types, and weather conditions but not others. However, there are not many frameworks and tools to help federal, state, and local agencies better understand the causes of crashes involving AVs, the locations of crash hotspots, and the infrastructure improvements and policies that could enhance road safety with AVs. Additionally, this study should help technology providers better understand under which scenarios and conditions further testing and technology development is needed to improve safety and performance. The research team also hypothesizes that the characteristics and patterns of crashes involving EVs differ from crashes not involving this technology. Most studies that have assessed the contributing factors in crashes involving vehicles with automated features have mostly focused on California (e.g., Kutela et al., 2022; Liu et al., 2021, 2024; Xu et al., 2019). This study will add to the previous literature due to the overall comprehensiveness of the crash data, which is representative of all 50 states and the District of Columbia. In the first part of this project, the research team will compile crash data on advanced driver assistance systems (i.e., Level 2 automation) and automated driving systems (i.e., Level 3 and above) to better understand the frequency of crashes with automated features across contributing factors (e.g., weather or vehicle speed). Second, the team will incorporate this data into a regression model to better understand the relationship between the contributing factors and the frequency and severity of crashes. Finally, the team will conduct an exploratory analysis on the contributing factors for crashes involving EVs. To do this analysis, the team will utilize several publicly available datasets such as NHTSA’s AV crash reports, which provides information on crashes involving advanced driver assistance systems and automated driving systems (NHTSA, 2023) and the 2022 Fatality Analysis Reporting System. In this phase of this project, the team will mainly focus on contributing factors for AV crashes and will use similar datasets to conduct a more in-depth analysis on the contributing factors for EV crashes in future iterations. Because of Tesla’s dominance in both the EV and automated vehicle (AV) market space it’s important to consider both the role that EVs and AVs play in crash safety. By focusing on both technologies independently, we can better understand the different ways they are impacting crash safety and how to mitigate any negative effects through policy.

• Record URL:
  • https://ppms.cit.cmu.edu/projects/detail/489

Language

• English

Project

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

  69A3552344811

• Sponsor Organizations:

  Office of the Assistant Secretary for Research and Technology

  University Transportation Centers Program
  Department of Transportation
  Washington, DC  United States  20590
• Managing Organizations:

  Carnegie Mellon University

  Pittsburgh, PA  United States 

  Safety21 University Transportation Center

  Carnegie Mellon University
  Pittsburgh, PA  United States  15213
• Project Managers:

  Stearns, Amy

• Performing Organizations:

  Carnegie Mellon University

  Pittsburgh, PA  United States 

  Safety21 University Transportation Center

  Carnegie Mellon University
  Pittsburgh, PA  United States  15213
• Principal Investigators:

  Harper, Corey

• Start Date: 20240701
• Expected Completion Date: 20250630
• Actual Completion Date: 0
• USDOT Program: University Transportation Centers

Subject/Index Terms

• TRT Terms: Autonomous vehicles; Crash causes; Crash characteristics; Crash severity; Electric vehicles; Regression analysis
• Subject Areas: Highways; Safety and Human Factors; Vehicles and Equipment;

Filing Info

• Accession Number: 01933387
• Record Type: Research project
• Source Agency: Safety21 University Transportation Center
• Contract Numbers: 69A3552344811
• Files: UTC, RIP
• Created Date: Oct 12 2024 12:00PM