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Cited by 90Traffic and Road SafetyHuman-Automation Interaction and SafetyAutonomous Vehicle Technology and Safety

Automated Vehicle Crash Rate Comparison Using Naturalistic Data

Blanco, Myra, Atwood, Jon, Russell, Sheldon, Trimble, Tammy E., McClafferty, Julie, Perez, Miguel·2016·Virginia Tech Transportation Institute
DOI 10.13140/rg.2.1.2336.1048
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Abstract

This study assessed driving risk for the United States nationally and for the Google Self-Driving Car project. Driving safety on public roads was examined in three ways. The total crash rates for the Self-Driving Car and the national population were compared to (1) rates reported to the police, (2) crash rates for different types of roadways, and (3) scenarios that give rise to unreported crashes. First, crash rates from the Google Self-Driving Car project per million miles driven, broken down by severity level were calculated. The Self-Driving Car rates were compared to rates developed using national databases which draw upon police-reported crashes and rates estimated from the Second Strategic Highway Research Program (SHRP 2) Naturalistic Driving Study (NDS). Second, SHRP 2 NDS data were used to calculate crash rates for three levels of crash severity on different types of roads, broken down by the speed limit and geographic classification (termed “locality” in the study; e.g., urban road, interstate). Third, SHRP 2 NDS data were again used to describe various scenarios related to crashes with no known police report. This analysis considered whether such factors as driver distraction or impairment were involved, or whether these crashes involved rear-end collisions or road departures. Crashes within the SHRP 2 NDS dataset were ranked according to severity for the referenced event/incident type(s) based on the magnitude of vehicle dynamics (e.g., high Delta-V or acceleration), the presumed amount of property damage (less than or greater than $1,500, airbag deployment), knowledge of human injuries (often unknown in this dataset), and the level of risk posed to the drivers and other road users (Antin, et al., 2015; Table 1). Google Self-Driving Car crashes were also analyzed using the methods developed for the SHRP 2 NDS in order to determine crash severity levels and fault (using these methods, none of the vehicles operating in autonomous mode were deemed at fault in crashes).

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  • Liability claims reporting standards are consistent across ADS and human drivers because liability claims are registered for any motor incident resulting in property damage or bodily injury, regardless of the vehicle's level of automation.

    “Unlike police-reported collisions which have varying severity thresholds across ADS and human drivers [3,6], liability claims are traditionally registered for any motor incident resulting in property damage or bodily injury, regardless of the vehicle's level of automation.”

    From Comparative safety performance of autonomous- and human drivers: A real-world case study of the Waymo Driver (2024) · cites this paper as [3]

  • Valid safety comparisons must select an appropriate safety performance metric, overcome differences in collision reporting standards between ADS and human-driven vehicles, account for the underreporting of human driving collisions, set a safety-relevant collision severity threshold, use human driver data specific to the region in which the ADS operates while maintaining robustness, and apply a统计方法

    “Valid safety comparisons must select an appropriate safety performance metric, overcome differences in collision reporting standards between ADS and human-driven vehicles (HDV), account for the underreporting of human driving collisions, and set a safety-relevant collision severity threshold. Furthermore, they should use human driver data specific to the region in which the ADS operates while”

    From Comparative safety performance of autonomous- and human drivers: A real-world case study of the Waymo Driver (2024) · cites this paper as [3]

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Authors
Blanco, M., Atwood, J., Russell, S., Trimble, T. E., McClafferty, J., & Perez, M.
Venue
Virginia Tech Transportation Institute
Publisher
Virginia Tech Transportation Institute
Published
2016
DOI
10.13140/rg.2.1.2336.1048
OpenAlex
W2231854570
Cited by
90
References
0
Citations by year
2025: 2 · 2024: 10 · 2023: 10 · 2022: 10 · 2021: 18 · 2020: 13 · 2019: 8 · 2018: 8 · 2017: 8 · 2016: 3
Type
report
Language
en
Open access
closed
Locations
VTechWorks (Virginia Tech) (submittedVersion)
Topics
  • Physical Sciences › Engineering › Safety, Risk, Reliability and Quality › Traffic and Road Safety
  • Social Sciences › Psychology › Social Psychology › Human-Automation Interaction and Safety
  • Physical Sciences › Engineering › Automotive Engineering › Autonomous Vehicle Technology and Safety
Keywords
autonomous vehicles, accident rates, driving risk, naturalistic driving study, SHRP2, police-reported crashes, accident severity, driver distraction, driver impairment, rear-end collisions, road departure, fault identification, vehicle dynamics

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