Comparison of automated vehicle struck-from-behind crash rates with national rates using naturalistic data
Introduction
Vehicles with automated driving features, defined here as combined lateral and longitudinal control or SAE Levels 3–5 (SAE International, 2018), have been tested on public roads in the United States nearly continuously since 2010 (Beiker, 2014). In September 2014, the California Department of Motor Vehicles began requiring companies wishing to test AVs on public roads to obtain permits (California Department of Motor Vehicles, 2018). All permit holders were required to report all crashes using form OL 316 within 10 days of the incident, regardless of severity or whether the vehicle was under human or computer control. Permit holders were also required report all test vehicles, miles driven in autonomous mode, and disengagements of the autonomous driving system by January 1st for the prior period of December to November. The combined public data sets of vehicle crashes and mileages for all autonomous vehicle testing in California proved a valuable resource for researchers, and several studies have shown that automated vehicle (AV) crash rates are lower than general public crash rates when controlling for crash severity (Blanco et al., 2016; Teoh and Kidd, 2017). Studies have also reported that a majority of AV crashes involve the automated vehicle being struck from behind (Leilabadi and Schmidt, 2019). As a proportion of total crashes, these are far higher than those reported by the driving public (Favarò et al., 2017).
Struck-from-behind crashes are a significant economic and public health concern. Rear end collisions account for 32.3 % of all crashes in the United States (National Highway Traffic Safety Administration, 2020a, p. 29). Economic losses from whiplash injuries were estimated as $2.7 B nationally in 2002 dollars ($3.9 B in 2020) (“Federal Motor Vehicle Safety Standards; Head Restraints,” 2010).
Previous studies have investigated the AV crash rates generally, but none have compared AV and human-driven struck-from-behind crash rates. The purposes of this study are to determine whether AVs are struck from behind at higher rates per distance traveled than conventional vehicles, and to investigate potential causes.
Section snippets
Literature review
Several studies have analyzed automated vehicle crash records, differing in the methods and metrics. Table 1 shows an overview of automated vehicle crash studies and comparisons in the literature. Some studies did not calculate crash rates but instead performed exploratory analysis (Das et al., 2020), text mined crash narratives (Alambeigi et al., 2020; Boggs et al., 2020b), or modeled crash severity (Wang and Li, 2019). Other studies calculated automated vehicle crash rates but did not compare
Materials and methods
Relevant data is publicly accessible and available for download (Goodall, 2020).
Results and discussion
Automated vehicle crash rates per million vehicle-miles traveled were compared with crash rates of human-driven vehicles from the SHRP 2 NDS dataset. Table 3 shows an overview of the crash rate comparisons.
Conclusions
Automation of the driving will profoundly affect transportation both in terms of mobility and safety. Understanding the performance of automated driving systems, especially early deployments, is of profound importance. While previous studies have found AV crashes to occur at lower rates than those of human-driven vehicles, this is the first study to investigate struck-from-behind crashes which represent 73 % of autonomous-mode crashes. While automated vehicles are struck from behind at higher
Funding
This work was sponsored by the Virginia Department of Transportation, United States.
CRediT authorship contribution statement
Noah J. Goodall: Conceptualization, Methodology, Software, Formal analysis, Investigation, Data curation, Writing - original draft, Writing - review & editing, Visualization.
Declaration of Competing Interest
The author has no known competing financial interests or personal relationships that could have appeared to influence the work reported in this paper.
Acknowledgements
Special thanks to Jeremy Sudweeks for clarifying total mileage figures in the SHRP 2 Naturalistic Driving Study, Dr. Michael Fontaine for comments on an earlier draft of this paper, and Dr. Subasish Das for providing a database of automated vehicle crashes in California through March 2020.
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