An Exploratory Analysis of Crashes Involving Autonomous Vehicles
Publication: International Conference on Transportation and Development 2023
ABSTRACT
Autonomous vehicles (AVs) have the potential to lower the number of vehicular crashes, as research has shown that most collisions are caused by driver error. Consequently, several companies in the US and other countries have conducted pilot tests on public highways to expedite the mainstream deployment of AVs and examine the effects that they have on traffic safety. Although a few studies have used AV crash data to attempt this task, their sample sizes were too small, and they failed to explore many of the factors causing the crashes. This study aims to analyze the characteristics of the collisions in terms of frequency per annum; company and make of AV; and vehicular, environmental, and roadway factors. The crash data, gathered for this research from the California Department of Motor Vehicles, pertains to the characteristics of 259 collisions involving AVs that occurred in California between September 2014 and June 2020 and reveals a sharp increase in the number of crashes after 2017. AVs were at fault for a small proportion of them, as most of them were rear-end collisions that occurred when they were operating in the autonomous mode, and the number of passengers sustaining a major injury was very low. Environmental factors such as weather and lightening did not have a significant impact, as the data shows that most of the accidents occurred in clear weather during daylight hours. The results of this study will help transportation experts more fully understand the advantages and disadvantages of autonomous vehicles by providing a comprehensive picture of collisions involving them.
Get full access to this article
View all available purchase options and get full access to this chapter.
REFERENCES
Alambeigi, H., McDonald, A. D., and Tankasala, S. R. 2020. Crash Themes in Automated Vehicles: A Topic Modeling Analysis of the California Department of Motor Vehicles Automated Vehicle Crash Database.
Ashraf, M. T. 2021. Identification of Crash Contributing Factors in AV involved Crashes. Master Thesis, West Virginia University.
Boggs, A. M., Wali, B., and Khattak, A. J. 2020. Exploratory analysis of automated vehicle crashes in California: A text analytics & hierarchical Bayesian heterogeneity-based approach. Accident Analysis & Prevention, 135, p.105354.
Blincoe, L., Miller, T. R., Zaloshnja, E., and Lawerence, B. A. 2015. The Economic and Societal Impact of Motor Vehicle Crashes, 2010 (Revised).
Carsten, O., Lai, F. C., Barnard, Y., Jamson, A. H., and Merat, N. 2012. Control task substitution in semi-automated driving: does it matter what aspects are automated? Hum. Factors 54 (5), 747–761.
Chand, S., and Dixit, V. V. 2018. Application of Fractal theory for crash rate prediction: insights from random parameters and latent class tobit models. Accid. Anal. Prev. 112, 30–38.
Combs, T. S., Sandt, L. S., Clamann, M. P., and McDonald, N. C. 2019. Automated vehicles and pedestrian safety: exploring the promise and limits of pedestrian detection. Am. J. Prev. Med. 56 (1), 1–7. https://doi.org/10.1016/j.amepre.2018.06.024.
Das, S., Dutta, A., and Tsapakis, I. 2020. Automated vehicle collisions in California: Applying Bayesian latent class model. IATSS research, 44(4), pp.300–308.
Dixit, V. V., Chand, S., and Nair, D. J. 2016. Autonomous vehicles: Disengagements, accidents and reaction times. PLoS ONE, 11(12) e0168054.
Favarò, F. M., Nader, N., Eurich, S. O., Tripp, M., and Varadaraju, N. 2017. Examining accident reports involving autonomous vehicles in California. PLoS one, 12(9), p.e0184952.
Jamson, A. H., Merat, N., Carsten, O. M., and Lai, F. C. 2013. Behavioral changes in driver experiencing highly-automated vehicle control in varying traffic conditions. Transp. Res. Part C Emerg. Technol. 30, 116–125.
Gucwa, M. 2014. Mobility and energy impacts of automated cars. Paper Presented at the Proceedings of the Automated Vehicles Symposium, San Francisco.
Yu, H., Tak, S., Park, M., ancd Yeo, H. 2019. Impact of automated-vehicle-only lanes inmixed traffic conditions, Transp. Res. Rec. 2673 (9), 430–439.
Kockelman, K., Avery, P., Bansal, P., Boyles, S. D., Bujanovic, P., and Choudhary, T. 2016. Implications of Connected and Automated Vehicles on the Safety and Operations of Roadway Networks: A Final Report.
Litman, T. 2014. Autonomous Vehicle Implementation Predictions. Victoria Transport Policy Institute, pp. 28.
Li, S. E., Zheng, Y., Li, K., Wu, Y., Hedrick, J. K., and Gao, F. 2017. Dynamical modeling and distributed control of connected and automated vehicles: Challenges and opportunities. IEEE Intelligent Transportation Systems Magazine, 9(3), 46–58.
Lodinger, N. R., and DeLucia, P. R. (2019). Does automated driving affect time-to-collision judgments? Transport. Res. F: Traffic Psychol. Behav. 64 (2019) 25–37.
Merat, N., Jamson, A. H., Lai, F. C., Daly, M., and Carsten, O. M. 2014. Transition to manual: driver behaviour when resuming control from a highly automated vehicle. Transp. Res. Part F Traffic Psychol. Behav. 27, 274–282.
Navarro, J., François, M., and Mars, F. 2016. Obstacle avoidance under automated steering: impact on driving and gaze behaviours, Transport. Res. F: Traffic Psychol. Behav. 43, 315–324.
National Center for Statistics and Analysis. 2017. 2016 Motor Vehicle Crashes: Overview. Washington, DC.
Teoh, E. R., and Kidd, D. G. 2017. Rage against the machine? Google’s self-driving cars versus human drivers. Journal of Safety Research, 63, 57.
Tran, N. 2018. Global status report on road safety 2018. Geneva: World Health Organization, 2018.
SAE. 2014. Taxonomy and Definitions for Terms Related to On-Road Motor Vehicle Automated Driving Systems.
Schoettle, B., and Sivak, M. 2015. A preliminary analysis of real-world crashes involving self-driving vehicles. Ann Arbor, MI: University of Michigan Transportation Research Institute (2015).
Sinha, A., Chand, S., Vu, V., Chen, H., and Dixit, V. 2021. Crash and disengagement data of autonomous vehicles on public roads in California. Scientific data, 8(1), pp.1–10.
Wang, S., and Li, Z. 2019. Exploring the mechanism of crashes with automated vehicles using statistical 528 modeling approaches. PLoS ONE, 14(3). https://doi.org/10.1371/journal.pone.0214550.
Wiseman, Y. 2022. “Autonomous vehicles.” Research Anthology on Cross-Disciplinary Designs and Applications of Automation, IGI Global, 878–889.
Ye, W., Wang, C., Chen, F., Yan, S., and Li, L. 2021. Approaching autonomous driving with cautious optimism: analysis of road traffic injuries involving autonomous vehicles based on field test data. Injury prevention, 27(1), pp.42–47.
Information & Authors
Information
Published In
International Conference on Transportation and Development 2023
Pages: 343 - 350
History
Published online: Jun 13, 2023
ASCE Technical Topics:
- Business management
- Driver behavior
- Engineering fundamentals
- Errors (statistics)
- Highway and road management
- Highway transportation
- Highways and roads
- Infrastructure
- Mathematics
- Practice and Profession
- Public administration
- Public health and safety
- Statistics
- Traffic accidents
- Traffic engineering
- Traffic management
- Traffic safety
- Transportation engineering
- Vehicles
Authors
Metrics & Citations
Metrics
Citations
Download citation
If you have the appropriate software installed, you can download article citation data to the citation manager of your choice. Simply select your manager software from the list below and click Download.