References
- Alambeigi, H., McDonald, A. D., Tankasala, S. R. (2020). Crash themes in automated vehicles: a topic modeling analysis of the California Department of Motor Vehicles automated vehicle crash database. ArXiv:2001.11087 [Stat]. http://arxiv.org/abs/2001.11087
- Antin, J. F., Lee, S., Perez, M. A., Dingus, T. A., Hankey, J. M., & Brach, A. (2019). Second strategic highway research program naturalistic driving study methods. Safety Science, 119, 2–10. doi:10.1016/j.ssci.2019.01.016
- Blanco, M., Atwood, J., Russell, S., Trimble, T., McClafferty, J., & Perez, M. (2016). Automated vehicle crash rate comparison using naturalistic data. Virginia Tech Transportation Institute.
- Blincoe, L. J., Miller, T. R., Zalashnja, E., & Lawrence, B. A. (2015). The Economic and Societal Impact of Motor Vehicle Crashes, 2010 (Revised). (DOT HS 812 013). National Highway Traffic Safety Administration. https://crashstats.nhtsa.dot.gov/Api/Public/ViewPublication/812013
- Boggs, A. M., Wali, B., & Khattak, A. J. (2020). Exploratory analysis of automated vehicle crashes in California: A text analytics & hierarchical Bayesian heterogeneity-based approach. Accident; Analysis and Prevention, 135, 105354. doi:10.1016/j.aap.2019.105354
- California Department of Motor Vehicles. (2018). Title 13, Division 1, Chapter 1, Article 3.7 – Testing of Autonomous Vehicles. https://www.dmv.ca.gov/portal/uploads/2020/06/Adopted-Regulatory-Text-2019.pdf
- Das, S., Dutta, A., & Tsapakis, I. (2020). Automated vehicle collisions in California: Applying Bayesian latent class model. IATSS Research, 44(4), 300–308. doi:10.1016/j.iatssr.2020.03.001
- Ding, L., Glazer, M., Wang, M., Mehler, B., Reimer, B., & Fridman, L. (2020). MIT-AVT clustered driving scene dataset: Evaluating perception systems in real-world naturalistic driving scenarios [Paper presentation]. 2020 IEEE Intelligent Vehicles Symposium (IV), pp. 232–237. doi:10.1109/IV47402.2020.9304677
- Dixit, V. V., Chand, S., & Nair, D. J. (2016). Autonomous vehicles: Disengagements, accidents and reaction times. PloS One, 11(12), e0168054. doi:10.1371/journal.pone.0168054
- Eustace, D., & Wei, H. (2010). The role of driver age and gender in motor vehicle fatal crashes. Journal of Transportation Safety & Security, 2(1), 28–44. doi:10.1080/19439961003590811
- Evans, L. (1994). Driver injury and fatality risk in two-car crashes versus mass ratio inferred using Newtonian mechanics. Accident; Analysis and Prevention, 26(5), 609–616. doi:10.1016/0001-4575(94)90022-1
- Fridman, L., Brown, D. E., Kindelsberger, J., Angell, L., Mehler, B., Reimer, B. (2018). Human side of tesla autopilot: Exploration of functional vigilance in real-world human-machine collaboration. https://www.coursehero.com/file/68234521/Case-Study-1pdf/
- Gershon, P., Seaman, S., Mehler, B., Reimer, B., & Coughlin, J. (2021). Driver behavior and the use of automation in real-world driving. Accident; Analysis and Prevention, 158, 106217. doi:10.1016/j.aap.2021.106217
- Goodall, N. J. (2021). Comparison of automated vehicle struck-from-behind crash rates with national rates using naturalistic data. Accident; Analysis and Prevention, 154, 106056. doi:10.1016/j.aap.2021.106056
- Hardman, S., Lee, J. H., & Tal, G. (2019). How do drivers use automation? Insights from a survey of partially automated vehicle owners in the United States. Transportation Research Part A: Policy and Practice, 129, 246–256. doi:10.1016/j.tra.2019.08.008
- Jing, L., Shan, W., & Zhang, Y. (2022). Risk preference, risk perception as predictors of risky driving behaviors: The moderating effects of gender, age, and driving experience. Journal of Transportation Safety & Security, 1–26. doi:10.1080/19439962.2022.2086953
- Kalra, N., & Paddock, S. M. (2016). Driving to safety: How many miles of driving would it take to demonstrate autonomous vehicle reliability? Transportation Research Part A: Policy and Practice, 94, 182–193. doi:10.1016/j.tra.2016.09.010
- Kane, M. (2021). Tesla vehicle safety report Q2 2021: Numbers are down again. InsideEVs. https://insideevs.com/news/537818/tesla-autopilot-safety-report-2021q2/
- Leilabadi, S. H., & Schmidt, S. (2019). In-depth analysis of autonomous vehicle collisions in California [Paper presentation]. 2019 IEEE Intelligent Transportation Systems Conference (ITSC), pp. 889–893. doi:10.1109/ITSC.2019.8916775
- Liu, P., Yang, R., & Xu, Z. (2019). How safe is safe enough for self-driving vehicles? Risk Analysis : An Official Publication of the Society for Risk Analysis, 39(2), 315–325. doi:10.1111/risa.13116
- M. Davis and Company, Inc. (2015). National telephone survey of reported and unreported motor vehicle crashes. (DOT HS 812 183). National Highway Traffic Safety Administration. http://www-nrd.nhtsa.dot.gov/Pubs/812183.pdf
- McCarthy, R. L. (2022). Autonomous vehicle (AV) accident data analysis: California OL 316 reports: 2015-2020. ASCE-ASME Journal of Risk and Uncertainty in Engineering Systems Part B. Mechanical Engineering, 8(3), 034502. doi:10.1115/1.4051779
- Morando, A., Gershon, P., Mehler, B., Reimer, B. (2020). Driver-initiated Tesla Autopilot disengagements in naturalistic driving. 12th International Conference on Automotive User Interfaces and Interactive Vehicular Applications, pp. 57–65. doi:10.1145/3409120.3410644
- National Conference of State Legislatures. (2019). Autonomous vehicles | Self-driving vehicles enacted legislation [Paper presentation]. National Conference of State Legislatures. http://www.ncsl.org/research/transportation/autonomous-vehicles-self-driving-vehicles-enacted-legislation.aspx
- National Highway Traffic Safety Administration. (2021a). Voluntary safety self-assessment [Text]. https://www.nhtsa.gov/automated-driving-systems/voluntary-safety-self-assessment
- National Highway Traffic Safety Administration. (2021b). Standing general order 2021-01: Incident reporting for automated driving systems and level 2 advanced driver assistance systems [Text]. https://www.nhtsa.gov/sites/nhtsa.gov/files/2021-06/Standing_General_Order_2021_01-digital-06292021.pdf
- Reagan, I. J., Hu, W., Cicchino, J. B., Seppelt, B., Fridman, L., & Glazer, M. (2019). Measuring adult drivers’ use of Level 1 and 2 driving automation by roadway functional class. Proceedings of the Human Factors and Ergonomics Society Annual Meeting, 63(1), 2093–2097. doi:10.1177/1071181319631225
- Reimer, B. (2021). Personal communication.
- SAE International. (2018). Taxonomy and definitions for terms related to driving automation systems for on-road motor vehicles (SAE J3016_201806). SAE International.
- Schoettle, B., & Sivak, M. (2015). A preliminary analysis of real-world crashes involving self-driving vehicles (UMTRI-2015-34). University of Michigan Transportation Research Institute. http://www.umich.edu/∼umtriswt/PDF/UMTRI-2015-34_Abstract_English.pdf
- Templeton, B. (2020). New Tesla Autopilot Statistics Show It’s Almost As Safe Driving With It As Without. Forbes. https://www.forbes.com/sites/bradtempleton/2020/10/28/new-tesla-autopilot-statistics-show-its-almost-as-safe-driving-with-it-as-without/
- Teoh, E. R., & Kidd, D. G. (2017). Rage against the machine? Google’s self-driving cars versus human drivers. Journal of Safety Research, 63, 57–60. doi:10.1016/j.jsr.2017.08.008
- Tesla, Inc. (2021). Tesla vehicle safety report. https://www.tesla.com/VehicleSafetyReport
- Tesla, Inc. (2022). Safety. Tesla. https://www.tesla.com/safety
- Transportation Research Board of the National Academy of Sciences. (2013). The 2nd strategic highway research program naturalistic driving study dataset. https://insight.shrp2nds.us/
- Virginia Tech Transportation Institute. (2015). SHRP2 researcher dictionary for video reduction data version 3.4. Virginia Tech Transportation Institute. https://drive.google.com/file/d/0B0WDAFahhsCGX05hM3U4OVVjZUk/view?usp=sharing&usp=embed_facebook
- Wang, S., & Li, Z. (2019). Exploring the mechanism of crashes with automated vehicles using statistical modeling approaches. PLoS One. 14(3), e0214550. doi:10.1371/journal.pone.0214550