Exploratory Investigation of Disengagements and Crashes in Autonomous Vehicles Under Mixed Traffic: An Endogenous Switching Regime Framework

Journal Article · Thu Jul 16 00:00:00 EDT 2020 · IEEE Transactions on Intelligent Transportation Systems

DOI:https://doi.org/10.1109/tits.2020.3003527· OSTI ID:1649156

Khattak, Zulqarnain H. ^[1]; Fontaine, Michael D. ^[2]; Smith, Brian L. ^[3]

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1. Oak Ridge National Lab. (ORNL), Oak Ridge, TN (United States). Energy and Transportation Science Division
2. Virginia Transportation Research Council, Charlottesville, VA (United States)
3. Univ. of Virginia, Charlottesville, VA (United States). Dept. of Engineering Systems and Environment

Autonomous Vehicles (AVs) have a large potential to improve traffic safety but also pose some critical challenges. While AVs may help reduce crashes caused by human error, they still may experience failures of technologies and sensing, as well as decision-making errors in a mixed traffic environment. A disengagement refers to an AV transitioning control from autonomous systems to the trained test driver. The safety critical nature of disengagements makes it imperative to analyze disengagements and crashes together. In this study, we analyze both crashes and disengagements from real-world AV driving in California to fill the knowledge gap regarding the relationship between disengagements and crashes in a mixed traffic environment. A nested logit model was calibrated using three different outcomes: (1) disengagement with a crash, (2) disengagement with no crash, and (3) no disengagement with a crash. Furthermore, endogenous switching regime models were also calibrated to draw distinctions between the relation of disengagements and crashes while accounting for endogeneity effects. The results show that factors related to AV systems (such as software failures) and other roadway participants increase the propensity of a disengagement without a crash. Furthermore, AVs were observed to disengage less often as the technology matured over time. Marginal effects revealed an 8% decrease. The results thus suggest that disengagements are a part of AVs' safe performance and disengagement alerts may need to be triggered in order to avoid certain failures with current technology.

• Research Organization: Oak Ridge National Laboratory (ORNL), Oak Ridge, TN (United States)
• Sponsoring Organization: USDOE
• Grant/Contract Number: AC05-00OR22725
• OSTI ID: 1649156
• Journal Information: IEEE Transactions on Intelligent Transportation Systems, Journal Name: IEEE Transactions on Intelligent Transportation Systems Journal Issue: 12 Vol. 22; ISSN 1524-9050
• Publisher: IEEE
• Country of Publication: United States
• Language: English

Related Subjects

42 ENGINEERING
autonomous vehicles
computer crashes
disengagements
endogenous switching regime
google
nested logit model
roads
safety
safety critical events
software and sensor failure.
testing
vehicle crash testing
vehicles