Exploring the who, what, when, where, and why of automated vehicle disengagements

• Published in: Accident analysis and prevention 2020-03, Vol.136, p.105406-105406, Article 105406
• Main Authors: Boggs, Alexandra M., Arvin, Ramin, Khattak, Asad J.
• Format: Article
• Language: English
• Subjects: Accidents, Traffic - statistics & numerical data; Automated vehicles; Automation; Automobile Driving - psychology; Automobile Driving - statistics & numerical data; Automobiles - classification; California; California Department of Motor Vehicle; Disengagements; Humans; Man-Machine Systems; Random parameter binary logit; Safety
• ISSN: 0001-4575; 1879-2057
• DOI: 10.1016/j.aap.2019.105406

•Who: Human-initiated disengagements are more likely than automated driving systems (ADS) initiated disengagements.•What/why: ADS-initiated disengagements are mostly due to planning and hardware/software discrepancies.•Where: ADS-initiated disengagements are more likely on streets and roads environments than on high-speed facilities.•When: System maturity has a positive effect on the likelihood of ADS-initiated disengagements. Automated vehicles are emerging on the transportation networks as manufacturers test their automated driving system (ADS) capabilities in complex real-world environments in testing operations like California’s Autonomous Vehicle Tester Program. A more comprehensive understanding of the ADS safety performances can be established through the California Department of Motor Vehicle disengagement and crash reports. This study comprehensively examines the safety performances (159,840 disengagements, 124 crashes, and 3,669,472 automated vehicle miles traveled by the manufacturers) documented since the inauguration of the testing program. The reported disengagements were categorized as control discrepancy, environmental conditions and other road users, hardware and software discrepancy, perception discrepancy, planning discrepancy, and operator takeover. An applicable subset of disengagements was then used to identify and quantify the 5 W’s of these safety-critical events: who (disengagement initiator), when (the maturity of the ADS), where (location of disengagement), and what/why (the facts causing the disengagement). The disengagement initiator, whether the ADS or human operator, is linked with contributing factors, such as the location, disengagement cause, and ADS testing maturity through a random parameter binary logit model that captured unobserved heterogeneity. Results reveal that compared to freeways and interstates, the ADS has a lower likelihood of initiating the disengagement on streets and roads compared to the human operator. Likewise, software and hardware, and planning discrepancies are associated with the ADS initiating the disengagement. As the ADS testing maturity advances in months, the probability of the disengagement being initiated by the ADS marginally increases when compared to human-initiated. Overall, the study contributes by understanding the factors associated with disengagements and exploring their implications for automated systems.