Exploring the benefits of conversing with a digital voice assistant during automated driving: A parametric duration model of takeover time

Mahajan, Kirti; Large, David R.; Burnett, Gary; Velaga, Nagendra

Abstract

The current study investigated the role of an in-vehicle digital voice-assistant (VA) in conditionally automated vehicles, offering discourse relating specifically to contextual factors, such as the traffic situation and road environment. The study involved twenty-four participants, each taking two drives: with VA and without VA, in a driving simulator. Participants were required to takeover vehicle control following the issuance of a takeover request (TOR) near the end of each drive. A parametric duration model was adopted to find the key factors determining takeover time (TOT). Paired comparisons showed higher alertness and higher active workload (mean NASA-TLX rating) during automation when accompanied by the VA. Paired t-test comparison of gaze behavior prior to takeover showed significantly higher instances of checking traffic signs, roadside objects, and the roadway during the drive with VA, indicating higher situation awareness. The parametric model indicated that the VA increased the likelihood of making a timely takeover by 31%. There was also some evidence that demographic factors influenced the TOT of drivers. Male drivers likely to resume control 1.72 times earlier than female drivers. The study findings highlight the benefits of adopting a futuristic in-car voice assistant to keep the drivers alert and aware about the recent traffic environment in partially AVs.

Citation

Mahajan, K., Large, D. R., Burnett, G., & Velaga, N. (2021). Exploring the benefits of conversing with a digital voice assistant during automated driving: A parametric duration model of takeover time. Transportation Research Part F: Traffic Psychology and Behaviour, 80, 104-126. https://doi.org/10.1016/j.trf.2021.03.012

• Journal Article Type: Article
• Acceptance Date: Oct 3, 2020
• Online Publication Date: Jan 29, 2021
• Publication Date: 2021-07
• Deposit Date: Dec 14, 2020
• Publicly Available Date: Jan 29, 2021
• Journal: Transportation Research Part F: Traffic Psychology and Behaviour
• Print ISSN: 1369-8478
• Electronic ISSN: 1873-5517
• Publisher: Elsevier
• Peer Reviewed: Peer Reviewed
• Volume: 80
• Pages: 104-126
• DOI: https://doi.org/10.1016/j.trf.2021.03.012
• Keywords: Applied Psychology; Transportation; Automotive Engineering; Civil and Structural Engineering
• Public URL: https://nottingham-repository.worktribe.com/output/5147463
• Publisher URL: https://annualmeeting.mytrb.org/OnlineProgram/Details/15855
• Related Public URLs: http://www.trb.org/AnnualMeeting/AnnualMeeting.aspx; https://annualmeeting.mytrb.org/OnlineProgram/Details/15855
• Additional Information: Accepted for oral presentation at conference. Presentation no. TRBAM-21-00712