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Exploring the benefits of conversing with a digital voice assistant during automated driving: A parametric duration model of takeover time
Affiliation:1. Transportation Systems Engineering, Indian Institute of Technology, Mumbai, India;2. Human Factors Research Group, University of Nottingham, Nottingham, UK;1. University of Twente, Drienerlolaan 5, 7522 NB Enschede, the Netherlands;2. TNO, Anna van Buerenplein 1, 2509 JE, The Hague, the Netherlands;1. Department of Industrial Engineering, Tsinghua University, Beijing, China;2. State Key Laboratory of Automotive Safety and Energy, Tsinghua University, Beijing, China;3. School of Economics and Management, Beijing University of Posts and Telecommunications, Beijing, China;4. College of Mechatronics and Control Engineering, Institute of Human Factors and Ergonomics, Shenzhen University, Shenzhen, China;1. Human Factors Research Group, Faculty of Engineering, University of Nottingham, UK;2. Jaguar Land Rover Research, International Digital Laboratory, Coventry, UK;1. Department of Sociology, Anthropology and Criminal Justice, Clemson University, Clemson, SC, United States;2. School of Civil and Environmental Engineering, Georgia Institute of Technology, Atlanta, GA, United States
Abstract:Vehicle automation allows drivers to disengage from driving causing a potential decline in their alertness. One of the major challenges of highly automated vehicles is to ensure a timely (with respect to safety and situation awareness) takeover in such conditions. For this purpose, the current study investigated the role of an in-vehicle digital voice-assistant (VA) in conditionally automated vehicles, offering spoken discourse relating specifically to contextual factors, such as the traffic situation and road environment. The study involved twenty-four participants, each taking two drives (counterbalanced): 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 signal, 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 39%. There was also some evidence suggesting that male drivers are likely to resume control 1.21 times earlier than female drivers. The study findings highlight the benefits of adopting a digital voice assistant to keep the drivers alert and aware about the recent traffic environment in partially automated vehicles.
Keywords:Human-machine-interfaces  Voice-user interfaces (VUI)  Conditional automation  SAE level 3  Passive fatigue  Driver takeover
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