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Urgent and non-urgent takeovers during conditional automated driving on public roads: The impact of different training programmes
Institution:1. Faculty of Engineering, Information and Systems, University of Tsukuba, Tsukuba, Ibaraki, Japan;2. Automotive Human Factors Research Center, National Institute of Advanced Industrial Science and Technology, Tsukuba, Ibaraki, Japan;1. Dunlap and Associates, Inc., 110 Lenox Ave., Stamford, CT, 06906, United States;2. University of Massachusetts Amherst, Department of Mechanical and Industrial Engineering, 160 Governors Drive, Amherst, MA, 01003, United States;3. University of Massachusetts Amherst, College of Information and Computer Sciences, 140 Governors Drive, Amherst, MA, 01003, United States;4. Volpe National Transportation Systems Center, 55 Broadway Street, Cambridge, MA, 02142, United States;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:Supplying training to drivers that teaches them about automated driving and requests to intervene may help them to build and maintain a mental representation of how automation works and thereby improve takeover performance. We aimed to investigate the effect of different types of training programmes about the functioning of automated driving on drivers’ takeover performance during real driving. Fifty-two participants were split into three groups for training sessions: paper (short notice), video (3-minute tutorial) and practice (short drive). After the training, participants experienced automated driving and both urgent and non-urgent requests to intervene in a Wizard-of-Oz vehicle on public roads. Participants’ takeover time, visual behaviour, mental workload, and flow levels during the requests to intervene were assessed. Our results indicated that in urgent circumstances, participants’ takeover response times were faster in the practice training condition compared to the other training conditions. Nevertheless, the practice training session did not present any other positive effect on drivers’ visual behaviour. This could indicate that prior training, particularly when reinforcing drivers' motor skills, improved their takeover response time at the latest motor stages rather than in the early sensory states. In addition, the analysis of in-vehicle videos revealed that participants’ attention was captured in the first place by the in-vehicle human-machine interface during the urgent request to intervene. This highlights the importance for designers to display information on the HMI in an appropriate way to optimise drivers’ situation awareness in critical situations.
Keywords:Automated driving  Request to intervene  Training  Wizard-of-Oz  Human-automation interaction
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