Driven to discussion: engaging drivers in conversation with a digital assistant as a countermeasure to passive task-related fatigue

Large, David R.; Burnett, Gary; Antrobus, Vicki; Skrypchuk, Lee

Abstract

Using a Wizard-of-Oz approach, we explored the effectiveness of engaging drivers in conversation with a digital assistant as an operational strategy to combat the symptoms of passive task-related fatigue. Twenty participants undertook two 30-minute drives in a medium-fidelity driving simulator between 13:00 and 16:30, when circadian and homeostatic influences naturally reduce alertness. Participants were asked to follow a lead-car travelling at a constant speed of 68mph, in a sparsely-populated UK motorway scenario. During one of the counterbalanced drives, participants were engaged in conversation by a digital assistant (‘Vid’). Results show that interacting with Vid had a positive effect on driving performance and arousal, evidenced by better lane-keeping, earlier response to a potential hazard situation, larger pupil diameter, and an increased spread of attention to the road-scene (i.e. fewer fixations concentrated on the road-centre indicating a lower incidence of ‘cognitive tunnelling’). Drivers also reported higher levels of alertness and lower sleepiness following the Vid drive. Subjective workload ratings suggest that drivers exerted less effort to ‘stay awake’ when engaged with Vid. The findings support the development and application of in-vehicle natural language interfaces, and can be used to inform the design of novel countermeasures for driver fatigue.

Citation

Large, D. R., Burnett, G., Antrobus, V., & Skrypchuk, L. (2018). Driven to discussion: engaging drivers in conversation with a digital assistant as a countermeasure to passive task-related fatigue. IET Intelligent Transport Systems, 12(6), 420-426. https://doi.org/10.1049/iet-its.2017.0201

• Journal Article Type: Article
• Acceptance Date: Feb 16, 2018
• Online Publication Date: Feb 20, 2018
• Publication Date: Aug 31, 2018
• Deposit Date: Mar 9, 2018
• Publicly Available Date: Mar 9, 2018
• Journal: IET Intelligent Transport Systems
• Print ISSN: 1751-956X
• Electronic ISSN: 1751-9578
• Publisher: Institution of Engineering and Technology (IET)
• Peer Reviewed: Peer Reviewed
• Volume: 12
• Issue: 6
• Pages: 420-426
• DOI: https://doi.org/10.1049/iet-its.2017.0201
• Keywords: Driver information systems; Natural language interfaces
• Public URL: https://nottingham-repository.worktribe.com/output/949701
• Publisher URL: http://digital-library.theiet.org/content/journals/10.1049/iet-its.2017.0201
• Additional Information: This paper is a postprint of a paper submitted to and accepted for publication in IET Intelligent Transport Systemsand is subject to Institution of Engineering and Technology Copyright. The copy of record is available at the IET Digital Library.