Drivers’ ability to engage in a non-driving related task while in automated driving mode in real trafficShow others and affiliations
2020 (English)In: IEEE Access, E-ISSN 2169-3536, Vol. 8, p. 221654-221668Article in journal (Refereed) Published
Abstract [en]
Engaging in non-driving related tasks (NDRTs) while driving can be considered distracting and safety detrimental. However, with the introduction of highly automated driving systems that relieve drivers from driving, more NDRTs will be feasible. In fact, many car manufacturers emphasize that one of the main advantages with automated cars is that it “frees up time” for other activities while on the move. This paper investigates how well drivers are able to engage in an NDRT while in automated driving mode (i.e., SAE Level 4) in real traffic, via a Wizard of Oz platform. The NDRT was designed to be visually and cognitively demanding and require manual interaction. The results show that the drivers’ attention to a great extent shifted from the road ahead towards the NDRT. Participants could perform the NDRT equally well as when in an office (e.g. correct answers, time to completion), showing that the performance did not deteriorate when in the automated vehicle. Yet, many participants indicated that they noted and reacted to environmental changes and sudden changes in vehicle motion. Participants were also surprised by their own ability to, with ease, disconnect from driving. The presented study extends previous research by identifying that drivers to a high extent are able to engage in an NDTR while in automated mode in real traffic. This is promising for future of automated cars ability to “free up time” and enable drivers to engage in non-driving related activities.
Place, publisher, year, edition, pages
2020. Vol. 8, p. 221654-221668
Keywords [en]
Vehicles, Task analysis, Automation, Automobiles, Roads, Visualization, Manuals, Automated Driving, Driver behavior, Driver experience, Non-driving related task, Secondary task
National Category
Engineering and Technology
Identifiers
URN: urn:nbn:se:ri:diva-51013DOI: 10.1109/ACCESS.2020.3043428OAI: oai:DiVA.org:ri-51013DiVA, id: diva2:1510919
2020-12-172020-12-172023-05-22Bibliographically approved