A1 Journal article (refereed)
Expert Drivers’ Prospective Thinking-Aloud to Enhance Automated Driving Technologies : Investigating Uncertainty and Anticipation in Traffic (2020)


Grahn, H., Kujala, T., Silvennoinen, J., Leppänen, A., & Saariluoma, P. (2020). Expert Drivers’ Prospective Thinking-Aloud to Enhance Automated Driving Technologies : Investigating Uncertainty and Anticipation in Traffic. Accident Analysis and Prevention, 146, Article 105717. https://doi.org/10.1016/j.aap.2020.105717


JYU authors or editors


Publication details

All authors or editors: Grahn, Hilkka; Kujala, Tuomo; Silvennoinen, Johanna; Leppänen, Aino; Saariluoma, Pertti

Journal or series: Accident Analysis and Prevention

ISSN: 0001-4575

eISSN: 1879-2057

Publication year: 2020

Volume: 146

Article number: 105717

Publisher: Elsevier BV

Publication country: United Kingdom

Publication language: English

DOI: https://doi.org/10.1016/j.aap.2020.105717

Publication open access: Not open

Publication channel open access:


Abstract

Current automated driving technology cannot cope in numerous conditions that are basic daily driving situations for human drivers. Previous studies show that profound understanding of human drivers’ capability to interpret and anticipate traffic situations is required in order to provide similar capacities for automated driving technologies. There is currently not enough a priori understanding of these anticipatory capacities for safe driving applicable to any given driving situation. To enable the development of safer, more economical, and more comfortable automated driving experience, expert drivers’ anticipations and related uncertainties were studied on public roads. First, driving instructors’ expertise in anticipating traffic situations was validated with a hazard prediction test. Then, selected driving instructors drove in real traffic while thinking aloud anticipations of unfolding events. The results indicate sources of uncertainty and related adaptive and social behaviors in specific traffic situations and environments. In addition, the applicability of these anticipatory capabilities to current automated driving technology is discussed. The presented method and results can be utilized to enhance automated driving technologies by indicating their potential limitations and may enable improved situation awareness for automated vehicles. Furthermore, the produced data can be utilized for recognizing such upcoming situations, in which the human should take over the vehicle, to enable timely take-over requests.


Keywords: autonomous cars; traffic safety; uncertainty; anticipation; specialists (experts); chauffeurs

Free keywords: automated driving; expert driver; prospective thinking-aloud; traffic safety; uncertainty; anticipation


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Related projects


Ministry reporting: Yes

Reporting Year: 2020

JUFO rating: 2


Last updated on 2021-07-07 at 21:37