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• W3048758174 endingPage "105717" @default.
• W3048758174 startingPage "105717" @default.
• W3048758174 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." @default.
• W3048758174 created "2020-08-18" @default.
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• W3048758174 date "2020-10-01" @default.
• W3048758174 modified "2023-09-24" @default.
• W3048758174 title "Expert Drivers’ Prospective Thinking-Aloud to Enhance Automated Driving Technologies – Investigating Uncertainty and Anticipation in Traffic" @default.
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• W3048758174 doi "https://doi.org/10.1016/j.aap.2020.105717" @default.
• W3048758174 hasPubMedId "https://pubmed.ncbi.nlm.nih.gov/32798781" @default.
• W3048758174 hasPublicationYear "2020" @default.
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