DOI

10.17077/drivingassessment.1667

Location

Santa Fe, New Mexico, USA

Date

25-6-2019

Session

Session 1 – Driver Behavior, Distraction and Crash Risk

Abstract

It is becoming increasingly important to understand how drivers strategically manage tasks and thread attention across time, as they drive through varying situations and conditions -- and as they have the opportunity to delegate tasks to vehicle automation while taking up other tasks themselves. To develop an understanding of these higher-level driver behaviors requires a research focus on longer periods of driving -- even on “whole trip” driving. It may also require new tools and methods. Therefore, to explore insights and implications of a “whole trip” focus, data from 10 drivers were analyzed using methods tailored for identifying patterns within larger sequences of driving data than single-task epochs. The results are reported, discussed, and contrasted with more conventional approaches based on single-task epochs.

Rights

Copyright © 2019 the author(s)

DC Citation

Proceedings of the Tenth International Driving Symposium on Human Factors in Driver Assessment, Training and Vehicle Design, 24-27 June 2019, Santa Fe, New Mexico. Iowa City, IA: Public Policy Center, of Iowa, 2019: 1-7.

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Jun 25th, 12:00 AM

In the Context of Whole Trips: New Insights Into Driver Management of Attention and Tasks

Santa Fe, New Mexico, USA

It is becoming increasingly important to understand how drivers strategically manage tasks and thread attention across time, as they drive through varying situations and conditions -- and as they have the opportunity to delegate tasks to vehicle automation while taking up other tasks themselves. To develop an understanding of these higher-level driver behaviors requires a research focus on longer periods of driving -- even on “whole trip” driving. It may also require new tools and methods. Therefore, to explore insights and implications of a “whole trip” focus, data from 10 drivers were analyzed using methods tailored for identifying patterns within larger sequences of driving data than single-task epochs. The results are reported, discussed, and contrasted with more conventional approaches based on single-task epochs.