Using Naturalistic Vehicle-Based Data to Predict Distraction and Environmental Demand

Dina Kanaan, Suzan Ayas, Birsen Donmez, Martina Risteska and Joyita Chakraborty
Additional contact information
Dina Kanaan: University of Toronto, Toronto, Canada
Suzan Ayas: University of Toronto, Toronto, Canada
Birsen Donmez: University of Toronto, Toronto, Canada
Martina Risteska: University of Toronto, Toronto, Canada
Joyita Chakraborty: University of Toronto, Toronto, Canada

International Journal of Mobile Human Computer Interaction (IJMHCI), 2019, vol. 11, issue 3, 59-70

Abstract: This research utilized vehicle-based measures from a naturalistic driving dataset to detect distraction as indicated by long off-path glances (≥ 2 s) and whether the driver was engaged in a secondary (non-driving) task or not, as well as to estimate motor control difficulty associated with the driving environment (i.e. curvature and poor surface conditions). Advanced driver assistance systems can exploit such driver behavior models to better support the driver and improve safety. Given the temporal nature of vehicle-based measures, Hidden Markov Models (HMMs) were utilized; GPS speed and steering wheel position were used to classify the existence of off-path glances (yes vs. no) and secondary task engagement (yes vs. no); lateral (x-axis) and longitudinal (y-axis) acceleration were used to classify motor control difficulty (lower vs. higher). Best classification accuracies were achieved for identifying cases of long off-path glances and secondary task engagement with both accuracies of 77%.

Date: 2019
References: View complete reference list from CitEc
Citations:

Downloads: (external link)
http://services.igi-global.com/resolvedoi/resolve. ... 18/IJMHCI.2019070104 (application/pdf)

Related works:
This item may be available elsewhere in EconPapers: Search for items with the same title.

Export reference: BibTeX RIS (EndNote, ProCite, RefMan) HTML/Text

Persistent link: https://EconPapers.repec.org/RePEc:igg:jmhci0:v:11:y:2019:i:3:p:59-70

Access Statistics for this article

International Journal of Mobile Human Computer Interaction (IJMHCI) is currently edited by Joanna Lumsden

More articles in International Journal of Mobile Human Computer Interaction (IJMHCI) from IGI Global
Bibliographic data for series maintained by Journal Editor ().