Towards a real-time driver workload estimator
An on-the-road study
Peter Jan Van Leeuwen (TU Delft - Biomechatronics & Human-Machine Control)
Renske Landman (Ergos Human Factors Engineering)
Lejo Buning (HAN University of Applied Sciences)
Tobias Heffelaar (Noldus Information Technology)
Jeroen Hogema (TNO)
J.M. van Hemert (TomTom BV)
JCF Winter (TU Delft - OLD Intelligent Vehicles & Cognitive Robotics)
R. Happee (TU Delft - OLD Intelligent Vehicles & Cognitive Robotics)
More Info
expand_more
Other than for strictly personal use, it is not permitted to download, forward or distribute the text or part of it, without the consent of the author(s) and/or copyright holder(s), unless the work is under an open content license such as Creative Commons.
Abstract
Driver distraction is a leading cause of crashes. The introduction of in-vehicle technology in the last decades has added support to the driving task. However, in-vehicle technologies and handheld electronic devices may also be a threat to driver safety due to information overload and distraction. Adaptive in-vehicle information systems may be a solution to this problem. Adaptive systems could aid the driver in obtaining information from the device (by reducing information density) or prevent distraction by not presenting or delaying information when the driver’s workload is high. In this paper, we describe an on-the-road evaluation of a real-time driver workload estimator that makes use of geo-specific information. The results demonstrate the relative validity of our experimental methods and show the potential for using location-based adaptive in-vehicle systems.