Towards a real-time driver workload estimator: An on-the-road study

Peter Van Leeuwen, Renske Landman, Lejo Buning, Tobias Heffelaar, Jeroen Hogema, Jasper Michiel van Hemert, Joost de Winter, Riender Happee

Research output: Chapter in Book/Conference proceedings/Edited volumeConference contributionScientificpeer-review

2 Citations (Scopus)
18 Downloads (Pure)


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.

Original languageEnglish
Title of host publicationAdvances in Human Aspects of Transportation
Subtitle of host publicationProceedings of the AHFE 2016 International Conference on Human Factors in Transportation
EditorsNeville A. Stanton, Steven Landry, Giuseppe Di Bucchianico, Andrea Vallicelli
Place of PublicationCham, Switzerland
ISBN (Electronic)978-3-319-41682-3
ISBN (Print)978-3-319-41681-6
Publication statusPublished - 2016
Event7 th International Conference on Human Factors in Transportation - Orlando, United States
Duration: 27 Jul 201631 Jul 2016

Publication series

NameAdvances in Intelligent Systems and Computing
ISSN (Print)21945357


Conference7 th International Conference on Human Factors in Transportation
Abbreviated titleAHFE 2016
CountryUnited States


  • Adaptive in-vehicle information (systems)
  • Driver distraction
  • Driver workload estimation

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