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

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Author: Leeuwen, P. van · Landman, R. · Buning, L. · Heffelaar, T. · Hogema, J. · Hemert, J.M. van · Winter, J. de · Happee, R.
Source:di Bucchianico G.Vallicelli A.Landry S.Stanton N.A., International Conference on Human Factors in Transportation, AHFE 2016. 27 July 2016 through 31 July 2016, 484, 1151-1164
Advances in Intelligent Systems and Computing
Identifier: 573511
doi: doi:10.1007/978-3-319-41682-3_94
ISBN: 9783319416816
Keywords: Ergonomics · Adaptive in-vehicle information (systems) · Driver distraction · Driver workload estimation · Automobile drivers · Crashworthiness · Human engineering · Roads and streets · Adaptive in-vehicle information (systems) · Driver distractions · Driver workload estimations · Experimental methods · In-vehicle information system · In-vehicle technology · Information overloads · Specific information · Vehicles · Work and Employment · Healthy Living · Human & Operational Modelling · PCS - Perceptual and Cognitive Systems · ELSS - Earth, Life and Social Sciences


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.