Towards a Real-Time Driver Workload Estimator: An On-the-Road Study
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 d
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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 P. van Leeuwen (&) J. de Winter R. Happee Department of BioMechanical Engineering, Delft University of Technology, Mekelweg 2, 2628 CD Delft, The Netherlands e-mail: [email protected] J. de Winter e-mail: [email protected] R. Happee e-mail: [email protected] R. Landman Ergos Human Factors Engineering, Hengelosestraat 448-a, 7521 AN Enschede, The Netherlands e-mail: [email protected] L. Buning HAN University of Applied Sciences, Ruitenberglaan 29, 6826 CC Arnhem, The Netherlands e-mail: [email protected] T. Heffelaar Noldus Information Technology, Nieuwe Kanaal 5, 6709 PA Wageningen, The Netherlands e-mail: [email protected] J. Hogema TNO, Perceptual and Cognitive Systems, Kampweg 5, 3769 DE Soesterberg, The Netherlands e-mail: [email protected] J.M. van Hemert TomTom BV, Oosterdokstraat 114, 1011 DK Amsterdam, The Netherlands e-mail: [email protected] © Springer International Publishing Switzerland 2017 N.A. Stanton et al. (eds.), Advances in Human Aspects of Transportation, Advances in Intelligent Systems and Computing 484, DOI 10.1007/978-3-319-41682-3_94
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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.
Keywords Driver distraction Adaptive in-vehicle information (systems) Driver workload estimation
1 Introduction Driver distraction is a leading contributor to road traffic crashes [1]. A recent naturalistic driving study showed that as much as 78 % of crashes were related to distraction [2]. Because of the increasing prevalence of technological aids, road safety has improved considerably in the last decades. However, certain in-vehicle technologies such as infotainment systems and handheld electronic devices are themselves a source of distraction and crash risk [1, 3–6]. Distracted driving not only reduces lane-keeping accuracy [7, 8] but also increases the brake reaction time to critical environmental events [9]. Furthermore, a complex in-vehicle display may result in an ‘information overload’ [10]. A potential remedy to these problems may be the use of adaptive information systems [11]. Adaptive information systems aid the driver by warning for upcoming high-workload situations or by adapting the in
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