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Estimation of Primary Task Demand using Naturalistic Field Operational Test Data

Arjun Venkataraman ; Premnaath Sukumar
Göteborg : Chalmers tekniska högskola, 2011. Diploma work - Department of Applied Mechanics, Chalmers University of Technology, Göteborg, Sweden, ISSN 1652-8557; 2011:54, 2011.
[Examensarbete på avancerad nivå]

Driver’s attention on the driving task is vital for safe travel. Intelligent vehicle systems (IVSs) are systems which assist the driver by, for instance, providing extra information about the vehicle, the environment, or the driver to make driving more comfortable and safe. IVSs have a high potential to improve driving, however they should not distract the driver by, for example, inducing the driver to take his/her eyes off the road in critical situations; rather IVSs shall direct the driver to shift focus to the direction of immediate danger. The objective of this thesis was to estimate ‘primary task demand’ (i.e. create a mathematical model describing the demand of the current driving situation) using naturalistic driving data. Such estimation may be used to increase the effectiveness of IVSs by 1) preventing such systems from distracting the driver when the primary task demand is high and 2) helping such systems providing information according to the current driving situation. Naturalistic driving data from the SeMiFOT field operational test was used to select several driving situations which were ranked by 40 drivers based on their perceived primary task demand. Further, this study focused on roundabouts since they are a very common scenario for low speed crashes and require continuous maneuvering from the driver. Finally, this study also elucidates the extent to which perception of primary task demand is influenced by cultural difference by comparing results from 20 Indians and 20 European drivers.

Nyckelord: Traffic Safety, Naturalist Driving, Field Operational Test, Workload, Active Safety, Roundabouts.

Publikationen registrerades 2011-12-13. Den ändrades senast 2013-04-04

CPL ID: 150020

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