Workload Management System for Drivers

Examensarbete för masterexamen

Please use this identifier to cite or link to this item: https://hdl.handle.net/20.500.12380/120120
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Type: Examensarbete för masterexamen
Master Thesis
Title: Workload Management System for Drivers
Authors: Azhar, Rehan
Ahsan, Rana Muhammad
Abstract: Drivers get busy in secondary tasks, such as making phone calls, adjusting radio systems, which divert the attention of driver from primary task of driving. Literature studies showed that the external and internal conditions are the main distracting factors for driving. To ensure the driver’s safety while performing secondary tasks during drive, different workload management systems are proposed. Zonal Adaptive Workload Management System is one of those systems which is proposed by Chen and Jordan [1], the level of allowance of driver’s secondary task performance depends on the zonal division that takes external traffic conditions, weather and lighting etc into consideration during driving. The review of the literature is to build up the theoretical and data support to zonal division. The statistical analysis of our experiments shows that the average workload rating of images and video clips of different driving situations were almost the same and the participants were not consistent in rating the same driving situation. It has been analyzed that workload rating increases when the weather conditions are raining or snowing, making the road condition wet or freezing, for conditions like raining at night and heavy snow weather conditions the workload rating is the maximum. Bad weather conditions at night like snowing and raining in the subjective questionnaire are rated very high in workload by most of the participants. The average workload ratings provided by the female participants are higher as compared to the ratings provided by the male participants.
Keywords: Datalogi;Computer science
Issue Date: 2010
Publisher: Chalmers tekniska högskola / Institutionen för data- och informationsteknik (Chalmers)
Chalmers University of Technology / Department of Computer Science and Engineering (Chalmers)
URI: https://hdl.handle.net/20.500.12380/120120
Collection:Examensarbeten för masterexamen // Master Theses



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