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Development of an Adaptive Workload Management System using Queueing Network-Model Human Processor (QN-MHP)
ISSN: 0148-7191, e-ISSN: 2688-3627
Published April 14, 2008 by SAE International in United States
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The chance of vehicle collisions significantly increases when drivers are overloaded with information from in-vehicle systems. Developing adaptive workload management systems (AWMS) to dynamically control the rate of messages from these in-vehicle systems is one of the solutions to this problem. However, existing AWMSs do not use a model of driver cognitive system to estimate workload and only suppress or redirect in-vehicle system messages, without changing their rate based on driver workload. In this work, we propose a prototype of a new adaptive workload management system (QN-MHP AWMS) and it includes: a queueing network model of driver workload (Wu & Liu, In Press) that estimates driver workload in different driving situations, and a message controller that determines the optimal delay times between messages and dynamically controls the rate of messages presented to drivers. Given the task information of a secondary task, QN-MHP AWMS was able to adapt the rate of messages to driving conditions (speeds and curvatures) and driver characteristics (age). A corresponding experimental study was conducted to validate the potential effectiveness of this system in reducing driver workload and improving driver performance. Further developments of QN-MHP AWMS including its usage in in-vehicle systems design and possible implementation in vehicles are discussed.
CitationWu, C., Tsimhoni, O., and Liu, Y., "Development of an Adaptive Workload Management System using Queueing Network-Model Human Processor (QN-MHP)," SAE Technical Paper 2008-01-1251, 2008, https://doi.org/10.4271/2008-01-1251.
Intelligent Transportation System: Safer, Smarter, Faster, 2008
Number: SP-2200 ; Published: 2008-04-14
Number: SP-2200 ; Published: 2008-04-14
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