Development of an Adaptive Workload Management System using Queueing Network-Model Human Processor (QN-MHP)

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.