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

• Published in: IEEE transactions on intelligent transportation systems Vol. 9; no. 3; pp. 463 - 475
• Main Authors: Wu, Changxu, Tsimhoni, Omer, Liu, Yili
• Format: Journal Article
• Language: English
• Published: Piscataway, NJ IEEE 01.09.2008; Institute of Electrical and Electronics Engineers; The Institute of Electrical and Electronics Engineers, Inc. (IEEE)
• Subjects: Adaptive control; Adaptive system; Adaptive systems; Applied sciences; Automobile driving; Computer science; control theory; systems; Computer systems and distributed systems. User interface; Control systems; Curvature; Delay estimation; driver workload; Drivers; Dynamical systems; Dynamics; Estimates; Exact sciences and technology; Humans; Information systems. Data bases; Memory organisation. Data processing; Messages; Optimal control; Programmable control; Prototypes; queueing network; Queues; Risk management; Software; Studies; Vehicle driving; Workload; workload management; queueing network; driver workload; Adaptive system; workload management; Human; Motor car; Processor; Vehicle driving; Collision; Queueing network; Modeling; Cognitive system; Workload; Minimum time; Message passing; Overload; Queueing system; Delay time; Curvature; Age
• ISSN: 1524-9050; 1558-0016
• DOI: 10.1109/TITS.2008.928172

The risk of vehicle collisions significantly increases when drivers are overloaded with information from in-vehicle systems. One of the solutions to this problem is developing adaptive workload management systems (AWMSs) to dynamically control the rate of messages from these in-vehicle systems. However, existing AWMSs do not use a model of the 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 paper, we propose a prototype of a new queueing network-model human processor AWMS (QN-MHP AWMS), which includes a queueing network model of driver workload that estimates the driver workload in several 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, the QN-MHP AWMS adapted the rate of messages to the driving conditions (i.e., speeds and curvatures) and driver characteristics (i.e., age). A corresponding experimental study was conducted to validate the potential effectiveness of this system in reducing driver workload and improving driver performance. Further development of the QN-MHP AWMS, including its use in in-vehicle system design and possible implementation in vehicles, is discussed.