IEEE TRANSACTIONS ON INTELLIGENT TRANSPORTATION SYSTEMS, VOL. 9, NO. 3, SEPTEMBER 2008 463 Development of an Adaptive Workload Management System Using the Queueing Network-Model Human Processor (QN-MHP) Changxu Wu, Member, IEEE, Omer Tsimhoni, Member, IEEE, and Yili Liu, Member, IEEE Abstract—The risk of vehicle collisions significantly increases when drivers are overloaded with information from in-vehicle sys- tems. 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, ex- isting 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 queue- ing 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 char- acteristics (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. Index Terms—Adaptive system, driver workload, queueing net- work, workload management. I. I NTRODUCTION W ITH THE development of in-vehicle system technology, increasingly more in-vehicle information and entertain- ment systems (e.g., navigation aides, mobile phones, e-mail, web browsers, vehicle-to-vehicle communication systems, and traffic information displays) are being used in vehicles. Mul- Manuscript received June 13, 2007; revised November 15, 2007, February 16, 2008, and February 19, 2008. This work was supported in part by the University of Michigan Transportation Research Institute (UMTRI) under the Doctoral Studies Program and in part by the National Science Foundation under Grant NSF 0308000. The Associate Editor for this paper was M. Brackstone. C. Wu was with the UMTRI and the Department of Industrial and Op- erations Engineering, University of Michigan, Ann Arbor, MI 48109-2119 USA. He is now with the Department of Industrial and System Engineer- ing, State University of New York, Buffalo, NY 14260-2050 USA (e-mail: changxu@buffalo.edu). O. Tsimhoni is with the UMTRI and the Department of Industrial and Operations Engineering, University of Michigan, Ann Arbor, MI 48109 USA (e-mail: omert@umich.edu). Y. Liu is with the Department of Industrial and Operations Engineering, Uni- versity of Michigan, Ann Arbor, MI 48109 USA (e-mail: yililiu@umich.edu). Color versions of one or more of the figures in this paper are available online at http://ieeexplore.ieee.org. Digital Object Identifier 10.1109/TITS.2008.928172 titasking between driving and using these systems may im- pose high information load on drivers, increasing their mental workload [1]–[3], which, in turn, increases the risk of vehicle collisions, compared with a single-task driving condition [1], [4]. Multitasking has also become particularly common for drivers with special duties. For example, police officers need to drive, communicate with other police officers, and monitor the speed of other cars via radar systems at the same time; ambulance drivers need to steer vehicles, navigate their vehicle to patients’ locations, and communicate with dispatchers and hospitals at the same time; and fire-fighting vehicle drivers also need to steer and navigate vehicles to target locations and communicate with their headquarters at the same time to receive updates on the situation of target locations. After Michon [5] proposed the basic concepts in designing an adaptive system for drivers, recently, several adaptive workload management systems (AWMSs) have been developed as one of the possible solutions in reducing driver mental workload [6] (see Table I). Some available systems include BMW’s phone adaptive system [6] and Toyota’s voice adaptive system [7] (see reviews in [8] and [9]). There are two important components in these systems: First, to estimate driver workload, these adaptive systems collect current driving information, such as steering wheel angle and lane position, and then use computational algorithms to directly estimate the current workload of the driver. In addition to these estimations of the workload, re- searchers can also use subjective workload questionnaires or psychophysiological measurement (e.g., event-related poten- tial) to estimate the workload; however, these subjective and psychophysiological measurements either require subjects to perform additional tasks or attach certain electrodes onto the human body, making them very difficult to use in practical situ- ations. Second, based on these estimations of driver workload, the systems propose corresponding actions to reduce driver workload, e.g., suppressing messages from in-vehicle systems [7] or redirecting messages into a voice mailbox when the driver’s estimated mental workload is high [6]. There are two important aspects in the human factors of these AWMSs that need further improvement: First, at the human end, a cognitive model of the driver might be useful in estimating a driver’s workload in a multitasking situation. Such a model may particularly be useful for the quantification of the effects of driving situations (e.g., speed and road curve), characteristics of drivers (e.g., age), and secondary tasks on the driver workload (e.g., the processing time of the secondary task 1524-9050/$25.00 © 2008 IEEE Authorized licensed use limited to: IEEE Xplore. Downloaded on November 3, 2008 at 16:55 from IEEE Xplore. Restrictions apply.