Queuing network modeling of driver workload and performance

Drivers overloaded with information significantly increase the chance of vehicle collisions. Driver workload, which is a multidimensional variable, is measured by both performance-based and subjective measurements and affected by driver age differences. Few existing computational models are able to cover these major properties of driver workload or simulate subjective mental workload and human performance at the same time. We describe a new computational approach in modeling driver performance and workload-a queuing network approach based on the queuing network theory of human performance and neuroscience discoveries. This modeling approach not only successfully models the mental workload measured by the six National Aeronautic and Space Administration Task Load Index workload scales in terms of subnetwork utilization but also simulates the driving performance, reflecting mental workload from both subjective- and performance-based measurements. In addition, it models age differences in workload and performance and allows us to visualize driver mental workload in real time. Further usage and implementation of the model in designing intelligent and adaptive in-vehicle systems are discussed.