Modeling and predicting age differences in driver mental workload and performance may help the in-vehicle system design to reduce or prevent information overloading on older drivers. However, few computational models exist that account for age differences in mental workload. We propose a new computational modeling approach to model workload and performance–a queuing network approach based on queuing network theory of human performance (Liu, 1996, 1997) and neuroscience discoveries. This modeling approach is composed of a simulation model of a queuing network architecture and a set of mathematical equations implemented in the simulation model to quantify the age differences. The model successfully accounts for the age differences in mental workload and performance between young and older drivers in an experimental study in driving. Further usage and implementation of this model in designing adaptive in-vehicle systems to assist older drivers are discussed.
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