This work develops a stochastic form of a human driver model which can be used for simulating vehicle guidance and control. The human motor-control function is complex and can be affected by factors such as driver’s training and experience, fatigue, road conditions, and attention. The variations in these effects become more pronounced in hazardous driving conditions such as in snow and ice. One example of such driving conditions is snow removal operation in highway maintenance, where the use of a stochastic driver model seems to be more desirable. This work evaluates and extends existing models of a human driver including stochastic or statistical considerations related to differences in drivers’ experiences and their conditions as well as variations in the effect of disturbances such as plowing forces. The aim is to develop a simulation environment that can be used in design and evaluation of driver assistance systems for snow removal operation in an Intelligent Transportation System environment.
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