A multi-sensory cybernetic driver model of stopping behavior

Abstract

Driver training effectiveness requires assessment of driver performance in order to compare and contrast the impact of different training techniques on the learned control. Human performance can be quantified from different perspectives ranging from aggregate measures to specific model coefficients in order to link observed performance to how the driver achieved this performance through a particular control strategy. A model based approach is needed to understand the pros and cons of different training programs. One important yet often ignored aspect of a model is the cost function that drives behavior adaptation. Here, a model based methodology is proposed that estimates the weights on different terms in the cost function that drivers use to adapt their behavior in order to satisfy their performance needs. Because the driver model includes the effect of the controlled dynamical system as well as any particularities of the training environment, one can use it to quantify the effect of training specific deviations from reality, such as the use of a driving simulator that causes known biases in perception, on behavior. This paper details the methodological approach and discusses it in the context of stopping behavior in reality versus in a driving simulator. The goal with training is to instill the right structural behavior so that only minor adaptations may be needed once applying the learned skill in reality. Because the adopted cost function plays such a large role, much focus should also be given to shaping the cost function that operators employ.