In the last few years, modeling dynamic human control strategy (HCS)
is becoming an increasingly popular paradigm in a number of different
research areas, such as the intelligent vehicle highway system,
virtual reality and robotics. Usually, these models are derived
empirically, rather than analytically, from real human input-output
control data. As such, there is a great need to develop adequate
performance criteria for these models, as few guarantees exist about
their theoretical performance. It is our goal in this paper to develop
several such criteria. In this paper, we first collect driving data
from different individuals through a real-time graphic driving
simulator. We then model each individual's control strategy through
the flexible cascade neural network learning architecture. Next, we
develop two performance measures for evaluating the resulting HCS
models, one dealing with obstacle avoidance, the other with
tight-turning behavior. Finally, we evaluate the relative skill of
different HCS models through the proposed performance criteria.
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