Models of human control strategy (HCS), which accurately emulate
dynamic human behavior, have far reaching potential in areas ranging
from robotics to virtual reality to the intelligent vehicle highway
project. A number of learning algorithms, including fuzzy logic,
neural networks, and locally weighted regression exist for modeling
continuous human control strategies. These algorithms, however, may
not be well suited for modeling discontinuous human control
strategies. Therefore, we propose a new stochastic, discontinuous
modeling framework, for abstracting human control strategies, based on
Hidden Markov Models. In this paper, we first describe the real-time
driving simulator which we have developed for investigating human
control strategies. Next, we demonstrate the shortcomings of a
typical continuous modeling approach in modeling a discontinuous human
control strategy. We then propose an HMM-based method of modeling
discontinuous human control strategies, and show that the proposed
controller overcomes these shortcomings and demonstrates greater
fidelity to the human training data. We conclude the paper with
further comparisons between the two competing modeling approaches.
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