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 discontinuous human control
strategies. We then propose an HMM-based method for modeling
discontinuous human control strategies. 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 and propose avenues for
future research.
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