Modeling dynamic human control strategy (HCS), or human skill in
response to real-time sensing is becoming an increasingly popular
paradigm in many different research areas, such as intelligent vehicle
systems, virtual reality, and space robotics. Such models are often
learned from experimental data, and as such can be characterized
despite the lack of a good physical model. Unfortunately, learned
models presently offer few, if any, guarantees in terms of model
fidelity to the training data. This is especially true for dynamic
reaction skills, where errors can feed back on themselves to generate
state and command trajectories uncharacteristic of the source
process. Thus, we propose a stochastic similarity measure - based on
Hidden Markov Model analysis - capable of comparing and contrasting
stochastic, dynamic, multi-dimensional trajectories. This similarity
measure is the first step in validating a learned model's fidelity to
its training data by comparing the model's dynamic trajectories in the
feedback loop to the human's dynamic trajectories. In this paper, we
first derive and demonstrate properties of the similarity measure for
stochastic systems. We then apply the similarity measure to real-time
human driving data by comparing different control strategies among
different individuals. We show that the proposed similarity measure
outperforms the more traditional Bayes classifier in correctly
grouping driving data from the same individual. Finally, we illustrate
how the similarity measure can be used in the validation of models
which are learned from experimental data., and how we can connect
model validation and model learning to iteratively improve our models
of human control strategy.
|
