Stochastic Similarity for Validating Human Control Strategy Models

Abstract

Modeling dynamic human control strategy (HCS), or human skill through learning is becoming an increasingly popular paradigm in many different research areas, such as intelligent vehicle systems, virtual reality, and space robotics. Validating the fidelity of such models requires that we compare the dynamic trajectories generated by the HCS model in the control feedback loop to the original human control data. To this end we have developed a stochastic similarity measure-based on hidden Markov model (HMM) analysis-capable of comparing dynamic, multi-dimensional trajectories. In this paper, we first derive and demonstrate properties of the proposed similarity measure for stochastic systems. We then apply the similarity measure to real-time human driving data by comparing different control strategies for different individuals. Finally, we show that the similarity measure outperforms the more traditional Bayes classifier in correctly grouping driving data from the same individual.