Characterization of Human Driving Behaviors in Shared Vehicle Control Based on Level-k Cognitive Modeling

This paper presents how game-theoretic level-k cognitive modeling, a framework for modeling the decision-making of agents with bounded rationality, can be used to describe the interactive policies within human-autonomy teams during collaborative tasks. The approach hypothesizes that prediction of human behavior can be used to enable autonomous systems to make better decisions. A case study is used to create a preliminary data library mapping human behavior to specific level-k strategies. Specifically, a participant completed laps on a shared control driving simulator while performing a secondary task. Between laps, the participant is informed of changes in the vehicle controller that impact the influence of the human input on the vehicle motion, reflecting changes in the belief the human holds about the autonomy, or changing their level-k. Using the library, the mapping between human behavior and level-k theory is evaluated through the classification of interactions between human-autonomy teams under directed scenarios.