Characterizing Driver Intention via Hierarchical Perception–Action Modeling

Abstract

We seek a mechanism for the classification of the intentional behavior of a cognitive agent, specifically a driver, in terms of a psychological Perception-Action (P-A) model, such that the resulting system would be potentially suitable for use in intelligent driver assistance. P-A models of human intentionality assume that a cognitive agent's perceptual domain is learned in response to the outcome of the agent's actions rather than vice versa. In this way, the perceptual domain is maintained at an appropriate level of complexity in relation to the agent's embodied motor capabilities, greatly simplifying visual processing. A subsumptive P-A model further captures the hierarchical nature of the subtask structure implicit in human actions and assumes that a parallel hierarchical structuring exists within the perceptual domain. Adopting this model enables us to characterize intentions at each level of the P-A hierarchy in terms of a range of descriptors derived from the U.K. Highway Code by examining their correlation with driver gaze behavior. The problem of classifying intentions thus becomes one of reconciling high-level protocols (i.e., Highway Code rules) with low-level perceptual features. We perform a “proof-of-concept” assessment of the model by comparative evaluation of a number of logic-based methods (both stochastic and deductive) for carrying out this classification utilizing the control, signal, and motor inputs of an instrumented vehicle driven by a single driver, and find that a deductive model gives superior intentional classification performance due to the strongly protocol-governed nature of the driving environment.