Characterizing Driver Intention via Hierarchical Perception–Action Modeling

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.