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Characterizing Driver Intention via Hierarchical Perception–Action Modeling

Windridge, D, Shaukat, A and Hollnagel, E (2012) Characterizing Driver Intention via Hierarchical Perception–Action Modeling IEEE Transactions on Human-Machine Systems, 43 (1). pp. 17-31.

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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.

Item Type: Article
Divisions : Faculty of Engineering and Physical Sciences > Electronic Engineering > Centre for Vision Speech and Signal Processing
Authors :
Date : 25 October 2012
Identification Number : 10.1109/TSMCA.2012.2216868
Contributors :
Related URLs :
Additional Information : © 2012 IEEE. Personal use of this material is permitted. Permission from IEEE must be obtained for all other uses, in any current or future media, including reprinting/republishing this material for advertising or promotional purposes, creating new collective works, for resale or redistribution to servers or lists, or reuse of any copyrighted component of this work in other works.
Depositing User : Symplectic Elements
Date Deposited : 18 Sep 2013 08:13
Last Modified : 23 Sep 2013 20:17

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