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- W2626929749 abstract "Modelling human behavior in terms of its physical, logical and cognitive correlates is innately complicated. One approach to simplifying this problem is to describe human intentions in the form of a subsumptive perception-action hierarchy [2, 1]. Perception-action methodologies assume that perceptual representation depends critically upon an agent’s action capability. We thus adopt the principle that an agent’s set of possible perceptual transitions exists in one-to-one correlation with intentional actions at each level of the perception-action hierarchy in order to model human cognition. In particular, we seek to infer the (hierarchical) mapping existing between domain-based action protocols and human visual representations within the scenario of car driving. To this end, we adopt the Extended Control Model (ECOM) which describes human driving behavior in terms of four distinct (but simultaneous) layers of perception-action feedback [3] (Targeting, Monitoring, Regulating, Tracking), that collectively represent every aspect of driving (from navigating to car-following) as a perceptionaction feedback cycle. Within this model, human visual representations are determined via the correlation of attention (measured by gaze-tracking) with a highway-code-derived scene description hierarchy involving traffic lights, lanes, roads, sign junction etc. Our goal is thus to use stochastic and structural machine learning techniques to correctly determine the active intention at each level of the ECOM hierarchy when given a set of gaze, signal, control and scene-description classifier inputs. Our ultimate aim is to do this in an adaptive, unsupervised manner." @default.
- W2626929749 created "2017-06-23" @default.
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- W2626929749 date "2010-01-01" @default.
- W2626929749 modified "2023-09-27" @default.
- W2626929749 title "Induction of the Human Perception-Action Hierarchy Employed in Junction-Navigation Scenarios" @default.
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