Automatic change detection of driving environments in a vision-based driver assistance system

Abstract

Abstract-Detecting critical changes of environments while driving is an important task in driver assistance systems. In this paper, a computational model motivated by human cognitive processing and selective attention is proposed for this purpose. The computational model consists of three major components, referred to as the sensory, perceptual, and conceptual analyzers. The sensory analyzer extracts temporal and spatial information from video sequences. The extracted information serves as the input stimuli to a spatiotemporal attention (STA) neural network embedded in the perceptual analyzer. If consistent stimuli repeatedly innervate the neural network, a focus of attention will be established in the network. The attention pattern associated with the focus, together with the location and direction of motion of the pattern, form what we call a categorical feature. Based on this feature, the class of the attention pattern and, in turn, the change in driving environment corresponding to the class are determined using a configurable adaptive resonance theory (CART) neural network, which is placed in the conceptual analyzer. Various changes in driving environment, both in daytime and at night, have been tested. The experimental results demonstrated the feasibilities of both the proposed computational model and the change detection system. Index Terms-Cognitive model, configurable adaptive resonance theory (CART) neural network, driver assistance system, sensory, perceptual, and conceptual analyzers, spatiotemporal attention (STA) neural network, system to detect change in driving environment.

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“…Fang et al [15] and McCall [16] used the temporal and spatial information from video sequences to work toward a vehicle safety system in complex driving environments. Salmane et al [17] explored a video-based railway-road safety system for detecting hazard situations at level crossings.…”

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