Predicting the Perceptual Demands of Urban Driving with Video Regression

Luke Palmer, Alina Bialkowski, Gabriel J. Brostow, Jonas Ambeck-Madsen, Nilli Lavie

Abstract: To drive safely requires perceiving vast amounts of rapidly changing visual information. This can exhaust our limited perceptual capacity and lead to cases of 'looking but failing to see', reportedly the third largest contributing factor to road traffic accidents. In the present work we use a 3D convolutional neural network to model the perceptual demand of varied driving situations. To validate the method we introduce a new labelled dataset of approximately 2300 videos of driving in Brussels and California.