Abstract
<bold xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">Attention for Vision-Based Assistive and Automated Driving: A Review of Algorithms and Datasets</b> <italic xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">I. Kotseruba and J. K. Tsotsos</i> Since first cars appeared on the streets, driver inattention has been a safety concern. Recently, advanced driver assistance systems (ADAS) and autonomous driving have been introduced to tackle this issue. In this article, the authors review a broad range of vision-based algorithms proposed in the past decade that model drivers’ attention for assistive and self-driving systems and detect drivers’ gaze for monitoring applications. Furthermore, they provide a brief theoretical background on visual attention in the driving domain, survey public datasets with attention-related annotations, and discuss remaining open problems in this research area.
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