Abstract
In order to observe driver’s attention levels, different approaches are followed. They include simple methods counting driver input changes [6], machine learning based approaches based on driver input [17], and methods considering additional inputs such as environmental data and eye tracking data [3,4,5, 7, 12, 16]. Recent studies have proposed geopositioned 3D AOIs as a tool for driver intention observation. Geopositioned 3D AOIs are three dimensional Areas (boxes), with fix geopositiones (e.g. GPS) which have to be observed for a safe completion of driving maneuvers. Examples are pedestrian waiting areas, crosswalks, and traffic light. Creating these AOIs by hand is a tedious task with ample room for potential errors, as the created AOIs might differ from the real AOIs drivers look at. We therefore propose a pipeline to generate real 3D AOIs from gaze clouds. To generate relevant gaze clouds we use the points of closest encounter in fleet gaze data collected in a driving simulator setup. The results show that the generation of 3D AOIs from fleet data is possible and the created AOIs are mostly consistent with the expected AOIs.
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Bickerdt, J., Gollnick, C., Sonnenberg, J., Kasneci, E. (2022). Creating Geopositioned 3D Areas of Interest from Fleet Gaze Data. In: Krömker, H. (eds) HCI in Mobility, Transport, and Automotive Systems. HCII 2022. Lecture Notes in Computer Science, vol 13335. Springer, Cham. https://doi.org/10.1007/978-3-031-04987-3_2
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