TY - GEN
T1 - Geopositioned 3D areas of interest for gaze analysis
AU - Bickerdt, Jan
AU - Sonnenberg, Jan
AU - Gollnick, Christian
AU - Kasneci, Enkelejda
N1 - Publisher Copyright:
© 2021 Association for Computing Machinery.
PY - 2021/9/9
Y1 - 2021/9/9
N2 - To understand driver's gaze behavior, the gaze is usually matched to surrounding objects or static areas of interest (AOI) at fixed positions around the car. Full surround object tracking allows for an understanding of the traffic situation. However, because it requires an extensive sensor set and a lot of processing power, it's not yet broadly available in production cars. The use of static AOIs only requires the addition of eye tracking sensors. They are at fixed positions around the car and can't adapt to the environment, therefore their usefulness is limited. We propose geopositioned 3D AOIs. With adaptability and the use of a small sensor set, they combine the strengths of both methods. To test 3D AOIs' capabilities for gaze analysis, a driving simulator study with 74 participants was conducted. We show that 3D AOIs are suitable for driver's gaze analysis and a promising tool for driver intention prediction.
AB - To understand driver's gaze behavior, the gaze is usually matched to surrounding objects or static areas of interest (AOI) at fixed positions around the car. Full surround object tracking allows for an understanding of the traffic situation. However, because it requires an extensive sensor set and a lot of processing power, it's not yet broadly available in production cars. The use of static AOIs only requires the addition of eye tracking sensors. They are at fixed positions around the car and can't adapt to the environment, therefore their usefulness is limited. We propose geopositioned 3D AOIs. With adaptability and the use of a small sensor set, they combine the strengths of both methods. To test 3D AOIs' capabilities for gaze analysis, a driving simulator study with 74 participants was conducted. We show that 3D AOIs are suitable for driver's gaze analysis and a promising tool for driver intention prediction.
KW - Areas of interest
KW - Automotive
KW - Driving simulation
KW - Eye tracking
UR - http://www.scopus.com/inward/record.url?scp=85116245136&partnerID=8YFLogxK
U2 - 10.1145/3409118.3475138
DO - 10.1145/3409118.3475138
M3 - Conference contribution
AN - SCOPUS:85116245136
T3 - Proceedings - 13th International ACM Conference on Automotive User Interfaces and Interactive Vehicular Applications, AutomotiveUI 2021
SP - 1
EP - 11
BT - Proceedings - 13th International ACM Conference on Automotive User Interfaces and Interactive Vehicular Applications, AutomotiveUI 2021
PB - Association for Computing Machinery, Inc
T2 - 13th ACM International Conference on Automotive User Interfaces and Interactive Vehicular Applications, AutomotiveUI 2021
Y2 - 9 September 2021 through 14 September 2021
ER -