Publikation
DriverGaze360: OmniDirectional Driver Attention with Object-Level Guidance
Didier Stricker Shreedhar Govil
In: 2026 IEEE/CVF Conference on Computer Vision and Pattern Recognition (CVPR) (Hrsg.). Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition (CVPR). International Conference on Computer Vision and Pattern Recognition (CVPR), June 3-7, Denver, Colorado, United States Minor Outlying Islands, IEEE Xplore, 2026.
Zusammenfassung
Predicting driver attention is a critical problem for developing explainable autonomous driving systems and understanding driver behavior in mixed human-autonomous vehicle traffic scenarios. Although significant progress has been made through large-scale driver attention datasets and deep learning architectures, existing works are constrained by narrow frontal field-of-view and limited driving diversity. Consequently, they fail to capture the full spatial context of driving environments, especially during lane changes, turns, and interactions involving peripheral objects such as pedestrians or cyclists. In this paper, we introduce DriverGaze360, a large-scale 360∘ field of view driver attention dataset, containing ∼1 million gaze-labeled frames collected from 19 human drivers, enabling comprehensive omnidirectional modeling of driver gaze behavior. Moreover, our panoramic attention prediction approach, DriverGaze360-Net, jointly learns attention maps and attended objects by employing an auxiliary semantic segmentation head. This improves spatial awareness and attention prediction across wide panoramic inputs. Extensive experiments demonstrate that DriverGaze360-Net achieves state-of-the-art attention prediction performance on multiple metrics on panoramic driving images. Dataset and method available at https://dfki-av.github.io/drivergaze360/.
Projekte
- BERTHA - BEhavioural Replication of Human drivers for CCAM
- COPPER - Kontinuierliche Optische Wahrnehmung für Dynamische Umgebungen in und um Fahrzeuge