Abstract
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$^\circ$ field of view driver attention dataset, containing $\sim$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 this https URL.
Abstract (translated)
预测驾驶员的注意力是开发可解释的自动驾驶系统和理解混合有人类驾驶与自主车辆交通场景中驾驶员行为的关键问题。尽管通过大规模驾驶员注意数据集和深度学习架构取得了显著进展，但现有的研究工作受到狭窄前方视野和有限驾驶多样性的限制，从而无法捕捉到完整的驾驶环境空间背景，特别是在变道、转弯以及涉及行人或骑自行车者等周边物体互动时。 在本文中，我们介绍了DriverGaze360，这是一个大规模的360°视场驾驶员注意力数据集，包含由19名人类驾驶员收集的大约100万张带有注视标签的图像帧。这使得能够对驾驶员的视线行为进行全面的全方位建模。此外，我们的全景注意预测方法——DriverGaze360-Net 通过采用辅助语义分割头，共同学习注意力图和被关注的对象，从而改善了在宽广全景输入中的空间感知能力和注意预测。广泛的实验表明，DriverGaze360-Net 在多个指标上实现了全景驾驶图像中注意预测的最先进的性能。 数据集和方法可在以下链接获取：[请在此处插入URL]。
URL
https://arxiv.org/abs/2512.14266
