  Driver's eye gaze holds a wealth of cognitive and intentional cues crucial
for intelligent vehicles. Despite its significance, research on in-vehicle gaze
estimation remains limited due to the scarcity of comprehensive and
well-annotated datasets in real driving scenarios. In this paper, we present
three novel elements to advance in-vehicle gaze research. Firstly, we introduce
IVGaze, a pioneering dataset capturing in-vehicle gaze, collected from 125
subjects and covering a large range of gaze and head poses within vehicles.
Conventional gaze collection systems are inadequate for in-vehicle use. In this
dataset, we propose a new vision-based solution for in-vehicle gaze collection,
introducing a refined gaze target calibration method to tackle annotation
challenges. Second, our research focuses on in-vehicle gaze estimation
leveraging the IVGaze. In-vehicle face images often suffer from low resolution,
prompting our introduction of a gaze pyramid transformer that leverages
transformer-based multilevel features integration. Expanding upon this, we
introduce the dual-stream gaze pyramid transformer (GazeDPTR). Employing
perspective transformation, we rotate virtual cameras to normalize images,
utilizing camera pose to merge normalized and original images for accurate gaze
estimation. GazeDPTR shows state-of-the-art performance on the IVGaze dataset.
Thirdly, we explore a novel strategy for gaze zone classification by extending
the GazeDPTR. A foundational tri-plane and project gaze onto these planes are
newly defined. Leveraging both positional features from the projection points
and visual attributes from images, we achieve superior performance compared to
relying solely on visual features, substantiating the advantage of gaze
estimation. Our project is available at https://yihua.zone/work/ivgaze.
