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In computer vision research, public datasets are crucial to objectively assess new algorithms. By the wide use of deep learning methods to solve computer vision problems, large-scale datasets are indispensable for proper network training. Various driver-centered analysis depend on accurate head pose and gaze estimation. In this paper, we present a new large-scale dataset, AutoPOSE. The dataset provides∼ 1.1 M IR images taken from the dashboard view, and∼ 315K from Kinect v2 (RGB, IR, Depth) taken from center mirror view. AutoPOSE’s ground truth-head orientation and position-was acquired with a sub-millimeter accurate motion capturing system. Moreover, we present a head orientation estimation baseline with a state-of-the-art method on our AutoPOSE dataset. We provide the dataset as a downloadable package from a public website.