  A driver's gaze is critical for determining their attention, state,
situational awareness, and readiness to take over control from partially
automated vehicles. Estimating the gaze direction is the most obvious way to
gauge a driver's state under ideal conditions when limited to using
non-intrusive imaging sensors. Unfortunately, the vehicular environment
introduces a variety of challenges that are usually unaccounted for - harsh
illumination, nighttime conditions, and reflective eyeglasses. Relying on head
pose alone under such conditions can prove to be unreliable and erroneous. In
this study, we offer solutions to address these problems encountered in the
real world. To solve issues with lighting, we demonstrate that using an
infrared camera with suitable equalization and normalization suffices. To
handle eyeglasses and their corresponding artifacts, we adopt image-to-image
translation using generative adversarial networks to pre-process images prior
to gaze estimation. Our proposed Gaze Preserving CycleGAN (GPCycleGAN) is
trained to preserve the driver's gaze while removing potential eyeglasses from
face images. GPCycleGAN is based on the well-known CycleGAN approach - with the
addition of a gaze classifier and a gaze consistency loss for additional
supervision. Our approach exhibits improved performance, interpretability,
robustness and superior qualitative results on challenging real-world datasets.
