  Robust perception is critical for autonomous driving, especially under
adverse weather and lighting conditions that commonly occur in real-world
environments. In this paper, we introduce the Stereo Image Dataset (SID), a
large-scale stereo-image dataset that captures a wide spectrum of challenging
real-world environmental scenarios. Recorded at a rate of 20 Hz using a ZED
stereo camera mounted on a vehicle, SID consists of 27 sequences totaling over
178k stereo image pairs that showcase conditions from clear skies to heavy
snow, captured during the day, dusk, and night. The dataset includes detailed
sequence-level annotations for weather conditions, time of day, location, and
road conditions, along with instances of camera lens soiling, offering a
realistic representation of the challenges in autonomous navigation. Our work
aims to address a notable gap in research for autonomous driving systems by
presenting high-fidelity stereo images essential for the development and
testing of advanced perception algorithms. These algorithms support consistent
and reliable operation across variable weather and lighting conditions, even
when handling challenging situations like lens soiling. SID is publicly
available at: https://doi.org/10.7302/esz6-nv83.
