SID: Stereo Image Dataset for Autonomous Driving in Adverse Conditions

TL;DR

The paper introduces SID, a comprehensive stereo image dataset capturing diverse adverse weather and lighting conditions for autonomous driving, enabling improved perception algorithms in challenging environments.

Contribution

It provides a large-scale, annotated stereo image dataset with challenging real-world conditions, filling a gap in autonomous driving research resources.

Findings

Dataset includes over 178,000 stereo image pairs.

Captures diverse weather and lighting conditions.

Includes detailed annotations for various environmental factors.

Abstract

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.