  Autonomous driving has experienced remarkable progress, bolstered by
innovations in computational hardware and sophisticated deep learning
methodologies. The foundation of these advancements rests on the availability
and quality of datasets, which are crucial for the development and refinement
of dependable and versatile autonomous driving algorithms. While numerous
datasets have been developed to support the evolution of autonomous driving
perception technologies, few offer the diversity required to thoroughly test
and enhance system robustness under varied weather conditions. Many public
datasets lack the comprehensive coverage of challenging weather scenarios and
detailed, high-resolution data, which are critical for training and validating
advanced autonomous-driving perception models. In this paper, we introduce
PLT-D3; a Dynamic-weather Driving Dataset, designed specifically to enhance
autonomous driving systems' adaptability to diverse weather conditions. PLT-D3
provides high-fidelity stereo depth and scene flow ground truth data generated
using Unreal Engine 5. In particular, this dataset includes synchronized
high-resolution stereo image sequences that replicate a wide array of dynamic
weather scenarios including rain, snow, fog, and diverse lighting conditions,
offering an unprecedented level of realism in simulation-based testing. The
primary aim of PLT-D3 is to address the scarcity of comprehensive training and
testing resources that can simulate real-world weather variations. Benchmarks
have been established for several critical autonomous driving tasks using
PLT-D3, such as depth estimation, optical flow and scene-flow to measure and
enhance the performance of state-of-the-art models.
