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.

自动驾驶在计算硬件和先进的深度学习方法的推动下取得了显著进展。然而现有的公共数据集却缺乏多样性的天气场景和详细高分辨率数据，用以训练和验证先进的自动驾驶感知模型。本研究介绍了 PLT-D3 数据集，其提供了逼真的模拟测试环境，包括雨雪、雾和各种光照条件，以增强自动驾驶系统在不同天气条件下的适应性。 PLT-D3 基于 Unreal Engine 5 生成了高保真度的立体深度和场景流动地真实数据，用来建立具有现实天气变化模拟的全面训练和测试资源。通过 PLT-D3，对深度估计、光流和场景流流动等关键自动驾驶任务建立了基准，以衡量和提升最先进模型的性能。