越野自动驾驶数据集ORAD-3D

We present ORAD-3D, which, to the best of our knowledge, is the largest dataset specifically curated for off-road autonomous driving. ORAD-3D covers a wide spectrum of terrains, including woodlands, farmlands, grasslands, riversides, gravel roads, cement roads, and rural areas, while capturing diverse environmental variations across weather conditions (sunny, rainy, foggy, and snowy) and illumination levels (bright daylight, daytime, twilight, and nighttime). Building upon this dataset, we establish a comprehensive suite of benchmark evaluations spanning five fundamental tasks: 2D free-space detection, 3D occupancy prediction, rough GPS-guided path planning, vision-language model-driven autonomous driving, and world model for off-road environments. Together, the dataset and benchmarks provide a unified and robust resource for advancing perception and planning in challenging off-road scenarios.Dataset file metadata and data files can be obtained by browsing the "Dataset Files" page. This dataset card uses the default template, and the dataset contributors have not provided a more detailed introduction to the dataset. However, you can download the dataset via the following GIT Clone command or ModelScope SDK. #### Download Methods :modelscope-code[]{type="sdk"} :modelscope-code[]{type="git"}

搜集汇总

背景与挑战

背景概述

该数据集名为ORAD-3D，由Georgehhhh贡献，专注于越野自动驾驶领域，发布于2026年。它采用Apache 2.0许可证，体积为325.47GB，可通过ModelScope平台下载。