发布首个大规模越野自动驾驶数据集，推动复杂地形感知与规划研究。
Advancing Off-Road Autonomous Driving: The Large-Scale ORAD-3D Dataset and Comprehensive Benchmarks
- 构建覆盖多种地形与环境的3D数据集，涵盖12类场景。
- 建立5项核心任务基准，包括3D占位预测与视觉语言导航。
- 适合研究越野自动驾驶的感知、规划与多模态模型团队使用。
越野自动驾驶研究的主要瓶颈在于缺乏大规模、高质量的数据集与基准。为此，我们提出ORAD-3D，据我们所知，这是首个专为越野自动驾驶设计的大型数据集。该数据集涵盖林地、农田、草地、河岸、碎石路、水泥路及乡村等多种地形，并覆盖晴天、雨天、雾天、雪天等天气条件以及明亮日光、白天、黄昏和夜晚等光照水平。基于此数据集，我们建立了涵盖五大核心任务的综合评估体系：2D自由空间检测、3D占位预测、粗略GPS引导路径规划、视觉语言模型驱动的自动驾驶，以及越野环境的世界模型。该数据集与基准共同提供了一个统一且可靠的资源，助力复杂越野场景下的感知与规划技术发展。数据集与代码将公开于https://github.com/chaytonmin/ORAD-3D。
原文摘要 · Abstract (English)
A major bottleneck in off-road autonomous driving research lies in the scarcity of large-scale, high-quality datasets and benchmarks. To bridge this gap, 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. The dataset and code will be made publicly available at https://github.com/chaytonmin/ORAD-3D.
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