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.
@article{arxiv.2510.16500,
title = {Advancing Off-Road Autonomous Driving: The Large-Scale ORAD-3D Dataset and Comprehensive Benchmarks},
author = {Chen Min and Jilin Mei and Heng Zhai and Shuai Wang and Tong Sun and Fanjie Kong and Haoyang Li and Fangyuan Mao and Fuyang Liu and Shuo Wang and Yiming Nie and Qi Zhu and Liang Xiao and Dawei Zhao and Yu Hu},
journal= {arXiv preprint arXiv:2510.16500},
year = {2025}
}