Autonomous Driving in Unstructured Environments: How Far Have We Come?
<|reference_start|>Autonomous Driving in Unstructured Environments: How Far Have We Come?: Research on autonomous driving in unstructured outdoor environments is less advanced than in structured urban settings due to challenges like environmental diversities and scene complexity. These environments-such as rural areas and rugged terrains-pose unique obstacles that are not common in structured urban areas. Despite these difficulties, autonomous driving in unstructured outdoor environments is crucial for applications in agriculture, mining, and military operations. Our survey reviews over 250 papers for autonomous driving in unstructured outdoor environments, covering offline mapping, pose estimation, environmental perception, path planning, end-to-end autonomous driving, datasets, and relevant challenges. We also discuss emerging trends and future research directions. This review aims to consolidate knowledge and encourage further research for autonomous driving in unstructured environments. To support ongoing work, we maintain an active repository with up-to-date literature and open-source projects at: https://github.com/chaytonmin/Survey-Autonomous-Driving-in-Unstructured-Environments.<|reference_end|>
@article{min2024autonomous,
    title={Autonomous Driving in Unstructured Environments: How Far Have We Come?},
    author={Chen Min, Shubin Si, Xu Wang, Hanzhang Xue, Weizhong Jiang, Yang Liu,
  Juan Wang, Qingtian Zhu, Qi Zhu, Lun Luo, Fanjie Kong, Jinyu Miao, Xudong
  Cai, Shuai An, Wei Li, Jilin Mei, Tong Sun, Heng Zhai, Qifeng Liu, Fangzhou
  Zhao, Liang Chen, Shuai Wang, Erke Shang, Linzhi Shang, Kunlong Zhao, Fuyang
  Li, Hao Fu, Lei Jin, Jian Zhao, Fangyuan Mao, Zhipeng Xiao, Chengyang Li, Bin
  Dai, Dawei Zhao, Liang Xiao, Yiming Nie, Yu Hu, Xuelong Li},
    journal={arXiv preprint arXiv:2410.07701},
    year={2024},
    archivePrefix={arXiv},
    eprint={2410.07701},
    primaryClass={cs.RO}
}