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.

翻译：非结构化户外环境中的自动驾驶研究相较于结构化城市环境进展较慢，这主要归因于环境多样性和场景复杂性等挑战。农村地区和崎岖地形等环境带来了结构化城市区域不常见的独特障碍。尽管存在这些困难，非结构化户外环境中的自动驾驶在农业、采矿和军事行动等领域具有关键应用价值。本综述分析了250余篇关于非结构化户外环境自动驾驶的论文，涵盖离线建图、位姿估计、环境感知、路径规划、端到端自动驾驶、数据集及相关挑战。我们还探讨了新兴趋势和未来研究方向。本文旨在整合该领域知识并推动非结构化环境自动驾驶的进一步研究。为支持持续研究，我们在以下地址维护包含最新文献和开源项目的动态资源库：https://github.com/chaytonmin/Survey-Autonomous-Driving-in-Unstructured-Environments。