综述非结构化环境自动驾驶研究进展，梳理250+论文与关键挑战。
Autonomous Driving in Unstructured Environments: How Far Have We Come?
- 系统梳理250+篇论文，覆盖感知、规划、地图构建等全链条技术
- 指出非结构化场景下环境多样性与复杂性是主要技术瓶颈
- 适合关注农业、采矿、军用自动驾驶的研究者与开发者
非结构化户外环境（如农村地区和崎岖地形）的自动驾驶研究进展落后于城市结构化场景，主要受限于环境多样性与场景复杂性。此类环境面临城市中少见的独特障碍，但对农业、采矿及军事应用至关重要。本综述回顾了超过250篇相关论文，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、数据集及关键挑战。同时分析新兴趋势与未来研究方向。本文旨在整合知识并推动该领域发展。为支持持续研究，作者维护一个活跃的开源文献与项目库：https://github.com/chaytonmin/Survey-Autonomous-Driving-in-Unstructured-Environments。
原文摘要 · Abstract (English)
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.
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