Autonomous Driving in Unstructured Off‐Road Environments: How Far Have We Come?
Chen Min, Shubin Si, Xu Wang, Hanzhang Xue, Weizhong Jiang, Zitong Chen, Mengmeng Li, Jilin Mei 等 17 位
Chinese Academy of Sciences Institute of Computing Technology Harbin Engineering University National University of Defense Technology
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摘要与影响
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 of up‐to‐date literature and open‐source projects at: https://github.com/chaytonmin/Survey‐Autonomous‐Driving‐in‐Unstructured‐Environments .
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工程Autonomous Vehicle Technology and Safety
Robotics and Sensor-Based Localization · Reinforcement Learning in Robotics
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