自动驾驶中3D目标检测的综合综述：技术进展与未来方向
A Comprehensive Review of 3D Object Detection in Autonomous Driving: Technological Advances and Future Directions
AI总结：
本文全面综述了自动驾驶中基于相机、LiDAR及融合检测的传统3D目标检测方法，分析了各方法优劣，并探讨了时序感知、占据网格、端到端学习及合作感知等未来发展方向。
摘要：
近年来，3D目标感知已成为自动驾驶系统发展中的关键组成部分，提供了必不可少的环境感知能力。然而，随着自动驾驶中感知任务的演进，其变体不断增加，导致了来自工业界和学术界的多样化见解。目前，缺乏从更广阔视角收集和总结这些感知任务及其发展的全面综述。本综述广泛总结了传统的3D目标检测方法，重点关注基于相机、基于LiDAR和融合检测技术。我们对每种方法的优势和局限性进行了全面分析，突出了在准确性和鲁棒性方面的进展。此外，我们讨论了未来方向，包括提高准确性的方法，如时序感知、占据网格和端到端学习框架。我们还探索了通过协作通信扩展感知范围的合作感知方法。通过提供3D目标感知现状和未来发展的整体视角，我们旨在为自动驾驶的感知任务提供更全面的理解。此外，我们建立了一个活跃的存储库，以提供该领域最新进展的持续更新，访问地址：https://github.com/Fishsoup0/Autonomous-Driving-Perception。
英文摘要：
In recent years, 3D object perception has become a crucial component in the development of autonomous driving systems, providing essential environmental awareness. However, as perception tasks in autonomous driving evolve, their variants have increased, leading to diverse insights from industry and academia. Currently, there is a lack of comprehensive surveys that collect and summarize these perception tasks and their developments from a broader perspective. This review extensively summarizes traditional 3D object detection methods, focusing on camera-based, LiDAR-based, and fusion detection techniques. We provide a comprehensive analysis of the strengths and limitations of each approach, highlighting advancements in accuracy and robustness. Furthermore, we discuss future directions, including methods to improve accuracy such as temporal perception, occupancy grids, and end-to-end learning frameworks. We also explore cooperative perception methods that extend the perception range through collaborative communication. By providing a holistic view of the current state and future developments in 3D object perception, we aim to offer a more comprehensive understanding of perception tasks for autonomous driving. Additionally, we have established an active repository to provide continuous updates on the latest advancements in this field, accessible at: https://github.com/Fishsoup0/Autonomous-Driving-Perception.
机构、总结、中文摘要均由AI分析而来，可能存在错误，请以论文正文为准。
