面向自动驾驶的网格中心交通场景感知：综合综述
Grid-Centric Traffic Scenario Perception for Autonomous Driving: A Comprehensive Review
AI总结：
本文系统综述了自动驾驶中网格中心感知技术，梳理了从2D BEV网格、3D占用到4D占用预测的发展脉络，并总结了标签高效学习、系统集成及未来趋势。
摘要：
网格中心感知是移动机器人感知与导航的关键领域。然而，与目标中心感知相比，网格中心感知的普及程度较低，因为自动驾驶车辆需要精确感知高度动态、大规模交通场景，且网格中心感知的复杂性和计算成本较高。近年来，深度学习技术和硬件的快速发展为网格中心感知的演进提供了新的视角。网格中心与目标中心流程的根本区别在于，网格中心感知遵循几何优先范式，该范式对开放世界驾驶场景中无穷无尽的长尾语义未知障碍物更具鲁棒性。近期研究展示了网格中心感知的巨大优势，例如全面的细粒度环境表示、对遮挡和不规则形状物体更强的鲁棒性、更好的地面估计以及更安全的规划策略。此外，一个日益增长的趋势是占用网络的能力被大幅扩展到4D场景感知与预测，并且最新技术与自动驾驶领域中的4D占用预测、生成式AI和世界模型等新研究主题高度相关。鉴于当前缺乏针对这一快速扩展领域的综述，我们对自动驾驶车辆的网格中心感知进行了层次化结构的综述。我们沿着从2D BEV网格到3D占用再到4D占用预测的主线，梳理了以往和当前的占用网格技术知识。我们还总结了标签高效的占用学习以及网格中心感知在驾驶系统中的作用。最后，我们总结了当前研究趋势并提供了未来展望。
英文摘要：
Grid-centric perception is a crucial field for mobile robot perception and navigation. Nonetheless, grid-centric perception is less prevalent than object-centric perception as autonomous vehicles need to accurately perceive highly dynamic, large-scale traffic scenarios and the complexity and computational costs of grid-centric perception are high. In recent years, the rapid development of deep learning techniques and hardware provides fresh insights into the evolution of grid-centric perception. The fundamental difference between grid-centric and object-centric pipeline lies in that grid-centric perception follows a geometry-first paradigm which is more robust to the open-world driving scenarios with endless long-tailed semantically-unknown obstacles. Recent researches demonstrate the great advantages of grid-centric perception, such as comprehensive fine-grained environmental representation, greater robustness to occlusion and irregular shaped objects, better ground estimation, and safer planning policies. There is also a growing trend that the capacity of occupancy networks are greatly expanded to 4D scene perception and prediction and latest techniques are highly related to new research topics such as 4D occupancy forecasting, generative AI and world models in the field of autonomous driving. Given the lack of current surveys for this rapidly expanding field, we present a hierarchically-structured review of grid-centric perception for autonomous vehicles. We organize previous and current knowledge of occupancy grid techniques along the main vein from 2D BEV grids to 3D occupancy to 4D occupancy forecasting. We additionally summarize label-efficient occupancy learning and the role of grid-centric perception in driving systems. Lastly, we present a summary of the current research trend and provide future outlooks.
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。
