arXiv 2508.075602025-08-11cs.ROcs.CV

面向安全关键自动驾驶的渐进式鸟瞰图感知：全面综述

Progressive Bird's Eye View Perception for Safety-Critical Autonomous Driving: A Comprehensive Survey

State Key Laboratory of Robotics and System（机器人系统国家重点实验室）；Harbin Institute of Technology（哈尔滨工业大学）；State Key Laboratory of Intelligent Green Vehicle and Mobility（智能绿色车辆与移动性国家重点实验室）；Tsinghua University（清华大学）；the School of Mechanical and Aerospace Engineering（机械与航空航天工程学院）；Nanyang Technological University（南洋理工大学）；Beijing Key Laboratory of Traffic Data Mining and Embodied Intelligence（北京交通数据挖掘与具身智能重点实验室）；Beijing Jiaotong University（北京交通大学）；the Institute for Infocomm Research（信息通信研究所）；A*STAR；the Engineering Cluster（工程集群）；the Singapore Institute of Technology（新加坡理工学院）

Yan Gong, Naibang Wang, Jianli Lu, Xinyu Zhang, Yongsheng Gao, Jie Zhao, Zifan Huang, Haozhi Bai, Nanxin Zeng, Nayu Su, Lei Yang, Ziying Song, Xiaoxi Hu, Xinmin… 展开作者

Yan Gong, Naibang Wang, Jianli Lu, Xinyu Zhang, Yongsheng Gao, Jie Zhao, Zifan Huang, Haozhi Bai, Nanxin Zeng, Nayu Su, Lei Yang, Ziying Song, Xiaoxi Hu, Xinmin Jiang, Xiaojuan Zhang, Susanto Rahardja

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AI总结：

本综述首次从安全关键角度全面回顾了自动驾驶中的BEV感知，系统分析了单模态车端、多模态车端及多智能体协作三个渐进阶段的框架，评估了相关数据集，并指出了开放世界挑战与未来研究方向。

摘要：

鸟瞰图（BEV）感知已成为自动驾驶的基础范式，能够提供统一的空间表示，支持稳健的多传感器融合与多智能体协作。随着自动驾驶车辆从受控环境向真实世界部署过渡，确保BEV感知在遮挡、恶劣天气和动态交通等复杂场景中的安全性与可靠性仍是一项关键挑战。本综述首次从安全关键角度对BEV感知进行了全面回顾，系统分析了三个渐进阶段的最新框架与实现策略：单模态车端、多模态车端以及多智能体协作感知。此外，我们考察了涵盖车端、路端和协作设置的公开数据集，评估其与安全性和鲁棒性的相关性。我们还指出了关键的开放世界挑战——包括开集识别、大规模无标签数据、传感器退化及智能体间通信延迟——并规划了未来研究方向，如与端到端自动驾驶系统、具身智能和大语言模型的集成。

英文摘要：

Bird's-Eye-View (BEV) perception has become a foundational paradigm in autonomous driving, enabling unified spatial representations that support robust multi-sensor fusion and multi-agent collaboration. As autonomous vehicles transition from controlled environments to real-world deployment, ensuring the safety and reliability of BEV perception in complex scenarios - such as occlusions, adverse weather, and dynamic traffic - remains a critical challenge. This survey provides the first comprehensive review of BEV perception from a safety-critical perspective, systematically analyzing state-of-the-art frameworks and implementation strategies across three progressive stages: single-modality vehicle-side, multimodal vehicle-side, and multi-agent collaborative perception. Furthermore, we examine public datasets encompassing vehicle-side, roadside, and collaborative settings, evaluating their relevance to safety and robustness. We also identify key open-world challenges - including open-set recognition, large-scale unlabeled data, sensor degradation, and inter-agent communication latency - and outline future research directions, such as integration with end-to-end autonomous driving systems, embodied intelligence, and large language models.

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。