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.
@article{arxiv.2508.07560,
title = {Progressive Bird's Eye View Perception for Safety-Critical Autonomous Driving: A Comprehensive Survey},
author = {Yan Gong and Naibang Wang and Jianli Lu and Xinyu Zhang and Yongsheng Gao and Jie Zhao and Zifan Huang and Haozhi Bai and Nanxin Zeng and Nayu Su and Lei Yang and Ziying Song and Xiaoxi Hu and Xinmin Jiang and Xiaojuan Zhang and Susanto Rahardja},
journal= {arXiv preprint arXiv:2508.07560},
year = {2025}
}