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

TL;DR

This survey reviews the progress and challenges of Bird's-Eye-View perception in autonomous driving, emphasizing safety-critical aspects, multi-stage frameworks, datasets, and future research directions.

Contribution

It provides the first comprehensive safety-focused review of BEV perception, analyzing frameworks, datasets, and open challenges across multiple perception stages.

Findings

BEV perception is crucial for autonomous driving safety.

Current datasets and methods face challenges like occlusions and sensor degradation.

Open research directions include multi-agent collaboration and integration with autonomous systems.

Abstract

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.