Delving Into the Secrets of BEV 3D Object Detection in Autonomous Driving: A Comprehensive Survey

3D object detection plays a crucial role in autonomous driving, with Bird’s Eye View (BEV) becoming increasingly popular for its rich contextual information, ease of multi-modal fusion, and scalability. Despite its advantages, current BEV-based 3D detection methods still face significant challenges, including multi-modal fusion, communication bottlenecks, robustness under varying conditions, and safety concerns. This survey provides a systematic review of recent advancements in BEV perception, and meanwhile organizes a research based on these focal areas. It spans a broad range of perspectives, offering valuable insights for future perception research. Additionally, this survey explores the influence of emerging technologies, such as large language models and end-to-end frameworks on enhancing BEV perception capabilities, focusing on improving performance and robustness. Key future directions would include: 1) advancement from isolated vehicle perception to vehicle-to-everything (V2X) cooperative perception; 2) evolution from single-modal to integrated multi-modal fusion; 3) shift from simulated environments to real-world applications; and 4) transition from hierarchical perception frameworks to interpretable, end-to-end large-scale models.