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

TY - JOUR

T1 - Delving Into the Secrets of BEV 3D Object Detection in Autonomous Driving

T2 - A Comprehensive Survey

AU - Li, Haoyu

AU - Zhao, Yueran

AU - Zhong, Jiaru

AU - Wang, Bo

AU - Sun, Chao

AU - Sun, Fengchun

N1 - Publisher Copyright: © 2000-2011 IEEE.

PY - 2026

Y1 - 2026

N2 - 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.

AB - 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.

KW - 3D object detection

KW - Autonomous driving

KW - bird’s-eye view

KW - multi-sensor fusion

KW - vehicle-to-everything

UR - https://www.scopus.com/pages/publications/105020935515

U2 - 10.1109/TITS.2025.3624830

DO - 10.1109/TITS.2025.3624830

M3 - Article

AN - SCOPUS:105020935515

SN - 1524-9050

VL - 27

SP - 119

EP - 144

JO - IEEE Transactions on Intelligent Transportation Systems

JF - IEEE Transactions on Intelligent Transportation Systems

IS - 1

ER -