[1]WU Yiquan,CAI Jiaqi.Deep learning-based 3D object detection for autonomous driving：a comprehensive review[J].CAAI Transactions on Intelligent Systems,2026,21(2):297-320.[doi:10.11992/tis.202504021]
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Deep learning-based 3D object detection for autonomous driving：a comprehensive review

CAAI Transactions on Intelligent Systems[ISSN 1673-4785/CN 23-1538/TP] Volume: 21 Number of periods: 2026 2 Page number: 297-320 Column: 综述 Public date: 2026-04-30
Title:
Deep learning-based 3D object detection for autonomous driving：a comprehensive review
Author(s):
WU Yiquan;  CAI Jiaqi
School of Electronic Information Engineering, Nanjing University of Aeronautics and Astronautics, Nanjing 211106, China
Keywords:
autonomous driving; 3D object detection; deep learning; point cloud; multi-sensor fusion; convolutional neural network; dataset; performance evaluation metrics
CLC:
TP391.41
DOI:
10.11992/tis.202504021
Abstract:
The rapid advancement of autonomous driving technology has increasingly heightened the demands for the accuracy and real-time performance of vehicle perception systems. 3D Object Detection, as a core component of vehicle perception systems, is of vital importance for ensuring driving safety and enhancing the driving experience. Firstly, 3D object detection algorithms are categorized into three types based on the data types acquired by sensors: Visual algorithms encompass subcategories based on 2D and 3D features; LiDAR point cloud algorithms cover grid-based point clouds, raw point clouds, and hybrid point cloud approaches; multi-sensor-based algorithms are classified based on the modes of serial and parallel fusion of the network. Accordingly, the features, contributions, and limitations of specific algorithms are summarized. Subsequently, typical 3D object detection datasets and their evaluation metrics are reviewed, and the performance of representative algorithms on different datasets is compared. Finally, the current technical challenges are analyzed, and the future development directions are prospected.
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