Deep learning-based 3D object detection for autonomous driving：a comprehensive review

WU Yiquan; CAI Jiaqi

doi:10.11992/tis.202504021

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Deep learning-based 3D object detection for autonomous driving：a comprehensive review

更新时间：2026-05-01

- Deep learning-based 3D object detection for autonomous driving：a comprehensive review
- Vol. 21, Issue 2, Pages: 297-320(2026)
- 作者机构：
- 作者简介：
- 基金信息：
- DOI：10.11992/tis.202504021
  CLC： TP391.41
- Online First：05 March 2026，
  
  Published：2026
- 稿件说明：
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[1]吴一全,蔡佳琦.自动驾驶中深度学习的三维目标检测方法综述[J].智能系统学报,2026,21(2):297-320.

[doi:10.11992/tis.202504021]
[1]吴一全,蔡佳琦.自动驾驶中深度学习的三维目标检测方法综述[J].智能系统学报,2026,21(2):297-320. DOI： 10.11992/tis.202504021.

[doi:10.11992/tis.202504021] DOI：

摘要

自动驾驶技术的快速发展对车辆感知系统准确性和实时性的要求日益提升。三维目标检测作为车辆感知系统的核心组成部分，对于确保行车安全和提升驾驶体验至关重要。首先将三维目标检测算法按传感器所获取的数据类型分为3类：视觉算法(包括基于二维特征和三维特征的子类)、激光点云算法(涵盖网格化点云、原始点云和混合点云)、基于多传感器的算法(按照网络串行融合和并行融合的方式进行分类)。据此总结了具体算法的特点、贡献及局限性。随后，介绍了典型三维目标检测数据集及其评价指标，并比较了代表性算法在不同数据集上的性能。最后，分析了当前技术面临的挑战，并对未来发展方向进行了展望。

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.

关键词

Keywords

references

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