自动驾驶中的3D目标检测研究进展

陈建; 苏思教; 黄立勤; 赵铁松

doi:10.12263/DZXB.20250043

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自动驾驶中的3D目标检测研究进展

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- 自动驾驶中的3D目标检测研究进展
- Research Advances on 3D Object Detection in Autonomous Driving
- 电子学报 2025年53卷第6期页码：2131-2156
- 作者机构：
  
  1.福州大学物理与信息工程学院，福建福州 350108
  2.媒体信息智能处理与无线传输福建省重点实验室，福建福州 350108
- 作者简介：
  
  [ "陈建女，1981年生.博士，福州大学物理与信息工程学院副教授、硕士生导师.主要研究方向为视频编码、压缩感知、点云压缩和目标跟踪. E-mail: chenjian-fzu@163.com" ]
  [ "苏思教男，2000年生.福州大学物理与信息工程学院硕士研究生.主要研究方向为3D目标检测. E-mail: 867225883@qq.com" ]
  [ "黄立勤男，1973年生.博士，福州大学物理与信息工程学院教授、博士生导师.主要研究方向为高性能计算、人工智能与机器学习、医学图像处理等.中国电子学会会员编号：E190055471M.E-mail: hlq@fzu.edu.cn" ]
  [ "赵铁松男，1984年生.博士，福州大学物理与信息工程学院教授、博士生导师.主要研究方向为多媒体通信系统、人工智能和视频编码等.中国电子学会会员编号：E190014840S.E-mail: t.zhao@fzu.edu.cn" ]
- 基金信息：
  
  国家自然科学基金(62171134;62271149);福州市科技项目(2023-P-001)
- DOI：10.12263/DZXB.20250043
  中图分类号： TP391.4;
- 收稿：2025-01-10，
  
  修回：2025-02-28，
  
  纸质出版：2025-06-25
- 稿件说明：
移动端阅览
陈建, 苏思教, 黄立勤, 等. 自动驾驶中的3D目标检测研究进展[J]. 电子学报, 2025, 53(06): 2131-2156.

CHEN Jian, SU Si-jiao, HUANG Li-qin, et al. Research Advances on 3D Object Detection in Autonomous Driving[J]. Acta Electronica Sinica, 2025, 53(06): 2131-2156.
陈建, 苏思教, 黄立勤, 等. 自动驾驶中的3D目标检测研究进展[J]. 电子学报, 2025, 53(06): 2131-2156. DOI：10.12263/DZXB.20250043

CHEN Jian, SU Si-jiao, HUANG Li-qin, et al. Research Advances on 3D Object Detection in Autonomous Driving[J]. Acta Electronica Sinica, 2025, 53(06): 2131-2156. DOI：10.12263/DZXB.20250043

摘要

近年来，自动驾驶因其在提升道路安全、提高交通效率等方面展现出巨大的潜力而受到越来越多的关注.在现代自动驾驶系统中，感知系统扮演着至关重要的角色，其目标是准确地估计周围环境的状态，并为预测和规划提供可靠的观测信息.其中，3D目标检测作为感知系统的重要组成部分，旨在预测自动驾驶车辆周围物体的位置、大小和类别.本文归纳了近年来自动驾驶领域中3D目标检测的研究进展，从单模态检测和多模态融合检测两个角度出发，介绍了使用不同传感器进行单模态方法和多模态融合方法的优势和不足.此外，本文还对比了各种代表性算法在公共数据集上的性能，总结了当前常用训练策略，并讨论了该领域未来的发展趋势.

Abstract

In recent years

autonomous driving has gained increasing attention due to its significant potential in improving road safety and enhancing traffic efficiency. The perception system plays a crucial role in modern autonomous driving systems

aiming to accurately estimate the surrounding environment’s state and provide reliable observations for prediction and planning. Among them

3D object detection serves as an important component of the perception system for predicting the positions

sizes

and categories of objects surrounding the autonomous vehicle. This paper provides a comprehensive overview of the research advancements in 3D object detection for autonomous driving in recent years. It discusses the advantages and limitations of single-modal methods and multi-modal fusion methods using different sensors from the perspectives of single-modal detection and multi-modal fusion detection. Furthermore

the paper compares the performance of various representative algorithms on public datasets

summarizes the current commonly used training strategies

and discusses the future development directions in this field.

关键词

Keywords

references

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