摘要
研究者关注利用多个传感器来提升自动驾驶中目标检测模型的准确率,因此对目标检测中的数据融合方法进行研究具有重要的学术和应用价值。为此,本文总结了近年来自动驾驶中深度目标检测模型中的数据融合方法。首先介绍了自动驾驶中深度目标检测技术和数据融合技术的发展,以及已有的研究综述;接着从多模态目标检测、数据融合的层次、数据融合的计算方法3个方面展开阐述,全面展现了该领域的前沿进展;此外,本文提出了数据融合的合理性分析,从方法、鲁棒性、冗余性3个角度对数据融合方法进行了讨论;最后讨论了融合方法的一些公开问题,并从挑战、策略和前景等方面作了总结。
In autonomous driving,there has been an increasing interest in utilizing multiple sensors to improve the accuracy of object detection models.Accordingly,the research on data fusion has important academic and application value.This paper summarizes the data fusion methods in deep object detection models of autonomous driving in recent years.The paper first introduces the development of deep object detection and data fusion in autonomous driving,as well as existing researches and reviews,then expounds from three aspects of multi-modal object detection,fusion levels and calculation methods,comprehensively showing the cutting-edge progress in this field.In addition,this paper proposes a rationality analysis of data fusion from another three perspectives:methods,robustness and redundancy.Finally,open issues are discussed,and the challenges,strategy and prospects are summarized.
作者
张新钰
邹镇洪
李志伟
刘华平
李骏
ZHANG Xinyu;ZOU Zhenhong;LI Zhiwei;LIU Huaping;LI Jun(State Key Laboratory of Automotive Safety and Energy,Tsinghua University,Beijing 100084,China;School of Vehicle and Mobility,Tsinghua University,Beijing 100084,China;Department of Computer Science and Technology,Tsinghua University,Beijing 100084,China)
出处
《智能系统学报》
CSCD
北大核心
2020年第4期758-771,共14页
CAAI Transactions on Intelligent Systems
基金
国家重点研发计划项目(2018YFE0204300)
北京市科技计划项目(Z191100007419008)
国强研究院项目(2019GQG1010).
关键词
数据融合
目标检测
自动驾驶
深度学习
多模态
感知
计算机视觉
传感器
综述
data fusion
object detection
autonomous driving
deep learning
multimodal
perception
computer vision
sensor
survey
