Multi-sensor Fusion and Cooperative Perception for Autonomous Driving A Review

doi:10.1109/MITS.2023.3283864

CORC > 自动化研究所 > 中国科学院自动化研究所 > 多模态人工智能系统全国重点实验室

	Multi-sensor Fusion and Cooperative Perception for Autonomous Driving A Review
	Xiang, Chao 1,5; Feng, Chen 1; Xie, Xiaopo 1; Shi, Botian 2; Lu, Hao 3; Lv, Yisheng 3; Yang, Mingchuan 6; Niu, Zhendong 4
刊名	IEEE INTELLIGENT TRANSPORTATION SYSTEMS MAGAZINE
	2023-08-04
页码	23
关键词	Point cloud compression Laser radar Classification algorithms Sensors Taxonomy Sensor fusion Three-dimensional displays
ISSN号	1939-1390
DOI	10.1109/MITS.2023.3283864
通讯作者	Niu, Zhendong(zniu@bit.edu.cn)
英文摘要	Autonomous driving (AD), including single-vehicle intelligent AD and vehicle-infrastructure cooperative AD, has become a current research hot spot in academia and industry, and multi-sensor fusion is a fundamental task for AD system perception. However, the multi-sensor fusion process faces the problem of differences in the type and dimensionality of sensory data acquired using different sensors (cameras, lidar, millimeter-wave radar, and so on) as well as differences in the performance of environmental perception caused by using different fusion strategies. In this article, we study multiple papers on multi-sensor fusion in the field of AD and address the problem that the category division in current multi-sensor fusion perception is not detailed and clear enough and is more subjective, which makes the classification strategies differ significantly among similar algorithms. We innovatively propose a multi-sensor fusion taxonomy, which divides the fusion perception classification strategies into two categories-symmetric fusion and asymmetric fusion-and seven subcategories of strategy combinations, such as data, features, and results. In addition, the reliability of current AD perception is limited by its insufficient environment perception capability and the robustness of data-driven methods in dealing with extreme situations (e.g., blind areas). This article also summarizes the innovative applications of multi-sensor fusion classification strategies in AD cooperative perception.
资助项目	National Key Research and Development Program of China[2019YFB1406302]
WOS关键词	TECHNOLOGIES ; VEHICLES
WOS研究方向	Engineering ; Transportation
语种	英语
出版者	IEEE-INST ELECTRICAL ELECTRONICS ENGINEERS INC
WOS记录号	WOS:001047576700001
资助机构	National Key Research and Development Program of China
内容类型	期刊论文
源URL	[http://ir.ia.ac.cn/handle/173211/54008]
专题	多模态人工智能系统全国重点实验室
通讯作者	Niu, Zhendong
作者单位	1.China Telecom Beijing Res Inst, Beijing, Peoples R China 2.Shanghai AI Lab, Shanghai 200232, Peoples R China 3.Chinese Acad Sci, Inst Automat, State Key Lab Multimodal Artificial Intelligence S, Beijing 100190, Peoples R China 4.Beijing Inst Technol, Sch Comp Sci & Technol, Beijing 100081, Peoples R China 5.Beijing Inst Technol, Beijing, Peoples R China 6.China Telecom Beijing Res Inst, Inst Big Data & Artificial Intelligence, Beijing 102209, Peoples R China
推荐引用方式 GB/T 7714	Xiang, Chao,Feng, Chen,Xie, Xiaopo,et al. Multi-sensor Fusion and Cooperative Perception for Autonomous Driving A Review[J]. IEEE INTELLIGENT TRANSPORTATION SYSTEMS MAGAZINE,2023:23.
APA	Xiang, Chao.,Feng, Chen.,Xie, Xiaopo.,Shi, Botian.,Lu, Hao.,...&Niu, Zhendong.(2023).Multi-sensor Fusion and Cooperative Perception for Autonomous Driving A Review.IEEE INTELLIGENT TRANSPORTATION SYSTEMS MAGAZINE,23.
MLA	Xiang, Chao,et al."Multi-sensor Fusion and Cooperative Perception for Autonomous Driving A Review".IEEE INTELLIGENT TRANSPORTATION SYSTEMS MAGAZINE (2023):23.