Deep Learning-Based Traffic Safety Solution for a Mixture of Autonomous and Manual Vehicles in a 5G-Enabled Intelligent Transportation System

doi:10.1109/TITS.2020.3042504

	Deep Learning-Based Traffic Safety Solution for a Mixture of Autonomous and Manual Vehicles in a 5G-Enabled Intelligent Transportation System
	Yu, Keping 1,2; Lin, Long 1; Alazab, Mamoun 3; Tan, Liang 1,4; Gu, Bo 5
	2021-07-01
发表期刊	IEEE TRANSACTIONS ON INTELLIGENT TRANSPORTATION SYSTEMS
ISSN	1524-9050
卷号	22 期号:7 页码:4337-4347
摘要	It is expected that a mixture of autonomous and manual vehicles will persist as a part of the intelligent transportation system (ITS) for many decades. Thus, addressing the safety issues arising from this mix of autonomous and manual vehicles before autonomous vehicles are entirely popularized is crucial. As the ITS system has increased in complexity, autonomous vehicles exhibit problems such as a low intention recognition rate and poor real-time performance when predicting the driving direction; these problems seriously affect the safety and comfort of mixed traffic systems. Therefore, the ability of autonomous vehicles to predict the driving direction in real time according to the surrounding traffic environment must be improved and researchers must work to create a more mature ITS. In this paper, we propose a deep learning-based traffic safety solution for a mixture of autonomous and manual vehicles in a 5G-enabled ITS. In this scheme, a driving trajectory dataset and a natural-driving dataset are employed as the network inputs to long-term memory networks in the 5G-enabled ITS: the probability matrix of each intention is calculated by the softmax function. Then, the final intention probability is obtained by fusing the mean rule in the decision layer. Experimental results show that the proposed scheme achieves intention recognition rates of 91.58% and 90.88% for left and right lane changes, respectively, effectively improving both accuracy and real-time intention recognition and improving the lane change problem in a mixed traffic environment.
关键词	Mixed traffic big data 5G deep learning LSTM SoftMax intention recognition
DOI	10.1109/TITS.2020.3042504
收录类别	SCI
语种	英语
资助项目	National Natural Science Foundation of China[61373162] ; Sichuan Provincial Science and Technology Department Project[2019YFG0183] ; Sichuan Provincial Key Laboratory Project[KJ201402] ; Japan Society for the Promotion of Science (JSPS)[JP18K18044] ; National Key Research and Development Program of China[2019YFB1704700]
WOS研究方向	Engineering ; Transportation
WOS类目	Engineering, Civil ; Engineering, Electrical & Electronic ; Transportation Science & Technology
WOS记录号	WOS:000673518500039
出版者	IEEE-INST ELECTRICAL ELECTRONICS ENGINEERS INC
引用统计	被引频次：149[WOS] [WOS记录] [WOS相关记录]
文献类型	期刊论文
条目标识符	http://119.78.100.204/handle/2XEOYT63/17478
专题	中国科学院计算技术研究所期刊论文_英文
通讯作者	Tan, Liang
作者单位	1.Sichuan Normal Univ, Coll Comp Sci, Chengdu 610101, Peoples R China 2.Waseda Univ, Global Informat & Telecommun Inst, Tokyo 1698050, Japan 3.Charles Darwin Univ, Coll Engn IT & Environm, Darwin, NT 0810, Australia 4.Chinese Acad Sci, Inst Comp Technol, Beijing 100190, Peoples R China 5.Sun Yat Sen Univ, Sch Intelligent Syst Engn, Guangzhou 510275, Peoples R China
推荐引用方式 GB/T 7714	Yu, Keping,Lin, Long,Alazab, Mamoun,et al. Deep Learning-Based Traffic Safety Solution for a Mixture of Autonomous and Manual Vehicles in a 5G-Enabled Intelligent Transportation System[J]. IEEE TRANSACTIONS ON INTELLIGENT TRANSPORTATION SYSTEMS,2021,22(7):4337-4347.
APA	Yu, Keping,Lin, Long,Alazab, Mamoun,Tan, Liang,&Gu, Bo.(2021).Deep Learning-Based Traffic Safety Solution for a Mixture of Autonomous and Manual Vehicles in a 5G-Enabled Intelligent Transportation System.IEEE TRANSACTIONS ON INTELLIGENT TRANSPORTATION SYSTEMS,22(7),4337-4347.
MLA	Yu, Keping,et al."Deep Learning-Based Traffic Safety Solution for a Mixture of Autonomous and Manual Vehicles in a 5G-Enabled Intelligent Transportation System".IEEE TRANSACTIONS ON INTELLIGENT TRANSPORTATION SYSTEMS 22.7(2021):4337-4347.