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
刊名	IEEE TRANSACTIONS ON INTELLIGENT TRANSPORTATION SYSTEMS
	2021-07-01
卷号	22 期号:7 页码:4337-4347
关键词	Mixed traffic big data 5G deep learning LSTM SoftMax intention recognition
ISSN号	1524-9050
DOI	10.1109/TITS.2020.3042504
英文摘要	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.
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
语种	英语
出版者	IEEE-INST ELECTRICAL ELECTRONICS ENGINEERS INC
WOS记录号	WOS:000673518500039
内容类型	期刊论文
源URL	[http://119.78.100.204/handle/2XEOYT63/17477]
专题	中国科学院计算技术研究所
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.