An Intelligent Congestion Avoidance Mechanism Based on Generalized Regression Neural Network for Heterogeneous Vehicular Networks

doi:10.1109/TIV.2023.3235732

	An Intelligent Congestion Avoidance Mechanism Based on Generalized Regression Neural Network for Heterogeneous Vehicular Networks
	Zhu, Yuxuan 1,2; Li, Zhiheng 3,4; Wang, Feiyue5 ; Li, Li 3
发表期刊	IEEE TRANSACTIONS ON INTELLIGENT VEHICLES
ISSN	2379-8858
	2023-04-01
卷号	8 期号:4 页码:2712-2722
通讯作者	Li, Li(li-li@tsinghua.edu.cn)
摘要	Extreme operating conditions refer to the critical dynamic state during vehicle operation. The lack of experimental data under critical conditions is one of the fundamental problems in the study. To solve the problem, we design an LSTM-VAE based generating model to generate rational control sequences that can push vehicles toward extreme operating conditions and used simulation tests to analyze them. Specifically, we train the Encoder to study the basic driving logic of the control sequences collected during free-drive tests by human drivers, forming a low-dimension latent feature space. Then, we sample from specified regions in the latent feature space and use the Decoder to generate new control sequences. Finally, we use the sequences as the control input of the 27-DoF high-precision vehicle dynamic simulation platform and analyze the variations of simulated vehicle dynamics. We conduct different experiments and validate the method from different aspects. Results reveal that by sampling from specific regions of the latent feature space, we get a higher chance to generate desired control sequences for extreme operating conditions.
关键词	Aerospace electronics Vehicle dynamics Trajectory Space vehicles Data models Intelligent vehicles Computational modeling Extreme operating conditions parallel learning vehicle testing
DOI	10.1109/TIV.2023.3235732
关键词[WOS]	SIMULATION
收录类别	SCI
语种	英语
资助项目	Key-Area Research and Development Program of Guangdong Province[2020B0909050003] ; National Natural Science Foundation of China[61790565]
项目资助者	Key-Area Research and Development Program of Guangdong Province ; National Natural Science Foundation of China
WOS研究方向	Computer Science ; Engineering ; Transportation
WOS类目	Computer Science, Artificial Intelligence ; Engineering, Electrical & Electronic ; Transportation Science & Technology
WOS记录号	WOS:000994739000010
出版者	IEEE-INST ELECTRICAL ELECTRONICS ENGINEERS INC
引用统计	被引频次：5[WOS] [WOS记录] [WOS相关记录]
文献类型	期刊论文
条目标识符	http://ir.ia.ac.cn/handle/173211/53394
专题	多模态人工智能系统全国重点实验室
通讯作者	Li, Li
作者单位	1.Tsinghua Univ, Dept Automat, Beijing 100084, Peoples R China 2.Pearl River Delta, Res Inst Tsinghua, Guangzhou 510530, Peoples R China 3.Tsinghua Univ, Dept Automat, BNRist, Beijing 100084, Peoples R China 4.Tsinghua Univ, Grad Sch Shenzhen, Shenzhen 518055, Peoples R China 5.Chinese Acad Sci, Inst Automat, State Key Lab Management & Control Complex Syst, Beijing 100080, Peoples R China
推荐引用方式 GB/T 7714	Zhu, Yuxuan,Li, Zhiheng,Wang, Feiyue,et al. An Intelligent Congestion Avoidance Mechanism Based on Generalized Regression Neural Network for Heterogeneous Vehicular Networks[J]. IEEE TRANSACTIONS ON INTELLIGENT VEHICLES,2023,8(4):2712-2722.
APA	Zhu, Yuxuan,Li, Zhiheng,Wang, Feiyue,&Li, Li.(2023).An Intelligent Congestion Avoidance Mechanism Based on Generalized Regression Neural Network for Heterogeneous Vehicular Networks.IEEE TRANSACTIONS ON INTELLIGENT VEHICLES,8(4),2712-2722.
MLA	Zhu, Yuxuan,et al."An Intelligent Congestion Avoidance Mechanism Based on Generalized Regression Neural Network for Heterogeneous Vehicular Networks".IEEE TRANSACTIONS ON INTELLIGENT VEHICLES 8.4(2023):2712-2722.