An Intelligent Congestion Avoidance Mechanism Based on Generalized Regression Neural Network for Heterogeneous Vehicular Networks
文献类型：期刊论文
| 作者 | Zhu, Yuxuan1,5; Li, Zhiheng2,3; Wang, Feiyue4 ; Li, Li2
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| 刊名 | IEEE TRANSACTIONS ON INTELLIGENT VEHICLES
 |
| 出版日期 | 2023-04-01 |
| 卷号 | 8期号:4页码:2712-2722 |
| 关键词 | Aerospace electronics Vehicle dynamics Trajectory Space vehicles Data models Intelligent vehicles Computational modeling Extreme operating conditions parallel learning vehicle testing |
| ISSN号 | 2379-8858 |
| DOI | 10.1109/TIV.2023.3235732 |
| 通讯作者 | Li, Li(li-li@tsinghua.edu.cn) |
| 英文摘要 | 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. |
| WOS关键词 | SIMULATION |
| 资助项目 | Key-Area Research and Development Program of Guangdong Province[2020B0909050003] ; National Natural Science Foundation of China[61790565] |
| WOS研究方向 | Computer Science ; Engineering ; Transportation |
| 语种 | 英语 |
| WOS记录号 | WOS:000994739000010 |
| 出版者 | IEEE-INST ELECTRICAL ELECTRONICS ENGINEERS INC |
| 资助机构 | Key-Area Research and Development Program of Guangdong Province ; National Natural Science Foundation of China |
| 源URL | [http://ir.ia.ac.cn/handle/173211/53393]  |
| 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.Pearl River Delta, Res Inst Tsinghua, Guangzhou 510530, Peoples R China 2.Tsinghua Univ, Dept Automat, BNRist, Beijing 100084, Peoples R China 3.Tsinghua Univ, Grad Sch Shenzhen, Shenzhen 518055, Peoples R China 4.Chinese Acad Sci, Inst Automat, State Key Lab Management & Control Complex Syst, Beijing 100080, Peoples R China 5.Tsinghua Univ, Dept Automat, Beijing 100084, 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. |
入库方式： OAI收割
来源：自动化研究所
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