Research Progress on Vehicle Status Information Perception Based on Distributed Acoustic Sensing
With the rapid development of intelligent transportation systems, obtaining vehicle status information across large-scale road networks is essential for the coordinated management and control of traffic conditions. Distributed Acoustic Sensing (DAS) demonstrates considerable potential in vehicle sta...
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| Format: | Article |
| Language: | English |
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MDPI AG
2025-06-01
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| Series: | Photonics |
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| Online Access: | https://www.mdpi.com/2304-6732/12/6/560 |
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| author | Wenqiang Dong Xin Cheng Jingmei Zhou Wei Liu Jianjin Gao Chuan Hu Xiangmo Zhao |
| author_facet | Wenqiang Dong Xin Cheng Jingmei Zhou Wei Liu Jianjin Gao Chuan Hu Xiangmo Zhao |
| author_sort | Wenqiang Dong |
| collection | DOAJ |
| description | With the rapid development of intelligent transportation systems, obtaining vehicle status information across large-scale road networks is essential for the coordinated management and control of traffic conditions. Distributed Acoustic Sensing (DAS) demonstrates considerable potential in vehicle status perception due to its characteristics such as high spatial resolution and robustness in complex sensing environments. This study first reviews the limitations of conventional vehicle detection technologies and introduces the operating principles and technical features of DAS. Secondly, it investigates the correlations between DAS sensing characteristics, deployment process, and driving behavior characteristics. The results indicate that both the intensity of driving behavior and the degree of deployment–process coupling are positively associated with DAS signal sensing characteristics. This study further examines the principles, advantages, limitations, and application scenarios of various DAS signal processing algorithms. Traditional methods are becoming less effective in handling massive data generated by numerous distributed nodes. Although deep learning achieves high classification accuracy and low latency, its generalization capability remains limited. Finally, this study discusses DAS-based traffic status perception frameworks and outlines key research frontiers in vehicle status monitoring using DAS technology. |
| format | Article |
| id | doaj-art-3feca48b382f444797334d4bf669880f |
| institution | Kabale University |
| issn | 2304-6732 |
| language | English |
| publishDate | 2025-06-01 |
| publisher | MDPI AG |
| record_format | Article |
| series | Photonics |
| spelling | doaj-art-3feca48b382f444797334d4bf669880f2025-08-20T03:29:44ZengMDPI AGPhotonics2304-67322025-06-0112656010.3390/photonics12060560Research Progress on Vehicle Status Information Perception Based on Distributed Acoustic SensingWenqiang Dong0Xin Cheng1Jingmei Zhou2Wei Liu3Jianjin Gao4Chuan Hu5Xiangmo Zhao6School of Mechatronic Engineering, Xi’an Technological University, Xi’an 710021, ChinaSchool of Information Engineering, Chang’an University, Xi’an 710064, ChinaSchool of Electronic and Control Engineering, Chang’an University, Xi’an 710064, ChinaSchool of Information Engineering, Chang’an University, Xi’an 710064, ChinaSchool of Electronic and Control Engineering, Chang’an University, Xi’an 710064, ChinaSchool of Mechanical Engineering, Shanghai Jiao Tong University, Shanghai 200240, ChinaSchool of Mechatronic Engineering, Xi’an Technological University, Xi’an 710021, ChinaWith the rapid development of intelligent transportation systems, obtaining vehicle status information across large-scale road networks is essential for the coordinated management and control of traffic conditions. Distributed Acoustic Sensing (DAS) demonstrates considerable potential in vehicle status perception due to its characteristics such as high spatial resolution and robustness in complex sensing environments. This study first reviews the limitations of conventional vehicle detection technologies and introduces the operating principles and technical features of DAS. Secondly, it investigates the correlations between DAS sensing characteristics, deployment process, and driving behavior characteristics. The results indicate that both the intensity of driving behavior and the degree of deployment–process coupling are positively associated with DAS signal sensing characteristics. This study further examines the principles, advantages, limitations, and application scenarios of various DAS signal processing algorithms. Traditional methods are becoming less effective in handling massive data generated by numerous distributed nodes. Although deep learning achieves high classification accuracy and low latency, its generalization capability remains limited. Finally, this study discusses DAS-based traffic status perception frameworks and outlines key research frontiers in vehicle status monitoring using DAS technology.https://www.mdpi.com/2304-6732/12/6/560distributed optical fiber acoustic sensingDAS signal characteristicsvehicle perception |
| spellingShingle | Wenqiang Dong Xin Cheng Jingmei Zhou Wei Liu Jianjin Gao Chuan Hu Xiangmo Zhao Research Progress on Vehicle Status Information Perception Based on Distributed Acoustic Sensing Photonics distributed optical fiber acoustic sensing DAS signal characteristics vehicle perception |
| title | Research Progress on Vehicle Status Information Perception Based on Distributed Acoustic Sensing |
| title_full | Research Progress on Vehicle Status Information Perception Based on Distributed Acoustic Sensing |
| title_fullStr | Research Progress on Vehicle Status Information Perception Based on Distributed Acoustic Sensing |
| title_full_unstemmed | Research Progress on Vehicle Status Information Perception Based on Distributed Acoustic Sensing |
| title_short | Research Progress on Vehicle Status Information Perception Based on Distributed Acoustic Sensing |
| title_sort | research progress on vehicle status information perception based on distributed acoustic sensing |
| topic | distributed optical fiber acoustic sensing DAS signal characteristics vehicle perception |
| url | https://www.mdpi.com/2304-6732/12/6/560 |
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