|SenseMag: Enabling Low-Cost Traffic Monitoring using Non-invasive Magnetic Sensing|
|Wang,Kafeng1; Xiong,Haoyi2; Zhang,Jie3; Chen,Hongyang4; Dou,Dejing5; Xu,Cheng Zhong6|
|Source Publication||IEEE Internet of Things Journal|
The operation and management of intelligent transportation systems (ITS), such as traffic monitoring, relies on real-time data aggregation of vehicular traffic information, including vehicular types (e.g., cars, trucks, and buses), in the critical roads and highways. While traditional approaches based on vehicular-embedded GPS sensors or camera networks would either invade drivers’ privacy or require high deployment cost, this paper introduces a low-cost method, namely , to recognize the vehicular type using a pair of non-invasive magnetic sensors deployed on the straight road section. filters out noises and segments received magnetic signals by the exact time points that the vehicle arrives or departs from every sensor node. Further, adopts a hierarchical recognition model to first estimate the speed/velocity, then identify the length of vehicle using the predicted speed, sampling cycles, and the distance between the sensor nodes. With the vehicle length identified and the temporal/spectral features extracted from the magnetic signals, classify the types of vehicles accordingly. Some semi-automated learning techniques have been adopted for the design of filters, features, and the choice of hyper-parameters. Extensive experiment based on real-word field deployment (on the highways in Shenzhen, China) shows that significantly outperforms the existing methods in both classification accuracy and the granularity of vehicle types (i.e., 7 types by versus 4 types by the existing work in comparisons). To be specific, our field experiment results validate that is with at least 90% vehicle type classification accuracy and less than 5% vehicle length classification error.
|Keyword||Internet Of Vehicles (Iov). Magnetic Domains Magnetic Sensing Magnetic Sensors Monitoring Perpendicular Magnetic Anisotropy Roads Sensors Time-frequency Analysis Traffic Monitoring Vehicle Type Classification|
|URL||View the original|
|WOS Research Area||Computer Science ; Telecommunications|
|WOS Subject||Computer Science, Information Systems ; Engineering, Electrical & Electronic ; Telecommunications|
|Document Type||Journal article|
|Collection||Faculty of Science and Technology|
|Affiliation||1.Shenzhen Institute of Advanced Technology, Chinese Academy of Sciences, and University of Chinese Academy of Sciences, Shenzhen, Guangdong, China|
2.Big Data Lab, Baidu Inc., Haidian, Beijing, China. (e-mail: firstname.lastname@example.org)
3.Key Laboratory of High Confidence Software Technologies, Peking University, Haidian, Beijing, China.
4.Research Center for Intelligent Network, Zhejiang Lab, Hangzhou, Zhejiang, China.
5.Big Data Lab, Baidu Inc., Haidian, Beijing, China.
6.State Key Laboratory of Internet of Things for Smart City, and Department of Computer and Information Science, University of Macau, Tapia, Macau, China.
|Wang,Kafeng,Xiong,Haoyi,Zhang,Jie,et al. SenseMag: Enabling Low-Cost Traffic Monitoring using Non-invasive Magnetic Sensing[J]. IEEE Internet of Things Journal,2021,8(22):16666-16679.|
|APA||Wang,Kafeng,Xiong,Haoyi,Zhang,Jie,Chen,Hongyang,Dou,Dejing,&Xu,Cheng Zhong.(2021).SenseMag: Enabling Low-Cost Traffic Monitoring using Non-invasive Magnetic Sensing.IEEE Internet of Things Journal,8(22),16666-16679.|
|MLA||Wang,Kafeng,et al."SenseMag: Enabling Low-Cost Traffic Monitoring using Non-invasive Magnetic Sensing".IEEE Internet of Things Journal 8.22(2021):16666-16679.|
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