{"title":"基于非接触式微波传感的路面状况运行时分析","authors":"J. Blanche, D. Mitchell, D. Flynn","doi":"10.1109/GCAIoT51063.2020.9345917","DOIUrl":null,"url":null,"abstract":"Safety management of winter roads is dependent on targeted distributions of salt. Insufficient salt dispersal results in dangerous driving conditions, while excessive deposition results in adverse environmental effects and wastes valuable resources. In this paper we present the results of Frequency Modulated Continuous Wave radar (FMCW) analysis for real-time salt detection on road surfaces. Experiments are conducted within laboratory conditions and field trials, with the FMCW sensor installed onto a commercial road gritter. Performed to industry-standard salt dispersal concentrations, we test FMCW sensitivity to ice-thaw on concrete, marine rock-salt on ice and brown-salt brine concentrations. Results demonstrate that FMCW in the K-band is sensitive to brine and rock-salt in both laboratory and field conditions. Consistent results for incremental salt residues in the field of view of the sensor are observed, where the return signal is consistently within a 0.5-3 x106 absolute unit (a.u.) range in the laboratory and a 10–50 (a.u.) range in the field. We propose that FMCW is uniquely suited to detecting black ice, concentrations of brine solutions and residual salt, invisible to visual inspection. FMCW sensing holds significant prospect for providing previously inaccessible data relating to runtime dynamic road surface conditions and environmental monitoring.","PeriodicalId":398815,"journal":{"name":"2020 IEEE Global Conference on Artificial Intelligence and Internet of Things (GCAIoT)","volume":"23 1","pages":"0"},"PeriodicalIF":0.0000,"publicationDate":"2020-12-12","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":"5","resultStr":"{\"title\":\"Run-Time Analysis of Road Surface Conditions Using Non-Contact Microwave Sensing\",\"authors\":\"J. Blanche, D. Mitchell, D. Flynn\",\"doi\":\"10.1109/GCAIoT51063.2020.9345917\",\"DOIUrl\":null,\"url\":null,\"abstract\":\"Safety management of winter roads is dependent on targeted distributions of salt. Insufficient salt dispersal results in dangerous driving conditions, while excessive deposition results in adverse environmental effects and wastes valuable resources. In this paper we present the results of Frequency Modulated Continuous Wave radar (FMCW) analysis for real-time salt detection on road surfaces. Experiments are conducted within laboratory conditions and field trials, with the FMCW sensor installed onto a commercial road gritter. Performed to industry-standard salt dispersal concentrations, we test FMCW sensitivity to ice-thaw on concrete, marine rock-salt on ice and brown-salt brine concentrations. Results demonstrate that FMCW in the K-band is sensitive to brine and rock-salt in both laboratory and field conditions. Consistent results for incremental salt residues in the field of view of the sensor are observed, where the return signal is consistently within a 0.5-3 x106 absolute unit (a.u.) range in the laboratory and a 10–50 (a.u.) range in the field. We propose that FMCW is uniquely suited to detecting black ice, concentrations of brine solutions and residual salt, invisible to visual inspection. FMCW sensing holds significant prospect for providing previously inaccessible data relating to runtime dynamic road surface conditions and environmental monitoring.\",\"PeriodicalId\":398815,\"journal\":{\"name\":\"2020 IEEE Global Conference on Artificial Intelligence and Internet of Things (GCAIoT)\",\"volume\":\"23 1\",\"pages\":\"0\"},\"PeriodicalIF\":0.0000,\"publicationDate\":\"2020-12-12\",\"publicationTypes\":\"Journal Article\",\"fieldsOfStudy\":null,\"isOpenAccess\":false,\"openAccessPdf\":\"\",\"citationCount\":\"5\",\"resultStr\":null,\"platform\":\"Semanticscholar\",\"paperid\":null,\"PeriodicalName\":\"2020 IEEE Global Conference on Artificial Intelligence and Internet of Things (GCAIoT)\",\"FirstCategoryId\":\"1085\",\"ListUrlMain\":\"https://doi.org/10.1109/GCAIoT51063.2020.9345917\",\"RegionNum\":0,\"RegionCategory\":null,\"ArticlePicture\":[],\"TitleCN\":null,\"AbstractTextCN\":null,\"PMCID\":null,\"EPubDate\":\"\",\"PubModel\":\"\",\"JCR\":\"\",\"JCRName\":\"\",\"Score\":null,\"Total\":0}","platform":"Semanticscholar","paperid":null,"PeriodicalName":"2020 IEEE Global Conference on Artificial Intelligence and Internet of Things (GCAIoT)","FirstCategoryId":"1085","ListUrlMain":"https://doi.org/10.1109/GCAIoT51063.2020.9345917","RegionNum":0,"RegionCategory":null,"ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":null,"EPubDate":"","PubModel":"","JCR":"","JCRName":"","Score":null,"Total":0}
Run-Time Analysis of Road Surface Conditions Using Non-Contact Microwave Sensing
Safety management of winter roads is dependent on targeted distributions of salt. Insufficient salt dispersal results in dangerous driving conditions, while excessive deposition results in adverse environmental effects and wastes valuable resources. In this paper we present the results of Frequency Modulated Continuous Wave radar (FMCW) analysis for real-time salt detection on road surfaces. Experiments are conducted within laboratory conditions and field trials, with the FMCW sensor installed onto a commercial road gritter. Performed to industry-standard salt dispersal concentrations, we test FMCW sensitivity to ice-thaw on concrete, marine rock-salt on ice and brown-salt brine concentrations. Results demonstrate that FMCW in the K-band is sensitive to brine and rock-salt in both laboratory and field conditions. Consistent results for incremental salt residues in the field of view of the sensor are observed, where the return signal is consistently within a 0.5-3 x106 absolute unit (a.u.) range in the laboratory and a 10–50 (a.u.) range in the field. We propose that FMCW is uniquely suited to detecting black ice, concentrations of brine solutions and residual salt, invisible to visual inspection. FMCW sensing holds significant prospect for providing previously inaccessible data relating to runtime dynamic road surface conditions and environmental monitoring.