{"title":"车对车通信交通安全应用的风险评估","authors":"C. B. Math, Hong Li, S. Groot","doi":"10.1109/VTCFall.2016.7881202","DOIUrl":null,"url":null,"abstract":"Vehicle-to-others (V2X) communication systems intend to increase safety and efficiency of our transportation networks. However, wireless communication imperfections such as missed messages due to collisions and fading in the wireless channel, may affect safety application reliability and lead to risky situations. Thus metrics are required to evaluate the impact of communication inadequacies on the safety applications. In this paper we perform analyses of various existing safety application reliability metrics and conclude that they do not reflect safety application risk and vulnerability of individual nodes effectively. We propose a new metric called Effective Risk Factor (ERF), which quantifies the risk at a node for each link, to identify dangers due to poor awareness of their neighbors. The ERF evaluation considers links of its neighbors, thus detecting risky situations over existing neighbor links on runtime making the ERF assessment realistic. The ERF metric is evaluated and compared with other reliability metrics for a stationary vehicle warning application in a simulated highway scenario. The results show that the ERF evaluation performed at each node on runtime is able to capture a fine time scale fluctuations in the risk experienced by an application precisely. The ERF also enables prediction of higher risk situations. The results also demonstrate that the ERF captures application risk experienced by nodes effectively compared to other reliability metrics.","PeriodicalId":6484,"journal":{"name":"2016 IEEE 84th Vehicular Technology Conference (VTC-Fall)","volume":"64 1","pages":"1-6"},"PeriodicalIF":0.0000,"publicationDate":"2016-09-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":"8","resultStr":"{\"title\":\"Risk Assessment for Traffic Safety Applications with V2V Communications\",\"authors\":\"C. B. Math, Hong Li, S. Groot\",\"doi\":\"10.1109/VTCFall.2016.7881202\",\"DOIUrl\":null,\"url\":null,\"abstract\":\"Vehicle-to-others (V2X) communication systems intend to increase safety and efficiency of our transportation networks. However, wireless communication imperfections such as missed messages due to collisions and fading in the wireless channel, may affect safety application reliability and lead to risky situations. Thus metrics are required to evaluate the impact of communication inadequacies on the safety applications. In this paper we perform analyses of various existing safety application reliability metrics and conclude that they do not reflect safety application risk and vulnerability of individual nodes effectively. We propose a new metric called Effective Risk Factor (ERF), which quantifies the risk at a node for each link, to identify dangers due to poor awareness of their neighbors. The ERF evaluation considers links of its neighbors, thus detecting risky situations over existing neighbor links on runtime making the ERF assessment realistic. The ERF metric is evaluated and compared with other reliability metrics for a stationary vehicle warning application in a simulated highway scenario. The results show that the ERF evaluation performed at each node on runtime is able to capture a fine time scale fluctuations in the risk experienced by an application precisely. The ERF also enables prediction of higher risk situations. The results also demonstrate that the ERF captures application risk experienced by nodes effectively compared to other reliability metrics.\",\"PeriodicalId\":6484,\"journal\":{\"name\":\"2016 IEEE 84th Vehicular Technology Conference (VTC-Fall)\",\"volume\":\"64 1\",\"pages\":\"1-6\"},\"PeriodicalIF\":0.0000,\"publicationDate\":\"2016-09-01\",\"publicationTypes\":\"Journal Article\",\"fieldsOfStudy\":null,\"isOpenAccess\":false,\"openAccessPdf\":\"\",\"citationCount\":\"8\",\"resultStr\":null,\"platform\":\"Semanticscholar\",\"paperid\":null,\"PeriodicalName\":\"2016 IEEE 84th Vehicular Technology Conference (VTC-Fall)\",\"FirstCategoryId\":\"1085\",\"ListUrlMain\":\"https://doi.org/10.1109/VTCFall.2016.7881202\",\"RegionNum\":0,\"RegionCategory\":null,\"ArticlePicture\":[],\"TitleCN\":null,\"AbstractTextCN\":null,\"PMCID\":null,\"EPubDate\":\"\",\"PubModel\":\"\",\"JCR\":\"\",\"JCRName\":\"\",\"Score\":null,\"Total\":0}","platform":"Semanticscholar","paperid":null,"PeriodicalName":"2016 IEEE 84th Vehicular Technology Conference (VTC-Fall)","FirstCategoryId":"1085","ListUrlMain":"https://doi.org/10.1109/VTCFall.2016.7881202","RegionNum":0,"RegionCategory":null,"ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":null,"EPubDate":"","PubModel":"","JCR":"","JCRName":"","Score":null,"Total":0}
Risk Assessment for Traffic Safety Applications with V2V Communications
Vehicle-to-others (V2X) communication systems intend to increase safety and efficiency of our transportation networks. However, wireless communication imperfections such as missed messages due to collisions and fading in the wireless channel, may affect safety application reliability and lead to risky situations. Thus metrics are required to evaluate the impact of communication inadequacies on the safety applications. In this paper we perform analyses of various existing safety application reliability metrics and conclude that they do not reflect safety application risk and vulnerability of individual nodes effectively. We propose a new metric called Effective Risk Factor (ERF), which quantifies the risk at a node for each link, to identify dangers due to poor awareness of their neighbors. The ERF evaluation considers links of its neighbors, thus detecting risky situations over existing neighbor links on runtime making the ERF assessment realistic. The ERF metric is evaluated and compared with other reliability metrics for a stationary vehicle warning application in a simulated highway scenario. The results show that the ERF evaluation performed at each node on runtime is able to capture a fine time scale fluctuations in the risk experienced by an application precisely. The ERF also enables prediction of higher risk situations. The results also demonstrate that the ERF captures application risk experienced by nodes effectively compared to other reliability metrics.