{"title":"基于物联网的事故预防V2I框架","authors":"Hitesh Mohapatra, A. K. Dalai","doi":"10.1109/AISP53593.2022.9760623","DOIUrl":null,"url":null,"abstract":"The volcanic growth of the population directly influences the density of vehicles on the road. The rapid growth of vehicles is the primary cause of traffic congestion, pollution, and life-loss through accidents. This paper has presented an IoT-based vehicle to infrastructure (V2I) model for better predictability about road behaviors. This V2I model helps to avoid accidents or collisions at cross-sections of the road. The implementation part of the proposed model has considered the speed of the vehicle and creates an advanced alert mechanism based on the speed. The implementation and validation of the proposed model have been done through the RMATLAB17 simulator. The simulation results are satisfactory and achieve 82.14% of accuracy in generating an alert signal for proper decision-making by the drivers.","PeriodicalId":6793,"journal":{"name":"2022 2nd International Conference on Artificial Intelligence and Signal Processing (AISP)","volume":"64 1","pages":"1-4"},"PeriodicalIF":0.0000,"publicationDate":"2022-02-12","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":"1","resultStr":"{\"title\":\"IoT Based V2I Framework For Accident Prevention\",\"authors\":\"Hitesh Mohapatra, A. K. Dalai\",\"doi\":\"10.1109/AISP53593.2022.9760623\",\"DOIUrl\":null,\"url\":null,\"abstract\":\"The volcanic growth of the population directly influences the density of vehicles on the road. The rapid growth of vehicles is the primary cause of traffic congestion, pollution, and life-loss through accidents. This paper has presented an IoT-based vehicle to infrastructure (V2I) model for better predictability about road behaviors. This V2I model helps to avoid accidents or collisions at cross-sections of the road. The implementation part of the proposed model has considered the speed of the vehicle and creates an advanced alert mechanism based on the speed. The implementation and validation of the proposed model have been done through the RMATLAB17 simulator. The simulation results are satisfactory and achieve 82.14% of accuracy in generating an alert signal for proper decision-making by the drivers.\",\"PeriodicalId\":6793,\"journal\":{\"name\":\"2022 2nd International Conference on Artificial Intelligence and Signal Processing (AISP)\",\"volume\":\"64 1\",\"pages\":\"1-4\"},\"PeriodicalIF\":0.0000,\"publicationDate\":\"2022-02-12\",\"publicationTypes\":\"Journal Article\",\"fieldsOfStudy\":null,\"isOpenAccess\":false,\"openAccessPdf\":\"\",\"citationCount\":\"1\",\"resultStr\":null,\"platform\":\"Semanticscholar\",\"paperid\":null,\"PeriodicalName\":\"2022 2nd International Conference on Artificial Intelligence and Signal Processing (AISP)\",\"FirstCategoryId\":\"1085\",\"ListUrlMain\":\"https://doi.org/10.1109/AISP53593.2022.9760623\",\"RegionNum\":0,\"RegionCategory\":null,\"ArticlePicture\":[],\"TitleCN\":null,\"AbstractTextCN\":null,\"PMCID\":null,\"EPubDate\":\"\",\"PubModel\":\"\",\"JCR\":\"\",\"JCRName\":\"\",\"Score\":null,\"Total\":0}","platform":"Semanticscholar","paperid":null,"PeriodicalName":"2022 2nd International Conference on Artificial Intelligence and Signal Processing (AISP)","FirstCategoryId":"1085","ListUrlMain":"https://doi.org/10.1109/AISP53593.2022.9760623","RegionNum":0,"RegionCategory":null,"ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":null,"EPubDate":"","PubModel":"","JCR":"","JCRName":"","Score":null,"Total":0}
The volcanic growth of the population directly influences the density of vehicles on the road. The rapid growth of vehicles is the primary cause of traffic congestion, pollution, and life-loss through accidents. This paper has presented an IoT-based vehicle to infrastructure (V2I) model for better predictability about road behaviors. This V2I model helps to avoid accidents or collisions at cross-sections of the road. The implementation part of the proposed model has considered the speed of the vehicle and creates an advanced alert mechanism based on the speed. The implementation and validation of the proposed model have been done through the RMATLAB17 simulator. The simulation results are satisfactory and achieve 82.14% of accuracy in generating an alert signal for proper decision-making by the drivers.