Nicholas Capurso, Eric Elsken, Donnell Payne, Liran Ma
{"title":"Poster: A robust vehicular accident detection system using inexpensive portable devices","authors":"Nicholas Capurso, Eric Elsken, Donnell Payne, Liran Ma","doi":"10.1145/2594368.2601456","DOIUrl":null,"url":null,"abstract":"In the event of a vehicular accident, there are many scenarios in which the occupants become incapacitated and unable to call for assistance. Currently, there exist systems such as OnStar [1] that provides accident detection and roadside assistance services. However, the cost of these proprietary systems and their availability for all vehicular models limit their use. We propose an inexpensive and robust system that provides accurate accident detection and emergency responder notification as our senior capstone project at Texas Christian University. The proposed system contains three primary components: a smartphone, a single-on-board computer (the Raspberry Pi [2]), and Texas Instruments SensorTags [3] as shown in Figure 1.","PeriodicalId":131209,"journal":{"name":"Proceedings of the 12th annual international conference on Mobile systems, applications, and services","volume":"42 1","pages":"0"},"PeriodicalIF":0.0000,"publicationDate":"2014-06-02","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":"8","resultStr":null,"platform":"Semanticscholar","paperid":null,"PeriodicalName":"Proceedings of the 12th annual international conference on Mobile systems, applications, and services","FirstCategoryId":"1085","ListUrlMain":"https://doi.org/10.1145/2594368.2601456","RegionNum":0,"RegionCategory":null,"ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":null,"EPubDate":"","PubModel":"","JCR":"","JCRName":"","Score":null,"Total":0}
引用次数: 8
Abstract
In the event of a vehicular accident, there are many scenarios in which the occupants become incapacitated and unable to call for assistance. Currently, there exist systems such as OnStar [1] that provides accident detection and roadside assistance services. However, the cost of these proprietary systems and their availability for all vehicular models limit their use. We propose an inexpensive and robust system that provides accurate accident detection and emergency responder notification as our senior capstone project at Texas Christian University. The proposed system contains three primary components: a smartphone, a single-on-board computer (the Raspberry Pi [2]), and Texas Instruments SensorTags [3] as shown in Figure 1.
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