Please use this identifier to cite or link to this item:
http://hdl.handle.net/10773/21569
Title: | Automatic Accident Detection with Multi-modal Alert System Implementation for ITS |
Author: | Silva, Bruno Fernandes Alam, Muhammad Gomes, Vitor Ferreira, Joaquim Castro Oliveira, Arnaldo |
Keywords: | Intelligent transportation system, IEEE 802.11p, eCall |
Issue Date: | 2016 |
Publisher: | Elsevier |
Abstract: | The rapid technological growth is now providing global opportunities to enable intelligent transportation system (ITS) to tackle road accidents which is considered one of the world's largest public injury prevention problem. For this purpose, eCall is an initiative by European Union (EU) with the purpose to bring rapid assistance to an accident location. This paper1 presents HDy Copilot, an application for automatic accident detection integrated with multimodal alert dissemination, via both eCall and IEEE 802.11p (ITS-G5). The proposed accident detection algorithm receives inputs from the vehicle, via ODB-II, and from the smartphone sensors, namely the accelerometer, the magnetometer and the gyroscope. An Android smartphone is used as human machine interface, so that the driver can configure the application, receive road hazard warnings issued by other vehicles in the vicinity and cancel countdown procedures upon false road vehicle crash detection. The HDy Copilot is developed for Android OS as it provides open source APIs that allow access to its hardware resources. The application is implemented, tested and connected to an IEEE 802.11p based prototype. The generated results show that the application successfully detects collisions, rollovers, performs the eCall along with sending Minimum Set of Data (MSD) and Decentralized Environmental Notification Message (DENM). |
Peer review: | yes |
URI: | http://hdl.handle.net/10773/21569 |
DOI: | 10.1016/j.vehcom.2015.11.001 |
ISSN: | 2214-2096 |
Appears in Collections: | ESTGA - Artigos |
Files in This Item:
File | Description | Size | Format | |
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1-s2.0-S2214209615000625-main.pdf | main paper | 2.7 MB | Adobe PDF |
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