Accident Detection and Notification System.
Journal Title: International Journal of Engineering Sciences & Research Technology - Year 30, Vol 3, Issue 7
Abstract
During the last few years the numbers of vehicles travelling in our roads have increased, increasing the driver’s attention requirements. The instant effect of this is the dramatic increase in the amount of accidents occurring on the road. reduce the number of road risks, vehicular networks will play an increasing role in the Intelligent Transportation Systems (ITS) area. Most ITS applications, such as road safety, feet management, and navigation, will rely on data exchanged between the vehicle and the roadside infrastructure (V2I), or even directly between vehicles (V2V). The integration of sensoring capabilities in vehicles along with peer to peer mobile communication among vehicles, significant in terms of safety. In order to maximize the benefits of using communication systems between vehicles, the infrastructure should be supported by a intelligent system capable of estimating the severity of accidents, and automatically notifying the actions required, thereby reducing the time needed to rescue injured passengers. Many of the manual decisions taken nowadays by emergency services are based on incomplete or inaccurate data, which may be replaced by automatic systems that adapt to the specific characteristics of each accident. A preliminary assessment of the severity of the accident will help emergency services to adapt the human and material resources to the conditions of the accident, with the consequent assistance quality improvement. In this paper, we take advantage of the use of vehicular networks to collect detailed information about road accidents that is then used to estimate the severity of the collision. We propose estimation based on data mining classification algorithms, trained using historical data about previous accidents. Our proposal does not focus on directly reducing the number of accidents, but on improving post- collision assistance.
Authors and Affiliations
Syed Khaleelfarhan*
Keywords
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- EP ID EP137857
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