New communication technologies integrated into modern vehicles provide a chance for higher help to individuals hurt in traffic accidents. Recent studies show however communication capabilities ought to be supported by AI systems capable of automating several of the choices to be taken by emergency services, thereby adapting the rescue resources to the severity of the accident and reducing help time. To enhance the general rescue method, a quick and correct estimation of the severity of the accident represent a key purpose to assist emergency services higher estimate the specified resources. This paper proposes a unique intelligent system that is in a position to automatically sight road accidents, notify them through transport networks, and estimate their severity supported the thought information of information} mining and knowledge abstract thought. Our system considers the foremost relevant variables which will characterize the severity of the accidents (variables like the vehicle speed, the sort of vehicles concerned, the impact speed, and also the standing of the airbag). Results show that a whole information Discovery in Databases (KDD) method, with associate degree adequate choice of relevant options, permits generating estimation models which will predict the severity of latest accidents. we tend to develop a paradigm of our system supported off the peg devices and validate it at the Applus+ IDIADA Automotive analysis Corporation facilities, showing that our system will notably cut back the time required to alert associate degreed deploy emergency services once an accident takes place.

Keywords : KDD, Data Mining, Vehicular Networks, Traffic Accident Assistance, Applus+ IDIADA.