A System for Automatic Notification and Severity Estimation of Automotive Accidents

ABSTRACT:

         During the last decades, the total number of vehicles in our roads has experienced a remarkable growth, making traffic density higher and increasing the drivers’ attention requirements. The immediate effect of this situation is the dramatic increase of traffic accidents on the road, representing a serious problem in most countries. New communication technologies integrated into modern vehicles offer an opportunity for better assistance to people injured in traffic accidents. Recent studies show how communication capabilities should be supported by artificial intelligence systems capable of automating many of the decisions to be taken by emergency services, thereby adapting the rescue resources to the severity of the accident and reducing assistance time. To improve the overall rescue process, a fast and accurate estimation of the severity of the accident represent a key point to help the emergency services to better estimate the required resources. This paper proposes a novel intelligent system which is able to automatically detect road accidents, notify them through vehicular networks, and estimate their severity based on the concept of data mining and knowledge inference. Our system considers the most relevant variables that can characterize the severity of the accidents (variables such as the vehicle speed, the type of vehicles involved, the impact speed, and the status of the airbag). Results show that a complete Knowledge Discovery in Databases (KDD) process, with an adequate selection of relevant features, allows generating estimation models able to predict the severity of new accidents. We develop a prototype of our system based on off-the-shelf devices, and validate it at the Applus+ IDIADA Automotive Research Corporation facilities, showing that our system can notably reduce the time needed to alert and deploy the emergency services after an accident takes place.