RT Journal Article
JF IEEE Transactions on Mobile Computing
YR 2014
VO 13
IS 5
SP 948
TI A System for Automatic Notification and Severity Estimation of Automotive Accidents
A1 Manuel Fogue,
A1 Piedad Garrido,
A1 Francisco J. Martinez,
A1 Juan-Carlos Cano,
A1 Carlos T. Calafate,
A1 Pietro Manzoni,
K1 vehicular ad hoc networks
K1 artificial intelligence
K1 automotive electronics
K1 data mining
K1 emergency services
K1 intelligent transportation systems
K1 road accidents
K1 road vehicles
K1 Applus+ IDIADA Automotive Research Corporation facilities
K1 automatic notification
K1 severity estimation
K1 automotive accidents
K1 communication technologies
K1 modern vehicles
K1 artificial intelligence systems
K1 emergency services
K1 assistance time
K1 intelligent system
K1 road accidents
K1 vehicular networks
K1 data mining
K1 knowledge inference
K1 vehicle speed
K1 impact speed
K1 airbag
K1 knowledge discovery
K1 databases
K1 KDD process
K1 estimation models
K1 off-the-shelf devices
K1 Accidents
K1 Vehicles
K1 Sensors
K1 Estimation
K1 Emergency services
K1 Databases
K1 Mobile computing
K1 Mobile communication systems
K1 Computer Systems Organization
K1 Communication/Networking and Information Technology
K1 Network Architecture and Design
K1 Wireless communication
K1 Mobile Computing
K1 traffic accident assistance
K1 data mining
K1 vehicular networks
AB 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 emergency services 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 that can 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 emergency services after an accident takes place.
PB IEEE Computer Society, [URL:http://www.computer.org]
SN 1536-1233
LA English
DO 10.1109/TMC.2013.35
LK http://doi.ieeecomputersociety.org/10.1109/TMC.2013.35