Characteristic analysis for cognition of dangerous driving using automobile black boxes

Automobile black boxes are devices that collect information regarding vehicle operation and the driver’s operating situation in the case of a traffic accident. The information collected from the automobile black box, which can also be used during normal driving, can provide information about dangerous driving cognition. This study was designed to analyze characteristics of dangerous driving data and build a dangerous driving cognition system as follows. First, dangerous driving is divided into four types by considering the vehicle’s movement, such as acceleration, deceleration, turning and statistical data of traffic accidents. Second, dangerous driving data were collected by vehicle tests using the automobile black box, and characteristics of the driving data were analyzed to classify dangerous driving. Third, a standard threshold was chosen to recognize dangerous driving, and an algorithm of dangerous driving cognition was created. Finally, verification was conducted by vehicle tests with automobile black boxes embedded with the developed algorithm. The presented recognition methods of dangerous driving can be used for on/off-line management of drivers and vehicles. Scientific traffic accident databases can be built with this driving and accident information, and can be used in various industrial areas.