According to the Association for Safe International Road Travel, around 13,000,000 are killed in road accidents every year. Research conducted in the field of driver safety has identified driver fatigue or sleepiness as one of the major causes of road crashes. In addition to driver fatigue, drunk driving and unpredictable conditions like weather, bad roads, etc., also lead to life threatening situations.
How can one identify and avoid such unexpected situations? An ideal solution would be to passively study and record a driver’s behavior pattern while driving under normal circumstances and raise an alert when the driver is drifting away from the normal pattern.
In this paper, we diagnosed the driving patterns that eventuated to collisions. Real-time driving data were collected from the OBD II port of a small segment car without interfering with the driver. The driving pattern was then analyzed and classified using a five-layer neural network to build a more effective driver safety program.
To know how the enhanced driver safety program proactively predicts abnormal driving patterns, thereby preventing accidents, download and read this paper now.