Semester of Graduation
Electrical and Computer Engineering
First Major Professor
Master of Science (MS)
Typical telematics and fleet-management systems today use embedded systems attached to the vehicle and get their driving data from their diagnostics port to identify the action of driver and grade it to provide feedback based on their quality of driving to efficiently handle the vehicle and also their driving behavior.
Today’s insurance companies provide embedded devices or the customer’s smartphone to analyze basic driving parameters such as speed, rpm, GPS location to understand driver’s braking, acceleration and distance travelled over a period and use it to assess quotes for insurance premium.
But most of the solutions above do not consider of context specific information in the cases of fixed-route scenarios whose details can be understood better in the first place and use it to grade the driver’s performance for the trip more efficiently.
In this experiment, a driver’s behavior on a pre-defined route is analyzed on different perspectives by also taking into account of the road context, such as turns, straight road segments, traffic lights, stop signs etc. and graded accordingly and providing a score to reflect their behavior in each segment of the road as well as a complete score for their trip.
Shaikh Mohammed, Ashraf
Shaikh Mohammed, Ashraf, "Analyzing driving behavior from CAN data using context-specific information" (2019). Creative Components. 428.