Driving Event Detection and Driving Style Classification using Artificial Neural Networks
IEEE International Conference on Industrial Technology
Toronto, ON, Canada
March 22 - 25, 2017
Abstract: Knowledge about the driving behavior of a driver is important for applications in many different areas, especially for Advanced Driver Assistance Systems. The driving style does not only affect the current driver and his vehicle but also his environment. For example, usage-based insurances classify the driving style in order to reward calm drivers by granting them a discount. In this paper we present a novel algorithm to provide an accurate classification of a person's driving style. Our model is based on the identification of driving maneuvers and the classification of the driving style for these events using artificial neural networks. Furthermore, an overall score of the driving style for one trip is calculated based on the classified events. We validate our developed model in 58 test trips from different test drivers using a recently developed low-cost measuring device based on a Raspberry Pi.
The results of our validation show that the model can identify more than 90 % of the driving maneuvers correctly. Moreover, the driving style classification matches the assessment of the driver in 81 % of the relevant trips with a normalized average mean squared error of less than 11 %. In addition, a moving average of the calculated score for each event shows validated changes in the driving behavior of the test persons.