Driver Classification Based on Driving Behaviors

In this project, we develop a model capable of classifying drivers from their driving behaviors sensed by low level sensors. The sensing platform consists of data available from the diagnostic outlet of the car and smartphone sensors. We are interested in arbitrary real world driving behavior such as turning, stop to start, start to stop and braking. We develop a window based support vector machine model to classify drivers. We test our model with two datasets collected under different conditions. Furthermore, we evaluate the model using each sensor source (car and phone) independently and combining both the sensors. The average classification accuracies attained with data collected from three different cars shared between couples in a naturalistic environment were 75.83%, 85.83% and 86.67% using only phone sensors, only cars sensors and combined car and phone sensors respectively.

This paper is published at IUI’16 Proceedings of the 21th International Conference on Intelligent User Interfaces .(Acceptance Rate: 24%) [Paper]

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