Analysis of driver behaviors during common tasks using frontal video camera and CAN-Bus information

Even a small distraction in drivers can lead to life-threatening accidents that affect the life of many. Monitoring distraction is a key aspect of any feedback system intended to keep the driver attention. Toward this goal, this paper studies the behaviors observed when the driver is performing in-vehicle common tasks such as operating a cellphone, radio or navigation system. The study employs the UTDrive platform -a car equipped with multiple sensors, including cameras, microphones, and Controller Area Network-Bus (CAN-Bus) information. The purpose of the analysis is to identify relevant features extracted from a frontal video camera and the car CAN-Bus data that can be used to distinguish between normal and task driving conditions. Statistical hypothesis tests are used to assess whether the differences observed in the selected features are significant. Then, these features are used in binary classification tasks (normal versus task). For most of the considered tasks, features extracted from the frontal video camera are found to be the most prominent indicators to distinguish between normal and task driving conditions (e.g., head pitch and yaw). The features from the car CAN-Bus data slightly improve the classification accuracy, from 76.7% (using features only from the frontal video) to 78.9% (using all features).