Driver decisions and behaviours regarding the surrounding traffic are critical to traffic safety. It is important for an intelligent vehicle to understand driver behaviour and assist in driving tasks according to their status. In this study, the consumer range camera Kinect is used to monitor drivers and identify driving tasks in a real vehicle. Specifically, seven common tasks performed by multiple drivers during driving are identified in this study. The tasks include normal driving, left, right, and rear mirror-checking, mobile phone answering, texting using a mobile phone with one or both hands, and the setup of in-vehicle video devices. The first four tasks are considered safe driving tasks while the other three tasks are regarded as dangerous and distracting tasks. The driver behaviour signals collected from the Kinect consist of a colour and depth image of the driver inside the vehicle cabin. Additionally, three-dimensional head rotation angles and the upper body (hand and arm at both sides) joint positions are recorded. Then, the importance of these features to behaviour recognition is evaluated using Random Forests (RF) and Maximal Information Coefficient (MIC) methods. Next, a Feedforward Neural Network (FFNN) is used to identify the seven tasks. Finally, the model performance for task recognition is evaluated with different features (body only, head only, and combined). The final detection result for the seven driving tasks among five participants achieved an average of greater than 80% accuracy, and the FFNN tasks detector is proved to be an efficient model that can be implemented for real-time driver distraction and dangerous behaviour recognition.

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