## 1 Introduction  
Drowsiness and distraction are the two most significant reasons for fatal car accidents in the last two decades [1]. In 2017, the US Department of Transportation's National Highway Traffic Safety Administration (NHTSA) reported that 795 causalities in vehicle crashes were the result of driver drowsiness, which was 2.3-2.5% of the total fatal crashes in the US. According to NHSTA reports, 2841 lives were claimed in the accidents due to distracted drivers in 2018, which was 6-9% of the total fatal crashes in the US [2]. At the time of a crash, 13% of distracted drivers were using their cell phones, indicating that cell phone usage was a major cause of these crashes [2]. Some signs that indicate the drowsiness of a driver include the inability to keep eyes open, frequent yawning, leaning the head forward, and face complexion change [3].  
There are various metrics to determine the level of a driver's drowsiness: physiological, vehicle-based, and behavioral measures [4, 5]. In behavioral measures, information is based on the camera to detect slight changes in the driver's facial expression. Facial expression analysis uses a combination of multiple facial features to predict various characteristics of the face, such as attractiveness [6] or disease [7]. It also can be used to evaluate driver drowsiness, such as extreme head poses and wrinkles in the forehead [8, 9, 10], and facial landmarks [11]. Eye blink rate and eye closure rate are other drowsiness detection measures [12, 13, 14], along with the physiological signal registered using wearable (on-body) sensors, such as electroencephalography (EEG) [15, 16], respiratory signals [17], electrocardiogram/photoplethysmogram (ECG/PPG) signals [18, 19], and heart rate variability (HRV) [20]. Finally, the sitting posture of the human body can be recognized and evaluated using motion sensors [21; 22] or video recordings [23; 24] as well as hand gestures [25].  
Distraction is mainly a result of the driver's in-attention that may be due to cell phone usage, eating, talking to other passengers, texting, or adjusting the radio or climate controls. In terms of the driver's functionality, the NHTSA characterizes distraction as auditory, biomechanical, visual, or cognitive [26]. Various approaches for detecting driver distraction have been developed, which can be classified based on the parameters being measured as driving performance, subjective, physical, biological, and hybrid measures. Driving performance measurements, such as braking, steering, and other relevant driving behaviors, are among these that can be used to detect visual distraction [27]. Eye gaze is a useful distraction measuring tool, while subjective measures cannot be obtained in real-time in an uncontrolled driving environment. The drivers' biological measures also affect the driving operation.  
Human lives can be saved by using effective automatic distraction detection technologies. One way for detecting driver distraction is to detect the driver's 3D head rotation, which can be classified into three types: changes in the yaw angle produce spinning (rotation in the horizontal plane), rotation caused due to the changes in pitch angle (rotation in the vertical plane), and rotation caused due to the changes in roll angle (back and forth rotation). The paper proposes a scale-invariant system under a boosting framework that can detect the driver's head rotation due to a change in the yaw angle more accurately than the existing state-of-the-art methods. The work also proposes spatial and temporal variance-based features that estimate the geometric orientation of a driver's head.  
The structure of this paper is as follows. Section 2 discusses related work on the drivers' distraction. Section 3 describes the proposed methodology for distraction detection due to drivers' head panning, while the results and discussion are explained in Section 4. Section 5 presents the conclusion and discusses future work.