## 2 Related Work
### Approaches Based on Deep Learning  
Deep learning networks have shown promising results towards solving computer vision problems in the last decade [29; 30]. [PERSON] et al. implemented an LSTM recurrent neural network for real-time detection of driver's distraction, head tracking, and modeling the temporal context of long-range driving [31]. They were able to implement subject-independent detection of inattention. [PERSON] et al. used the Faster-RCNN deep learning model [32] and obtained an accuracy of 94.2% on the dataset developed by [PERSON] et al. [22]. [PERSON] et al. proposed a deep learning architecture, DarNet, that used the sensor data as an input for the classification of driving behavior [25], yielding better results than the baseline model. In 2016, [PERSON] et al. proposed an R-CNN model to detect the hand position on the steering wheel [33]. In the same year, [PERSON] et al. implemented AlexNet for head pose estimation and used the stacked hourglass network in the refinement stage to predict facial landmarks and reduce the face localization [34]. They classified distraction based on the yaw angle of the driver's head [35]. In 2017, [PERSON] et al. proposed an architecture to classify open and closed eye images with different conditions, acquired by a visible light camera, using a deep residual convolutional neural network [36]. In the same year, [PERSON] et al. made a comparison of various deep learning networks including Inception, ResNet-50, and MobileNet using the ImageNet Large Scale Visual Recognition Challenge (ILSVRC) 2012 dataset [13]. Their key insight was that the MobileNet outperformed the other networks. In 2018, [PERSON] et al. implemented VGG-16 to identify the cause of distraction and achieved an accuracy of 99% on the State Farm Distracted Driver Detection (SFDDD) dataset [37]. In the same year, [PERSON] et al. [38] developed an assisted driving testbed to create realistic driving experiences and validate the distraction detection algorithms. They used four CNNs, AlexNet, VGG-16, ResNet, and GoogLeNet, that were implemented and evaluated on an embedded GPU platform. They also developed a warning system for alerting the distracted driver in real time. Another distraction detection system based on convolutional neural network and data augmentation techniques was proposed by [PERSON] et al. whose main purpose was to decrease overfitting and increase the variability of the dataset [39]. In 2019, [PERSON] et al. proposed a CNN model based on a Gaussian mixture model (GMM) for recognition of driver's behavior [40]. To minimize the training cost, transfer learning was applied before training the model. They used three CNN models, namely: GoogLeNet, AlexNet, and ResNet-50. The authors focused on classifying various activities including texting, rear and side mirror checking, answering a cellular phone, talking, and using an in-vehicle radio device. They were able to achieve an 81.6% accuracy using AlexNet, and a lower accuracy of 78.6% and 74.9% using GoogLeNet and ResNet50, respectively. They were able to achieve 78.6%, 81.6%, and 74.9% using AlexNet, GoogLeNet, and ResNet-50, respectively. More recently, [PERSON] et al. empirically observed that Inception-V3 coupled with bidirectional LSTMs outperformed other CNN and recurrent neural network (RNN) architectures with an average F1-score of 93.1%. Their approach focused on identifying postures which were indicative of distraction. In 2020, [PERSON] et al. proposed a bimodular approach for distraction detection consisting of two modules on a self-generated dataset [41]. The first module computes the bounding box of the driver's right ear and hand, and provides this as input to the second module. The second module of the proposed approach used the input information to predict the distraction type. [PERSON] et al. proposed an ensemble method to detect distracted drivers by stacking the feature vectors of various convolutional networks [42].