## 2 Related Work
The raw data of the inputs are tracked to find eye openness, mouth openness, head yaw angle, head pitch angle, the vehicle's position and to recognize speech. Through this tracked information, the driver's states of danger can be classified into drowsiness, distraction, high pulse rate, drunk driving, aggressive driving, and stress. Once the driver's behavior is found to be in a dangerous state, recommendations are generated and given to the driver. The proposed method was tested in real time, and it enhanced driver safety and reduced the probability of road accidents. The limitations faced in this work are that the proposed model will not be suitable in remote areas where mobile connectivity can be disturbed. In addition, every time a new driver drives a car, he or she has to install the application on their phone. In [30], the authors proposed an approach to effectively increasing the spreading of information by deploying a latent edge with high potential influence. The strategy is called the \"latent-edge-influence strategy\" (LEI). Through this strategy, the highest potential influence at which an edge should be introduced is calculated so that it will provide effective performance in spreading information. The proposed method was compared with the degree-product strategy (DP) and eigenvector centrality product (ECP). A monitoring and coaching strategy for reducing road accidents caused by vehicles transporting heavy goods was proposed in [31]. Using this strategy, the authors compared the vehicles of two companies, A and B, to calculate the increase in performance by implementing the strategy. The time period for comparing the vehicles was one year, in which the first eight months were considered as the baseline period in which parameters such as harsh braking, harsh cornering, and over speeding were noted. The next time period of four months was termed the intervention period. During this period, Company A deployed both monitoring and coaching, whereas Company B deployed only monitoring. By calculating the above-mentioned parameters during the intervention period for both companies, it was proved that the proposed strategy performs better in reducing road accidents and improving driver safety. However, the strategy uses only cameras to detect when the driver is in a state of emergency, which will not be accurate. The parameters used in this strategy will not be sufficient enough to differentiate the causes of accidents. In addition, no real-time recommendations are given to the driver, which would be more effective than coaching. In [32], the authors proposed a fuzzy cluster analysis based on risk sensitivity and a judgment threshold for two different age groups of drivers. The risk level was evaluated by integrating both data obtained for expert drivers and the driversof the different age groups. Fisher discriminant analysis was used to classify drivers into discriminant models. The analysis showed that the risk sensitivity and judgment threshold are better for drivers of the younger age group, whereas the drivers of the elderly age group ignore risks due to inefficient judgment of the threshold level. This showed that the younger drivers adapted slowly to the driving simulation system. However, the analysis was carried out with a low number of drivers; if it were performed under a real-world scenario, the accuracy would be decreased, and the proposed model would not detect the behavior of drivers, which would further affect the performance. A hybrid convolutional neural network framework was proposed in [33] by combining residual neural network 50, Inception version 3, and Xception. Transfer learning is used to extract the features of drivers. These features are concatenated to obtain useful information. The proposed framework is trained to ignore the non-distractive behavior of drivers. Ten classes of typical driver behavior were tested, and the features were highlighted by using class activation mapping.