## 2 Related Work
\begin{table}
\begin{tabular}{c c c c} \hline \hline
**Existing Work** & **Problems Addressed** & \multicolumn{2}{c}{**Proposed Solutions**} \\ \hline
33 & \(\bullet\) & Visual result of environment (scenes by images) & \(\bullet\) & Visual features of driver’s vehicle are considered in SqueezeNet for modelling facial features and head position. Furthermore, weather information is not of focus, which causes low accuracy in monitoring driver behavior. & is considered and learned by SqueezeNet for modelling the driver’s current behavior based on past sequences. \\
33 & \(\bullet\) & System error in modelling driver stress becomes very high due to the usage of the XGBoost algorithm, and training time is large compared to the Catboost and light GBM algorithms. & \(\bullet\) & SqueezeNet is a lightweight deep learning architecture that is able to process both large and small volumes of data to produce the corresponding outcomes. \\ \hline
34 & \(\bullet\) & Environmental and health status is not considered in this paper, which causes a higher accident risk and high economic damage. & \(\bullet\) & Alert messages are forwarded to the drivers and personalized driver monitoring is implemented in this work. \\
34 & \(\bullet\) & CNN does not effectively handle all image features converted from signals, and behavior modelling takes a large amount of time to generate predictions. This increases the chance of accidents and does not ensure traffic safety through early alert message transmission. & \(\bullet\) & SqueezeNet is used to handle individual type of features. Based on the features processed in SqueezeNet, the driver behavior is modelled. \\ \hline
35 & \(\bullet\) & Cloud-based driver behavior monitoring increases latency. It is not effective to handle the monitoring of a large number of drivers’ behavior for real-time warning message updates. & \(\bullet\) & Edge is deployed in monitoring all RSUs deployed on the road side, which decreases latency. It can handle the number of vehicles inside the region and multiaccess edges coordinate with the corresponding RSUs. \\
35 & \(\bullet\) & The health (physiological) parameters of the driver are not considered, which are important in emergency situations. The lack of smart sensor deployment increases pandemic cases. & \(\bullet\) & Smart wearables, smart sensors, and smart phone devices are used to continuously monitor the physiological parameters and sense the health issues of the driver for accurate behavior modelling. \\
35 & \(\bullet\) & Recommendations are not personalized for drivers since environment parameters (events) and weather information is not considered, which causes low accuracy in recommendations. & \(\bullet\) & Personalized assistance is recommended for drivers in which the driver’s next action is presented. Based on the action, route planning is initiated for drivers. \\ \hline
36 & \(\bullet\) & Vehicle motion is not focused, which measures the lane change deviation using image processing techniques. Hence, it is not suited for all types of drivers. High acceleration, continuous speeding, frequent lane changes, and large brakes must be considered to monitor driver behavior. & Vehicle motion and lane change detection is implemented using the hidden Markov model, which uses vehicle sensor information of past and current sequences. With this, we find vehicle motion of three classes: high, medium, and low. On the other hand, lane change is detected and classified into three classes: frequent change, no change, and mild change. & Vehicle motion and lane change detection is implemented using the hidden Markov model, which uses vehicle sensor information of past and current sequences. With this, we find vehicle motion of three classes: high, medium, and low. On the other hand, lane change is detected and classified into three classes: frequent change, no change, and mild change. & \(\bullet\) & Vehicle motion and lane change detection is implemented using the hidden Markov model, which uses vehicle sensor information of past and current sequences. With this, we find vehicle motion of three classes: high, medium, and low. On the other hand, lane change is detected and classified into three classes: frequent change, no change, and mild change. & \(\bullet\) & There are several classes of dangerous driver that are identified using multimodal feature processing in the SqueezeNet algorithm. \\ \hline \hline \end{tabular}
\end{table}