## 6 Discussion Section  
### Discussion  
Context aware technologies employ sensors and other informational sources to inform users and systems about the operating environment and activities within a given domain. Advanced implementation of these systems will increasingly seek to integrate information concerning the physical, physiological, and psychological dimensions of the user. The integration of these three dimensions enables the output of context aware, and other ambient intelligence systems, to provide enhanced monitoring and reporting to a third party on the well-being or behavior of a target population in fixed locations. This context rich information can better inform the management of activities or resources and ultimately the support and motivation of certain behaviors that improve overall wellbeing.  
Although previous research results such as [33] driver's stress is assessed during driving which is based on three different factors as behavior, environment and route familiarity, the system error in modelling driver's stress becomes very high due to the usage of the XGBoost algorithm and the training time is large as compared to the Catboost and light GBM algorithms. In [36], CNN is presented for driver behavior detection and classification, but CNN is not effectively handle in all image features converted from  
Figure 14: Number of edges vs. false alarm rate.  
Figure 13: Number of vehicles vs. false alarm rate.  
signals. Behavior modelling also takes a large amount of time for prediction. This increases the chance of accidents and does not ensure traffic safety through early alert message transmission. Thus, in this work the proposed MODAL-IoCV approach is validated with respect to two different metrics, namely the number of vehicles and the number of edges, respectively. The driver behavior analysis is carried out and the generation of personalized assistance based on the current state of the driver is performed in the proposed approach. The classification of driver behavior is facilitated based on the significant features such as visual features of driver, vehicular features, and physiological features acquired from the smart wearables. The driver behavior is extensively influenced by the physiological features which are used to accurately detect the driver behavior. The integration of visual features and physiological features contributed to the increased accuracy of the classification of driver behavior. The existing approaches implemented conventional models and considered either any of the feature set which affects the accuracy of detection. The proposed SqueezeNet performs the classification of driver behavior with reduced time, thereby reducing the overhead associated with the computation. The warning message dissemination and personalized assistance generation are executed by TA-SAC, which provides assistance based on multiple factors. Through this, the proposed approach is able to achieve increased accuracy and reduced latency, motion prediction error, and false alarm rate of about 90%, 0.16 s, 10%, and 0.37, respectively.  
The proposed approach possesses reduced latency due to the execution speed of the existing approaches, which requires only a fewer number of parameters. The lightweight nature of the proposed model makes it possible to execute with less consumption of time.  
Table 7 presents the numerical comparison of the performance metrics of our proposed MODAL-IoCV and the existing approaches with respect to both the number of vehicles and the number of edge nodes. From this, we can conclude that our proposed approach outperforms the existing approaches in all of the metrics to provide precise, personalized assistance and warning message dissemination based on the analysis of the behavior of the driver.