## 4 Proposed Work
### Cluster-Based Vehicle Motion Detection
The accuracy decreased as the number of vehicles increased; this was due to the overhead caused by the higher number of vehicles. The proposed DBA-PA model had comparatively higher accuracy than the other existing approaches due to an increase in the scalability of detection from classifying the driver's current state based on MCB-HCC and A3C. The existing approaches lack in terms of considering an increased number of vehicles in the driving environment, which results in reduced accuracy due to scalability issues. #### 5.2.6 Impact of Assistance Accuracy

The assistance accuracy is referred to as a measure for assisting the driver based on the detected behavior of the driver. For a system said to be efficient, the assistance accuracy should be high. Figure 10 is a comparison of the assistance accuracy of the proposed DBA-PA model with other existing approaches with respect to the number of risky drivers. The assistance accuracy decreased as the number of risky drivers increased. The accuracy of the DBA-PA model in assisting was comparatively higher than the other approaches due to the generation of precise assistance to the drivers. This is carried out by using the multi-attribute utility model, in which the provided assistance is also based on the current location of the vehicle. Assistance is generated one or more times for drivers based

Figure 10: Number of Risky Drivers vs. Assistance Accuracy. Figure 9: Number of Vehicles vs. Driver-Behavior Detection Accuracy. on detecting the current state multiple times to reduce the possibility of road accidents. The existing approaches provided only limited assistance, which is not appropriate for many real-time situations. #### 5.2.7 Impact of Latency

Latency is a significant factor that affects the efficiency of the behavior analysis and assistance generation system. Latency in providing assistance results in delayed assistance, which is not useful as there are many uncertainties in driving environments. Assistance must be provided with ultra-low latency for reducing casualties in the driving environment. Figure 11 is a comparison of the latency of the proposed DBA-PA model and MADBA and D-QLOA [41] with respect to the number of vehicles. It can be seen that the latency increased as the number of vehicles increased. The proposed method had low latency compared with the other existing approaches due to the computation of driver behavior and assistance generation in the fog layer. The existing approaches perform computations in a cloud server, which introduces latency in assisting, thereby affecting performance. Figure 12 is a comparison of the latency of the proposed DBA-PA model and FC-IOV [7] and ACOM [28] with respect to the number of fog nodes. The latency decreased as the number increased. The proposed DBA-PA model had very low latency compared with the other approaches. This is due to the computation of behavior analysis performed using E-RSU and fog nodes. The E-RSU performs clustering and motion prediction with which the behavior analysis and prediction are carried out with ultra-low latency. The execution of clustering along with the computation of driver behavior in the fog layer reduces the latency to a very low value. For instance, the latency associated with the proposed model when five fog nodes were deployed was around 4 s, which is very low. Further, the deployment

Figure 11: Number of Vehicles vs. Latency. Figure 12: Number of Fog Nodes vs. Latency. of virtual fog nodes in the case that there are no fog nodes reduces latency to a greater extent. The existing approaches also perform computation in fog layers, but the lack of consideration for large numbers of vehicles in the environment introduces latency, which affects the proficiency of these approaches. #### 5.2.8 Impact of False Alarm Rate

The false alarm rate is due to the inaccurate detection of driver behavior and assistance provided to the driver; this further results in increased distress and panic to the driver.