## 4 Proposed Work
### Cluster-Based Vehicle Motion Detection
The existing approaches lack in scalable computation of motion prediction. #### 5.2.2 Impact of Number of Alerts

The assistance to the driver is to be provided whenever necessary, but improper analysis of driver behavior and inefficient recognition of states of danger result in an increased number of alerts to drivers, which further leads to discomfort. Figure 6 is a comparison of the number of alerts of the proposed DBA-PA model and MADBA [29] and IDA [43] with respect to the number of vehicles. The number of alerts increased as the number of vehicles increased. The proposed model had a low number of alerts, which means that it is efficient in precisely identifying states of danger to assist the driver. This is possible due to the efficient analysis of driver behavior performed using A3C from a heterogeneous amount of data and providing alerts in accordance with it. The existing approaches lack in terms of proper detection of states of danger, which affects the performance of these approaches. Figure 5: Number of Vehicles vs. Motion Prediction Error. Figure 6: Number of Vehicles vs. Number of Alerts. #### 5.2.3 Impact of Assisted Risk Maneuvers

Risk maneuvering is defined as the process of overcoming a state of risk with the help of the assistance provided by the system. It is an important metric for measuring the efficiency of the model in terms of detection and providing assistance. Figure 7 is a comparison of the number of assisted risk maneuvers with the proposed DBA-PA model and other existing approaches with respect to the number of vehicles. The number increased as the number of vehicles increased. For instance, when the number of vehicles was 60, the number of the proposed DBA-PA model reached 17, whereas, for MADBA and IDA, the number was reduced at 14 and 12, respectively. This was due to the efficient personalized assistance provided by implementing the multi-attribute utility model in which the assistance is provided for more than one identified risk state at a time. The existing approaches lack precise generation of assistance, which limits the efficiency of these approaches. #### 5.2.4 Impact of Vehicle-motion Detection Accuracy

The detection of vehicle motion is a significant process executed to accurately analyze driver behavior in order to identify states of danger. Figure 8 depicts the accuracy of vehicle-motion detection with the proposed DBA-PA model and other existing approaches with respect to vehicle speed. The vehicle-motion detection accuracy decreased as the vehicle speed increased. For example, when the vehicle was travelling at a speed of 80 Km/h, the detection accuracy of the proposed model was around 96%, whereas that of DMPC and TIP approached 89% and 84%, respectively. This is due to the formation of MCB-HCC and classification of vehicles based on mobility in each cluster with E-RSUs and AttResNet, for which mobility detection is based on velocity, speed, location, and direction. The lack of proper detection of vehicle mobility affects the performance of the existing approaches. In TIP method, Bi-LSTM was implemented to detect the behavior of vehicles based on lateral velocity, lateral acceleration and heading angle. However, Bi-LSTM takes more time for classification, and it is not able

Figure 8: Vehicle Speed vs. Vehicle-Motion Detection Accuracy. Figure 7: Number of Vehicles vs. Number of Assisted Risk Maneuvers. to train huge amount of data that increases the complexity in real time environment which decreases the vehicle motion detection accuracy of this method when compared with the proposed DBA-PA method. #### 5.2.5 Impact of Driver-behavior Detection Accuracy

The accuracy of detecting driver behavior is defined as a measure for accurately detecting the behavior of the driver and classifying the driver's current state based on analysis. Figure 9 is a comparison of the accuracy of the proposed DBA-PA model and that of MADBA and HCF [31] with respect to the number of vehicles.