## 5 Results and Discussion  
In the proposed system, radar, lidar, and vision sensors are used with various angular views for the simulation utilizing the AEBTestBench module so that the simulation's field of coverage will be greater. The model consists of two primary subsystems. The first is the research part, which includes the AEB with a sensor fusion and AEB controller. The second part of the system is Environment and Vehicle, which mimics the dynamics of the ego vehicle and the surrounding environment. Driving scenario readers, lidar point cloud producers, and radar detection generators all give synthetic sensor data for the objects. The AEB controller is used to compute the stopping time and implements the AEB control algorithm and the FCW in accordance. This model is intended to run for 6 s in order to check each result. The reaction time of the system is 0.1 s. For instance, in a simulation, if a vehicle is spotted by sensors and the conditions (0 > T_ttc and T_FCW > 1T_ttc 1) hold, the system will generate an alarm or warning within 0.1 s.  
Figure 15 shows the observed number of lidar, vision, and radar sensor detections in addition to the computed sensor fusion result detections. The number of detections represents the quantity of the leading vehicle tracks that the sensors have detected. The track-to-track fuser block and data concatenation are used to implement the fusion method. The block receives data from the cuboid lidar, rectangular lidar, rectangular radar, and rectangular vision detection and outputs fused tracks. The data concatenation block creates a single-track bus by combining the number of detections from all the sources. Using the Source-Config variable through the Pre-Load-Function callback, the fuser source configuration for the radar, lidar, and vision is set. Track fusion is represented in this graph at a single time step. The fact that the fused tracks are more accurate and precise than the individual sensor detections shows that the fusing of the detection estimates from all three sensors improves track accuracy. Fusion detection is utilized for additional computations. In this scenario, all the sensors are healthy; no error is found in any sensor. The stopping time is the period from when an ego vehicle first decelerates by using its brakes until it comes to a complete stop. The stopping time is calculated mathematically using Equation (2). When the FCW system alerts a driver that a collision with the lead vehicle is imminent, they are required to react to the alarm and to apply the brake during the delay period. Equation (3) gives the total distance that the ego vehicle must travel before colliding with the lead vehicle. The TTC of the leading vehicle must be smaller than the TFCW for the FCW alert to be activated. The AEBS reacts automatically to prevent or decrease the impact of the collision when a driver fails to apply the brakes. AEBSs typically use a step-by-step braking technique that alternates between partial braking in multiple stages and full braking. The following Figure 16 describes the autonomous emergency braking logic utilized by the AEBS controller to initiate the FCW and show the AEBS status. Figure 14: Dashboard display of AEBS. Figure 16: Status of AEB, FCW, ego car stop, and collision at the normal scenario. Figure 15: Sensors measured detections and calculated detections with sensor fusion. Figures 16 and 17 show how the vehicle's speed increased and reached its maximum speed of 8.3 m/s in one second, as well as how the FCW and PB influenced the vehicle's speed. The leading car was identified as soon as the system started, and the ego vehicle used the sensor fusion algorithm to identify it. The first stage of the FCW became true in the 1.2 s condition, at which point the FCW was immediately triggered.