## 3 Research Methodology
### Sensor Fusion  
One of the most difficult parts of the area of autonomous vehicles is the identification and tracking of moving objects. The performance and natability of the solutions are vital since tackling this challenge is essential for autonomous driving. As a result, it is usual for the car to use all of its installed sensors. The most well-liked method uses data from lidars, radars, and cameras to create sensor fusion. Early methods for the detection and tracking of moving objects concentrated on combining sensor data after tracking with additional data from a simultaneous localization and a mapping module. For a more comprehensive understanding of the processes, additional fusion was performed at the track level. An innovative method in this research area combines detection from the lidar and radar levels with the camera-based classifier after feeding regions of interest from the lidar point clouds into it. The tracking module receives its data from the fusion module, which is used to create a list of moving objects. The perceived model of the world is enhanced by including the object classification from various sensor detectors. Figure 11 displays a block diagram of the various sensors perception system. Instead of processing the data through a system to obtain object information or to extract features, restricted fusion is often used to pre-process lidar and radar data in a sensor fusion, combining camera, lidar, and radar sensors data. The combined data are then added to high levels of blocks of fusion that incorporate camera input. In this situation, high-level fusion produces the detection and classification while low-level fusion addresses the mapping and localization method. To make a high-level fusion by combining the low-level fusion as inputs may be one perceived trend in an autonomous vehicle. The movement classification and effectiveness of the data association can be enhanced by using visual shapes and information about the object class when choosing an object detection method. A tracking system can alternate between the 3D box and point representations depending on how far the object is from the vehicle. This means that camera data  
Figure 11: The system with multiple sensors for perception [33]. Figure 10: Comparison of PI with super twisting controller [32]. are essential for localization and tracking activities as well. In the future, tracking systems will be able to track more accurately thanks to the exploration of contextual data about urban traffic surroundings. A camera can also provide information about its width and height, estimated speed, and relative position. The test vehicle's relative velocity to an object moving forward can be determined using radar. The ability of a radar sensor to detect a car is good, but it does not do well when a pedestrian is present. The precise relative distance, estimated velocity, object class, and breadth and height can all be determined with the lidar. The test vehicle with the three different sensors and leading vehicle detected is shown in the below-given Figure 12.  
The relative distance and relative velocity of the item are the details needed in this study for data fusion. Equations (15)-(18) explain the measured data _z(k)_ at time step \(k\). \[z(k)=\left\{z^{r}(k),z^{R}(k),z^{L}(k),\right\} \tag{15}\]  
\[z^{r}(k)=\left\{v_{1},v_{2},\ldots,v_{p}\right\}\hskip 28.452756 ptv_{i}=\left[x \ y\ \dot{x}\right]^{T}\hskip 14.226378 pti=1,\ldots,p \tag{16}\]  
\[z^{R}(k)=\left\{r_{1},r_{2},\ldots,r_{q}\right\}\hskip 28.452756 ptr_{i}=\left[x \ y\ \dot{x}\right]^{T}\hskip 14.226378 pti=1,\ldots,q \tag{17}\]  
\[z^{L}(k)=\left\{l_{1},l_{2},\ldots,l_{r}\right\}\hskip 28.452756 ptl_{i}=\left[x \ y\ \dot{x}\right]^{T}\hskip 14.226378 pti=1,\ldots,r \tag{18}\]  
Here, \(z^{r}(k)\), \(z^{R}(k)\), and \(z^{L}(k)\) denote the measured value of the vision, radar, and lidar at time step \(k\), respectively.