## 1 Introduction
### Sensor Fusion  
The method to combine data from various cameras, lidars, and radars to create a single model or image of the area surrounding a vehicle is known as sensor fusion. As a result of balancing the strengths of the various sensor, the model that results is more precise. The data collected by sensor fusion can subsequently be used by vehicle systems to enable smarter actions [9].  
Every sensor type or modality has advantages and disadvantages on its own. Even in adverse weather, radars are quite effective at calculating distance and speed, but they are unable to recognize the color of a stoplight or read street signs. Cameras are excellent at interpreting signs or categorizing objects such as humans, other vehicles, and bicycles. However, they are quickly dazzled (unresponsive) by debris, the snow, the light, the rain, or the night. Lidars are capable of precise object detection, but they lack the price and range of cameras or radars [10]. Some properties of vision, lidars, and radars are given below Table 1.  
To create a more thorough and accurate environmental model, sensor fusion combines the data from all of these different types of sensors, or a highly variable collection of sensor modalities. By using a technique called internal and exterior sensor fusion, it may also correlate with the information obtained from the environment [11]. The information from numerous sensors of the same sort, such as radars, might also be combined by a vehicle via sensor fusion. Trying to take advantage of slightly overlapping areas of view enhances the experience. More than one sensor will pick up things simultaneously when more than one radar scans the area around a vehicle [12].  
The detection likelihood and reliability of things nearby the vehicle can be increased by fusing or overlapping the detections from those various sensors when interpreted by global 360\({}^{\circ}\) perception software, which also produces a more accurate and trustworthy picture of the environment. The most fundamental distinction between centralized and decentralized sensor fusion is made by the data being used, the raw sensor data, the features extracted from the sensor data, and the judgments made by utilizing the extracted features and other information. Depending on how it is used, sensor fusion may have the following advantages:  
* Improved data dependability
* Improved data quality
* Estimate the unmeasured states
* Expanded coverage  
Radar or vision-based systems are frequently employed in previously built systems, and the implementation of an AEBS can be conducted in MATLAB and Simulink. The suggested technique uses sensor data fusion from radars, vision, and lidars. The fundamental form used to track automobiles, people, or any obstacle in good or bad weather is the sensors model, which is based on the acquisition of MATLAB and Simulink. Three advanced  
\begin{table}
\begin{tabular}{c c c} \hline \hline
**Radar** & **Lidar** & **Vision** \\ \hline Long-range sensing & Precise 3D object detection & Object classification \\ \hline Object movement & Range accuracy & Object angular position \\ \hline All weather performance & Free space detection & Scene context \\ \hline \hline \end{tabular}
\end{table}
Table 1: Main features of radar, lidar, and vision sensors.  
sensor-based sensor fusion techniques are proposed, which are included by the MATLAB model in the emergency braking procedure. In contrast to earlier linear systems, our advanced system also features a non-linear super twisting speed controller which also supports EBS.  
This paper aims to demonstrate stable AEBS operation to avoid or minimize forward collisions and prevent accidents while any vehicle is identified. In Section 2, the existing AEBS and fusion methods that have been applied in this field are discussed. The research and methodology are covered in Section 3, a simulation is discussed in Section 4, Section 5 consists of Results and Discussions, and Sections 6 and 7 consist of a comparison with the existing research and the conclusion, respectively.