## 2 Literature Review
Initially offered in [22] was a vehicle control strategy for multi-directional collision avoidance. Numerous researchers have tried to develop collision-warning systems based on non-parametric methods because the prior parametric deterministic approaches have the drawback that they do not represent the influence of PRT. ANN-based technology is one of the most important new technologies on the back-end CWS. The advantage of using an ANN-based approach is that it can deal with handling complex unobservable problems. Initially suggested in [23] was a vehicle control strategy for multi-directional collision avoidance. The multi-layer perceptron neural network and fuzzy logic algorithms, respectively, were used in this study. More recently, a method to integrate fuzzy logic and neural networks was meant to create an algorithm that operates a car on the highway based on highly accurate GPS data [24]. The authors in [25] developed an algorithm for making decisions for AEB pedestrians using radar and camera sensors for the data fusion technique. The EuroNCAP protocol's potential collision avoidance scenarios were examined and a reliable pedestrian tracking method was suggested. By creating the system activation zone with the relative speed and potential distance needed to stop pedestrians, as well as by utilizing a brake model to anticipate the collision avoidance time, the performance of the AEB system was improved. In [26], a MATLAB-created AEBS module, based on two radar sensors, was suggested. The proposed AEBS depends on a short-range and long-range radar, the time between the detection of the obstacle in front of the autonomous vehicle, and the speed of the autonomous vehicle. Figure 3: Overview of stopping distance algorithm [19]. In [27], an autonomous vehicle collision avoidance and pedestrian detection system was proposed based on stereo vision. It monitors the area using two cameras that are placed at a set distance apart. When a pedestrian is detected, the system estimates the stopping distance, and the suggested controller algorithm will initiate braking if the calculated distance is less than the safe driving distance. Using an infrared camera that can detect heat and lidar sensors in RC cars for obstacle detection, ref. [28] proposed a new obstacle recognition method for accurately identifying front cars and pedestrians and reducing the danger of vehicle collisions in bad weather. By using RC cars as testbeds for autonomous vehicles, the author showed that the suggested technique is feasible by integrating lidars and a thermal infrared camera on vehicles. In [29], a new method to determine the distance (Hurdle Detection) was proposed for a secure environment within a moving vehicle. Eight ultrasonic sensors are employed in this system to detect various object types. The car and sensor are allowed to function normally up to the sensor detecting a potential risk by putting into practice a potential increase in the safety system in the vehicle. The literature review suggests that the existing system employed one or two (mostly radar and vision) types of sensors for data fusion with various EBS controller algorithms. Existing obstacle identification fusion technologies have resulted in major accidents because they cannot effectively identify vehicles or pedestrians at night and in bad weather. False positives of any sensor are also drawbacks of the previously existing AEB systems that may cause disruptions or accidents. In this paper, our contribution is to introduce a dependable multi-sensor fusion architecture and a reliable decision-making algorithm for the AEB controller to perform autonomous emergency braking and protect pedestrians. The proposed multi-sensor fusion architecture has three different types of sensors: radar, lidar, and vision sensors. The TTC and stopping time calculation approaches are used to implement the AEB controller and FCW algorithm. A non-linear speed controller is included in place of existing linear controllers in addition to supporting AEB.