## 2 Literature Review  
The article [13] presents a recognition model for the front wheel of the car which is dependent upon the backpropagation (BP) neural network and hidden Markov model. In this proposed system, the inputs included are the brake pedal, accelerator pedal, and vehicle speed data, which are used to examine the driver's intention. An AEB model for the following vehicle is suggested that may dynamically adjust the critical braking distance under varied driving circumstances to avoid rear-end collisions, according to the recognized driver's intention provided through the Internet of Vehicles. In [14], based on model predictive control (MPC) theory, a variable time headway autonomous emergency braking (AEB) control method is suggested. This kind of interference is addressed by the variable time headway-based safety distance model. The AEB controller is designed with collision avoidance, driver characteristics, and ride comfort variables in mind. The variables used to build the state equation are the separation between the two vehicles, their relative velocities, and the ego vehicle's velocities and accelerations. The authors in [15] describe the new moving object identification and tracking system, which builds on and improves the previous system developed for the DARPA Urban Challenge in 2007. The author updated the previous motion and observation models to include active sensors and vision sensors. The vision module in the new system recognizes bikers, pedestrians, and cars and generates a vision target for them. This visual recognition information is used by the proposed system to improve the tracking prediction, data association, and motion categorization of the previous system. The purpose of [16] was to investigate the design and operation of an AEBS using the many fundamentals of mechanical and electronic engineering, commonly referred to as Mechatronics. An ultrasonic sensor combined with a stereo camera identifies an impediment in front of the car and provides information about the relative distances between the object and the vehicles in this system. The ECU will then determine whether or not an accident is likely to occur, and the brake will be deployed autonomously as a result of this approach. Time-to-collision (TTC) is one of the most extensively used time-based approaches presented in [17], and it was designed to evaluate the time it will take for an accident occurs between a previous and accompanying vehicle. In Figure 2, a summary of the TTC algorithm is presented. Figure 2: Overview of TTC Algorithm [18]. The TTC was used to calculate the remaining time for two vehicles to collide [18]. The following is how the TTC algorithm is calculated. \[\text{TTC}=\frac{\text{h}-\text{L}}{\
abla_{\text{P}}-\
abla_{\text{F}}} \tag{1}\]  
In Equation (1), h stands for the distance between the preceding and following vehicles, VF and VP stand for the speed of the following and preceding vehicles, and L stands for the length of the preceding vehicle. To address the shortcomings of the previous method, the researchers proposed new stopping distance-based algorithms (SDAs) [19]. Calculating a safe stopping distance is one of the most effective ways to keep an eye on the possibility of a rear-end accident. SDA-based techniques for assessing rear-end collision risk are based on the assumption that in a car-following position, the leading car's friction coefficient must be higher than the pursuing cars. An overview of the SDA is shown below in Figure 3[20]. When stopping at a given speed, longer reaction times increase the thinking distance; however, collisions can occur in this algorithm with shorter reaction times and faster decision times [21]. Based on non-parametric techniques, many scholars have sought to construct collision-warning solutions. Artificial Neural Network (ANN)-based technology is one of the most important new technologies in the rear-end collision warning system (CWS). The advantage of the ANN-based approach is that it can deal with solving complex unobservable problems.