# Multi-Factor Rear-End Collision Avoidance in Connected Autonomous Vehicles
## 1 Introduction  
According to the WHO [1], around 20 to 50 million people suffer from severe injuries in road traffic crashes, with many experiencing disabilities because of their injury. Road traffic injuries were the leading cause of death for children and adults between the ages of 5 and 29 years [1]. To reduce fatalities and injuries from road traffic crashes, the World Health Organization (WHO) acts as a team with partners responsible for technical support to countries. The leading cause of fatalities and injuries is the rear-end collision, which make up 70% of all vehicle collisions [2]. Another report, according to the authors in [3], is that 1.078 million injuries in the USA are only due to rear-end collisions. So, an efficient collisionavoidance system in vehicles is needed to reduce the death rate [1]. In the existing literature, many researchers have proposed solutions for collision avoidance from the rear-end. The authors in [4] proposed a rear-end collision avoidance controller based on proportional-integral-derivative. Another research was proposed by authors in [5] for vehicle rear-end collision avoidance using the linear quadratic optimal control technique. The problem with these solutions is that these are highly dependent on mathematical models [6]. The problem of mathematical-model-based solutions can be overcome using fuzzy logic [7]. Now we figure out important factors from literature which can be used in fuzzy logic to solve the problem of rear-end collision avoidance. The authors in [8] used the physical, environmental, and mental factors to reduce the chances of accidents (COA). The authors in [9] analyzed and discussed the road and weather condition factors in accident occurrence. The authors in [10] also used the environmental factors, such as road and weather conditions in rear-end crash avoidance. Driver characteristics can be added in reducing accident chances and increasing the flexibility of the algorithm. The authors in [11] proposed an algorithm in which they pass the characteristics of driver in the proposed algorithm and showed a significant improvement. Driver's characteristics are also important in decision making because the warning thresholds can be improved by adding driver experience [12], age, and time of accident, along with the factors that are used in [8]. Different factors can change the results of accident occurrence. The authors in [12] discuss different single factors and multi-factors involved in road accidents. The time of the accident can also play an important role in accident avoidance in all these factors discussed in [12]. The combinations of these factors, as discussed in [8; 11; 12], can improve decision making while driving. We will use fuzzy logic to check how these factors can increase the chances of accidents. First, we examine whether these fuzzy rules can be verified and validated or not. There will be serious problems, e.g., false warning when there is no need for that, if the fuzzy rules are not properly validated. There is an existing model in this regard that provides the Virtual Overlay for Multi-Agent System (VOMAS), which can test any kind of system for accurate results. VOMAS can be applied using NetLogo tool for the validation purpose of different simulations. The authors in [13] used the VOMAS in their proposed system. The proposed system requires the output of the Fuzzy system to take actions for accident prevention. The actions can be simulated with the help of NetLogo Tool. This is possible if we fed the input of multi-levels of factors, such as environmental and physical conditions [8], driver [11], and weekday and time [12], into the simulation model. The contributions of the proposed system are as follows:  
* Providing simulation-based solution to the problem highlighted by authors in [12] of multiple factor analysis. * Use of the Mamdani Fuzzy Inference system to compute the values of factors involved in collisions. * Highlighting the combination of leading factors involved in rear-end collision.