# Multi-Factor Rear-End Collision Avoidance in Connected Autonomous Vehicles
## 2 Related Work
According to their study, the most influential factors are environmental and human factors. In addition, high-speed driving, cell phone use, and use of substances also increases the risk of accidents. The authors suggested some strategies to reduce the chance of accidents. [PERSON] et al. [26] proposed a novel approach for collision avoidance between autonomous vehicles following social norms and emotions. The authors used the fuzzy logic to compute the results of factors involved. A simulation was created for the proposed model using NetLogo. [PERSON] et al. [27] proposed a forward collision avoidance algorithm where fuzzy logic rules were used for initiating critical brake control. The researchers in [28] have proposed a rear-end collision avoidance scheme between vehicles. They have considered factors such as the road, vehicle type, driver, and external environment. For implementation purposes, Fuzzy Logic, VISSIM, and MATLAB were used. The authors in [29] deal with two key aspects of road transport: efficiency and safety. The proposed system detects the obstacles and generates the warnings and then sends them to the driver. In case the driver fails to perform an action, the control shifts to cruise control system. The authors in [30] proposed software-based collision avoidance systems using Dedicated Short-Range Communication (DSRC). They performed the timing analysis of events based on the DSRC detection range, communication latency, and road condition. [PERSON] et al. [31] also proposed a collision warning system based on DSRC. The authors in [32] proposed a collision warning algorithm in which they analyze different factors (human, road condition, time, and position), which can affect the performance of collision. The authors in [33] proposed a collision avoidance system where traffic lights communicate with nearby smartphones. Then these smartphones share warning with other smartphones. Though they performed collision avoidance, but no factors were used in their proposed system. The authors in [34] described a rear-end collision system using a model called the Bayesian Network. The model depends on ego factors of drivers and the braking intention of the front vehicle. They will implement and test their work in the future. The authors in [35] proposed a framework for space-based collision avoidance. V2V communication and a machine learning approach were used to accurately detect the collision and avoid its occurrence. According to the authors in [36], features of human drivers have been used to control the rear-end collision using fuzzy logic, and according to the authors in [37], fuzzy logic can resolve the rear-end collision avoidance mathematical issues. In our previous work [8], we have proposed a V2V rear-end collision avoidance algorithm with the help of fuzzy rules bearing in mind the environmental, physical, and mental factors. These factors contribute to road collisions. A Multi-Factor-Based Road Accident Prevention System (MFBRAPS) was proposed to avoid collisions. MATLAB and Net Logo were used for the implementation. In this paper, we are fetching new important factors which can help the collision avoidance algorithms in more effective way based on existing research. We put these new factors in MFBRAPS for a better collision warning system in V2V.