# Smart Roads for Autonomous Accident Detection and Warnings
## 2 Related Works
### Cooperative Approaches  
[PERSON] et al. [17] presented a vehicle-to-vehicle (V2V) and vehicle-to-infrastructure (V2I) communication system that leverages short-range communication technologies. The on-board unit (OBU) and the roadside unit (RSU) are the two primary components of this system. The OBD includes a processor, vehicle sensors, a touch screen, speakers, radio link modules, Bluetooth, and a GPS module. The OBU is mounted on vehicles and connected with the system and the in-vehicle network. The primary function of the OBU is to offer vehicle-to-vehicle communication and to gather diagnostic messages. The RSU is a stationary unit installed on roadways and in other conspicuous locations, such as gas stations and toll plazas, to facilitate V2I communication. The RSU receives and sends information to the OBU for ITS and internet services. Messages about collisions or speed limits are shown on a display screen. A survey by [PERSON] et al. [18] discussed the latest techniques and algorithms in connected and automated vehicles (CAVs) for the identification, perception, planning, and control of vehicles. They addressed remarkable issues related to decision making on path tracking and control of vehicle communications in a cooperative environment. They mentioned how, in a CAV, stability and loss of signals is the major problem in wireless communications. Finally, issues and challenges unsolved in CAVs were outlined for researchers and vendors. A framework for safe lane changing in connected and automated vehicles was proposed by [PERSON] et al. [19]. Their lane-changing approach was based on V2V and V2I communications for better movement and traffic operations. The study suggests that changing lanes is risky when the vehicle's declaration is more than the threshold value of a leading vehicle. Total time, surrogate safety measures, and traffic waves are among the characteristics used to make lane-changing decisions. A heuristic technique is used to identify the lane-changing zones before simulation. A cooperative strategy is implemented near highway off-ramps via vehicle-based control of the aforementioned applications. The main limitation of this work is that it did not consider the environment of human-driven vehicles and the diversity in calculating lane-changing behaviors in different and unexplored places. A cooperative neighboring vehicle positioning system (CNVPS) [20] was introduced by [PERSON] et al. that leverages GPS technology and other sensors to improve localization. The GPS is used to provide locations owing to its availability 24 h a day, 7 days a week, and in any weather. The technology uses a local map to identify and interact with nearby cars (using V2V communication) in order to obtain an accurate relative position. It uses maximum likelihood estimation to obtain the correct position of a nearby vehicle. The suggested that CNVPS refreshes positions faster; however, it has a high deployment cost and can only be used for already predicted behavior. To overcome GPS refinement concerns, a DL-based cooperative vehicle localization algorithm, called a graph convolution network-CNVPS (GCN-CNVPS), was proposed [21]. To facilitate V2V communication, the technique uses GPS technology to obtain the coordinates, relative distances, angles to neighboring vehicles, and received signal strength indication (RSSI) information from a basic safety message (BSM). Three DL-based approaches that require less hardware were presented to address the challenge of localization. Finally, comparison results showed that the GCN-CNVPS effectively utilizes temporal and spatial correlations with lower overhead. Through fuzzy logic and the Dempster-Shafer Theory, a predictive multi-criteria technique [22] was employed for multi-criteria decision-making in Avs following other vehicles and for lane changes. [PERSON]'s rule is used to identify risk values for hypotheses and trajectories, while fuzzy logic is used to handle uncertain situations and nonlinear data.