A Deep Learning Approach to Vehicle-to-Vehicle Communication for Enhanced Road Safety

As Vehicle-to-Vehicle communication (V2VC) technology continues to develop, it is clearly important that safety and driver awareness is becoming more and more important. Our experiment dives into some practical applications of V2VC by simulating real-time communication among vehicles in a network circle. The primary focus of the research is to explore how vehicles can communicate with one another to share realtime driving updates-such as hazards, traffic conditions, nearby emergencies, and vehicle malfunctions-to enhance road safety and maintain smooth traffic flow. Our developed deep learning (DL) framework provides a robust system for thoroughly exploring and understanding the context of V2VC. The experiment also demonstrates how the evolved V2VC technology has the potential to create a new way of multi-vehicle communication for a better driving experience, increase driver awareness, enhance road safety, and improve efficiency in high-traffic flow scenarios.