Leveraging V2P communications to improve the VRU’s safety

The advent of connected and autonomous vehicles (CAVs) holds immense promise for revolutionizing road safety and efficiency. Communications between the vehicles and other road users or elements (V2X) opens various possibilities that can prevent accidents and fatalities. Vulnerable Road Users (VRUs), like pedestrians and cyclists, are at particular risk since a collision with a vehicle has a high likelihood of inflicting severe injuries. On the other hand, VRUs usually have one or more connected devices, and these can be used to increase their safety by allowing the prediction and prevention of collisions with CAVs. However, for this to be possible, it will be necessary to ensure not only reliable and low latency communication between the VRUs, CAVs and, eventually, Road Side Units (RSUs), but also the development of robust and reliable collision prediction algorithms capable of anticipating potential collisions and enabling timely interventions to prevent accidents. This paper presents a system that can be used to track a vehicle and a VRU, predict possible collisions and alert the VRU about the incoming danger. Additionally, it delves into the intricate aspects of collision prediction algorithms for CAVs, addressing the fundamental requirements for effective collision prediction and presenting a balanced approach that meets the demands of speed, accuracy, robustness, adaptability, and scalability.