SmartDENM—A System for Enhancing Pedestrian Safety Through Machine Vision and V2X Communication

A pivotal moment in the leap toward autonomous vehicles in recent years has revealed the need to enhance vehicle-to-everything (V2X) communication systems so as to improve road safety. A key challenge is to integrate real-time pedestrian detection to permit the use of timely alerts in situations where vulnerable road users, especially pedestrians, might pose a risk. Seeing that, in this article, a YOLO-based object detection model was used to identify pedestrians and extract key data such as bounding box coordinates and confidence levels. These data were encoded afterward into decentralized environmental notification messages (DENM) using ASN.1 schemas to ensure compliance with V2X standards, allowing for real-time communication between vehicles and infrastructure. This research identified that the integration of pedestrian detection with V2X communication brought about a reliable system wherein the roadside unit (RSU) broadcasts DENM alerts to vehicles. These vehicles, upon receiving the messages, initiate appropriate responses such as slowing down or lane changing, with the testing demonstrating reliable message transmission and high pedestrian detection accuracy in simulated–controlled environments. To conclude, this work demonstrates a scalable framework for improving road safety by combining machine vision with V2X communication.