MULTI-VEHICLE COLLABORATIVE HAZARD DETECTION USING IOT AND EDGE NETWORKS

Abstract

Road safety is a major concern due to the increasing number of vehicles and the presence of unexpected hazards such as accidents, obstacles, and traffic congestion. Traditional hazard detection systems rely on individual vehicle sensing, which limits awareness beyond a vehicle’s immediate surroundings. This chapter proposes a multi-vehicle collaborative hazard detection system using Internet of Things (IoT) and edge computing technologies to enhance real-time hazard awareness and accident prevention. Vehicles equipped with IoT sensors, including cameras, radar, LiDAR, and GPS, continuously collect environmental and vehicular data. Edge computing nodes process this data locally to enable low-latency hazard detection, while detected hazards are shared with nearby vehicles through Vehicle-to-Vehicle (V2V) communication. Centralized cloud coordination supports broader traffic analysis and system optimization through V2X communication. The proposed architecture improves detection accuracy, reduces response time, and enhances overall traffic safety, making it a scalable and efficient solution for intelligent transportation systems and connected vehicular environments.