AI-Driven IoT Framework for Vehicle Accident Avoidance and Detection with Cloud Integrated Energy Efficient Solutions

IoT based machine learning solution formulated in this paper aims to enhance the real time accident analysis and prevention for vehicles. The IoT sensors interface with advanced deep learning models and the cloud computing system to form a real-time analytics system that acknowledges the data gathered by the sensors, assesses existing risks of accidents, and provides actions that can be taken to prevent them. Energy is one of the main characteristics implemented, where sleep scheduling and adaptive data transmission is used to maximize the battery life of battery driven IoT devices. The use of cloud integration also helps increase the scalability of the given system by referenced computation needs and the favorite continuous learning from accumulated logs. Simulation outputs show better identification accuracy well over 95% on accidents and reduced power usage relative to other IoT applications. Due to the capacity to optimally discern and integrate performance, energetic, and real-time responsiveness, the system offers a feasible strategy for increasing vehicular safety at smart transportation systems.