Abstract
Smart vehicles using Internet of Things systems are significant game-changers in smart cities, enhancing user comfort, security, and safety. Due to the VANET (Vehicle Ad-hoc Network), many cars are using V2V (Vehicle-to-Vehicle), I2V (Infrastructure-to-Vehicle), and V2I (Vehicle-to-Infrastructure) communications to notify of and detect collisions. But without prior notification, dangers such as earthquakes/flash floods can endanger the safety of passengers at any time. Thus we need real-time updates and notification through IoT-based sensors and warnings through predictions to the vehicle as I2V and V2I communications. Through apps, we can gather data regarding speed, gravitational force, pressure, sound, and location from the user and when changes occur abruptly and cross a threshold, a notification is sent to a centralized VANET using mobile metadata and IoT, and prediction using machine learning (ML), to determine a hazard-prone area is proposed. A fault-tolerant, low-cost alert system with fewer errors and false alarms is a key focus of this research.
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