A Perceptive Collision Avoidance System Enhanced for Terrain Guidance in Elevated Terrain Situations

The increasing number of vehicles on the roads has resulted in a higher risk of accidents and collisions. To address this critical issue, this research proposes an intelligent system for vehicle collision avoidance using the Internet of Things (IoT). The system aims to enhance road safety by providing real-time monitoring, analysis, and proactive collision avoidance mechanisms. The system operates in three main stages: perception, decision-making, and action. In the perception stage, sensor data from vehicles and infrastructure are collected and processed to gather relevant information about the surrounding environment. This includes detecting the presence, position, and velocity of nearby vehicles, pedestrians, and obstacles. In the decision-making stage, the collected data is analyzed and evaluated using machine learning techniques. The system generates real-time predictions and risk assessments based on the analyzed data to determine potential collision scenarios and prioritize the most critical situations. This system also has an over-speed detection feature that monitors speed and notifies the driver when the car exceeds a certain speed restriction.