Design and Development of an Intelligent AI-Powered Speed Breaker System Using Computer Vision

Abstract

In the era of growing urbanization and traffic jams, they are, however, outdated and obstructive in high-traffic or emergency locations. This project introduces the concept of smart AI-based speed breakers that can ensure better road safety and efficient traffic management. The proposed approach is based on machine learning, computer vision, and real-time data processing, rendering a dynamic solution for speed control and emergency vehicle preemption. The system is supported by advanced object identification technology like the YOLOv8 model for identifying and classifying vehicles in real time. The system uses CNNs to estimate vehicle speed accurately and further to classify vehicle types such as emergency vehicles, VIPs, or standard vehicles. The OCR feature enhances the system's ability to capture and store vehicle license plate information for traffic monitoring and control. The speed breaker system is controlled by a set of motors and sensors that activate only when the car is detected as moving above the speed limit or tries to enter a restricted region. Unmarked roadside emergency units, such as ambulances, firefighting, and police vehicles, are quickly rec ognized and given priority when clear transit is required. C700AI's mechanical structure has a decoupling gear, motors, and bearing system, which work together with the AI part to raise and lower the speed breaker according to the real-time recognition of vehicles. The information collected through this technique supports the creation of operational intelligence for city planners and traffic control entities to advance the smart city infrastructure goal. The proposed design will improve traffic flow, reduce excessive wear on vehicles, and reduce accidents by reducing the potential hazard of the conventional speed breaker, especially in critical areas like schools, hospitals, and ambulance approach roads. This intelligent traffic technology gives all road users a more equitable answer for the time being, and it also facilitates future development for urban traffic planning, promoting a smarter, safer, and more sustainable transport ecosystem.