REAR COLLISION AVOIDANCE SYSTEM USING MACHINE LEARNING INTEGRATED WITH ARDUINO

Abstract

The rear collision avoidance system using machine learning integrated with Arduino is designed to enhance vehicle safety by preventing rear-end collisions through real-time detection and alert mechanisms. This system utilizes machine learning algorithms to analyze sensor data from ultrasonic mounted at the rear of the vehicle, enabling it to detect the presence, speed, and distance of approaching vehicles or obstacles. By leveraging machine learning models trained on diverse driving scenarios, the system can predict potential collisions and trigger appropriate warnings. The Arduino microcontroller serves as the core processing unit, collecting sensor data and executing the trained model’s decision-making process. The system is further equipped with an alerting mechanism, such as buzzer alarms and LCD, to notify drivers of imminent threats. Additionally, the integration of wireless communication allows data transmission to a central monitoring system for further analysis and improvements. The implementation of such an intelligent rear collision avoidance system significantly enhances road safety by reducing the likelihood of rear-end crashes, minimizing human errors, and offering a cost-effective solution that can be easily incorporated into existing vehicle models.