A RAILWAY ACCIDENT PREVENTION SYSTEM BY USING AN A INTELLIGENT PILOT VEHICLE

Abstract

Recent train mishaps brought on by collisions and track failures highlight how urgently improved safety measures are needed. In order to identify track gaps, avoid collisions, and offer live streaming for real-time surveillance, this project presents an experimental vehicle-based railway monitoring system. With the help of the train's momentum and no separate motors, the pilot vehicle—a lightweight attachment placed 200 meters in front of the train—moves down the rails with little assistance from a battery. To detect gaps, it has a laser transmitter and receiver device placed thoughtfully on either side of the track. A camera is triggered for visual confirmation if the receiver detects an interruption in the laser beam, which indicates a possible track gap. Furthermore, a collision detection system that is integrated with the pilot car uses cameras and sensors to detect obstructions within a 200-meter radius, such as approaching trains, trespassers, or animals. Additionally, the system has a live-streaming camera that gives railway control centers visual input in real time, guaranteeing prompt response when abnormalities are identified. By facilitating proactive maintenance and obstacle identification, this novel strategy not only increases the dependability of railway operations but also reduces the danger of accidents. The suggested approach sets a standard for upcoming improvements in railway infrastructure by utilizing straightforward but powerful sensor technology to provide a scalable and affordable answer to contemporary railway safety issues.