Railway Train Collision Avoidance on Same track and Animal Detection Using AI - IoT

Abstract

The increasing demand for safer and more efficient railway transportation systems has prompted the exploration of advanced technologies to mitigate collision risks and address emerging challenges such as animal incursions on railway tracks. This paper proposes a comprehensive solution leveraging Artificial Intelligence (AI) and Internet of Things (IoT) technologies for real-time detection and avoidance of collisions between trains operating on the same track and encounters with animals. These sensors capture various environmental and operational data such as train positions, velocities, and track conditions. The AI algorithms analyze this data to identify potential collision risks and animal intrusions. Additionally, real-time communication between trains and the centralized control system enables timely intervention and rerouting decisions to ensure safe operations. The system incorporates advanced image recognition algorithms to detect and classify animals near railway tracks. Utilizing high-resolution cameras and IoT-connected devices, the system identifies animals in the vicinity and alerts train operators or initiates automated braking mechanisms to prevent accidents caused by animal incursions. Key features of the proposed system include scalability to accommodate varying railway infrastructures, adaptability to diverse environmental conditions, and interoperability with existing railway signaling and control systems. Moreover, the integration of AI and IoT technologies enhances the system's predictive capabilities, enabling proactive risk mitigation and improving overall.