Railway transportation is increasingly relying on autonomous technology to improve safety and efficiency. However, conventional control systems often lack the ability to adapt to the dynamic and uncertain nature of railway environments. In this paper, we propose an intelligent autonomous train framework that integrates computer vision, artificial intelligence–based risk assessment, and adaptive decision-making. The proposed system is organized into four complementary phases, namely multi-sensor environmental perception, object detection using You Only Look Once version 5 (YOLOv5), deep neural networks aligned with safety integrity levels (SIL), and open automata–based control for real-time decision generation. All of these components work together to ensure that trains can operate independently and safely in complex situations. Experimental results demonstrate the system’s ability to accurately assess risks and provide robust and reliable performance under realistic operational conditions.