Course Overview
This course covers the application of artificial intelligence in railway signalling systems, from fundamental concepts to real-world implementations. It explores AI techniques for perception, positioning, cognitive signalling, traffic management, asset health monitoring, and predictive maintenance within the railway context.
Key Takeaways
- Understand AI fundamentals and their relevance to railway signalling and ERTMS.
- Learn AI-based perception and positioning methods including computer vision and sensor fusion.
- Explore cognitive signalling and predictive traffic management using reinforcement learning.
- Gain insights into asset health monitoring and predictive maintenance models for signalling infrastructure.
- Examine safety, standards, and strategic implementation challenges in AI-enabled railway systems.
Target Learners
This course is suitable for railway signalling professionals, AI practitioners interested in transport applications, engineers, and researchers seeking to understand AI integration in railway signalling and traffic management systems.
The Third Digital Revolution 6:53
Defining AI for Signalling 6:10
Machine Learning Fundamentals 5:30
Deep Learning & Neural Networks 7:48
Signalling Evolution 5:30
ERTMS Deep Dive 8:11
The Positioning Problem: 6:32
Computer Vision Positioning 6:55
Obstacle Detection (OD) 7:09
Z-Movements and Switch Recognition 7:06
Sensor Fusion 7:32
The Paradigm Shift: From Reactive to Predictive Traffic Management 7:34
Intellectualization of Train Control 6:44
Intelligent Dispatching (Case Study: TPS.live) 7:03
Reinforcement Learning for Traffic Flow Optimization 5:51
The Cost of Failure: Switches and Interlockings 5:33
Predictive Maintenance Models: Transitioning to PdM 6:47
Digital Track Capturing & Digital Site Surveys (DSS) 5:15
AI Maintenance Co-pilots & Troubleshooting Assistants 5:12
Access Files No pi required.
Free Course files 1 file package
- Full Pack
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