Railway systems have been in use for over two centuries, and they only continue to grow more complex with every year. Today’s infrastructure managers and signaling suppliers are faced with the formidable task of balancing ever-increasing demands for greater speed, frequency, capacity, and smarter features with higher requirements on safety and efficiency.
There is a clear need for technology that can help make sense of the complexity and remove the safety concerns involved in developing and operating rail control systems. Luckily, this is an area where a lot of progress is already being made.
As experts in signaling design automation here at Prover, we always have our eyes open for the technological puzzle piece that can help deliver even safer and more efficient systems. Right now, we are especially excited about the prospect of combining the generative abilities of AI with the precision and rigor offered by formal methods.
Here we will explore how rail control and signaling systems can potentially benefit from AI, with a particular focus on generative AI such as Large Language Models (LLMs).
Why LLM and Formal Methods make a promising match
At Prover, we are currently exploring how AI can be used to assist at different phases of the rail control system development process, especially working in conjunction with our existing tools. In particular, we see great potential in Large Language Models where there have been rapid advancements in recent years.
A large language model (LLM) is a type of artificial intelligence (AI) program with the ability to achieve general-purpose language understanding and generation by being trained on massive amounts of data. We see an opportunity to use LLMs to, for example, help generate data to use in our safety tools, build models for automation, and generally make our tools even easier to use.
If applied correctly, LLMs could very well revolutionize requirement management, development, and V&V processes in the railway signaling industry. Let’s explore further.
Potential applications of LLM in rail control projects
Here are some of the ways LLM-based technologies could be used to benefit the different development phases of rail control and signaling systems, as well as other safety-critical systems.
1. The requirement engineering phase
During the requirement engineering phase, a LLM-based assistant could intelligently answer questions about a collection of requirement documents written in natural languages. It could also extract structured information (from the requirements), and offer suggestions and identify issues aimed at improving the quality of requirement specifications.
2. The design and implementation phase
During the design and implementation phase, including activities such as configuration data creation, model-based software design, and code production, a LLM-based modeling tool could be used to create initial versions of data, software logic models, and formalized requirements (as logic formulas), from given requirements and high-level instructions.
3. The integration, verification, and validation phase
During the integration, verification, and validation phase, a LLM could be used to improve the usability of formal verification tools that have complex user interfaces. For instance, an intelligent formal verification assistant could autonomously perform complex and advanced verification tasks based on the high-level instructions and goals it receives from users.
In addition to the above, we are also interested in exploring how AI could assist the non-safety critical parts of signaling systems, such as traffic management systems. The AI could, for instance, create timetables and scheduling automatically based on user requirements and other constraints.
Based on our initial exploration, it is clear that AI, namely the LLM, has a ton of potential for use in combination with our tools to achieve much higher automation and enhanced safety in rail control signaling systems. All while helping infrastructure managers and signaling system suppliers to more easily balance and meet demands for greater safety, efficiency, innovation, and cost-effectiveness.
At Prover, we have already started prototyping and experimenting, and plan to apply for research funds to study and implement the proposed approaches in the near future.