Fei Niu, författare på Prover - Engineering a Safer World Interlocking Design Automation to meet demand for complex digital train control Thu, 04 Sep 2025 09:45:29 +0000 en-US hourly 1 https://wordpress.org/?v=6.9.4 AI-driven railway signaling is now available via Prover Labs https://www.prover.com/ai/ai-driven-railway-signaling-is-now-available-via-prover-labs/ Mon, 02 Dec 2024 07:00:55 +0000 https://www.prover.com/?p=20580 Explore how Prover is revolutionizing railway signaling with AI and formal methods. Discover Prover Labs: a hub for innovation, collaboration, and shaping AI-driven automation for enhanced safety, efficiency, and precision in signaling design.

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There is huge potential to leverage AI in the railway signaling domain. Prover is an expert in developing railway control solutions. We’re continuously pushing the limits of what’s possible in automating signaling design. Our approach, built on formal methods and digital twins, has already set a new standard for automation and efficiency with guaranteed safety. Now, we’re taking these innovations even further by incorporating AI to accelerate and enhance these efforts.

Prover Labs is a space and community where innovation thrives. Through Prover Labs, we invite the railway signaling industry and our customers to explore and shape future AI-driven applications. Here, you can engage with our latest developments and test early versions of our AI-driven applications. It’s more than just a showcase; it’s an interactive environment where your feedback directly influences the future of our solutions and contributes to advancements in automation, efficiency, and safety.

The synergy between formal methods and AI is key to our approach. Formal methods ensure that AI-generated content, such as specifications, models, and code, is rigorously validated against strict rules and properties, guaranteeing accuracy and safety. In turn, AI, particularly large language models (LLMs), makes formal methods more accessible and scalable.

By combining AI, formal methods, and automation tools, we aim to deliver unprecedented efficiency and precision to railway signaling. Tasks that are currently manual—such as input document analysis, requirements engineering (including formalization), and compliance reporting—can be automated, reducing errors and accelerating project timelines.

At Prover Labs, we are exploring these possibilities and more. Starting with simpler applications, such as chatbots for Q&A, we are progressing toward advanced solutions capable of addressing complex challenges, including requirements formalization, compliance analysis, and verification—all powered by the combination of AI and formal methods.

This is just the beginning. Our early prototypes are now available for exploration, and we encourage you to join us on this journey. Try Prover Labs here. Your insights will help refine these tools and drive the next wave of innovation in the railway industry.

Inlägget AI-driven railway signaling is now available via Prover Labs dök först upp på Prover - Engineering a Safer World.

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How AI can take automation and safety to a new level in railway signaling https://www.prover.com/webinar/ai-automation-and-safety-railway-signaling/ Fri, 25 Oct 2024 09:43:03 +0000 https://www.prover.com/?p=20452 Discover how Prover is transforming railway signaling design automation with AI, formal methods, and digital twins for enhanced efficiency and safety.

Inlägget How AI can take automation and safety to a new level in railway signaling dök först upp på Prover - Engineering a Safer World.

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RECORDED WEBINAR

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Recorded on December 18, 2024

At Prover, we’re continuously pushing the limits of what’s possible in automating signaling design for railway control solutions. Our approach, built on formal methods and digital twins, has already set a new standard for automation and efficiency with guaranteed safety.

Now, we’re taking these innovations even further by incorporating AI to accelerate and enhance these efforts. In this webinar, we will explore how AI, together with formal methods and automation tools, can elevate signaling design automation and discuss the future possibilities it brings to the industry.

Agenda:
  • How AI will bring new levels of automation and safety

  • How formal methods powered with AI benefit from each other

  • The most relevant AI use cases in rail control solutions today

  • How to connect with our ongoing AI innovation via Prover Labs

  • Interactive Q&A with Prover AI experts

Speaker
Fei Niu Prover

Fei Niu
AI Innovation Lead

Yes please, send me the recording!

Inlägget How AI can take automation and safety to a new level in railway signaling dök först upp på Prover - Engineering a Safer World.

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Prover takes railway Signaling Design Automation to the next level with AI and formal methods https://www.prover.com/ai/railway-signaling-design-automation-with-ai-and-formal-methods/ Mon, 26 Aug 2024 12:00:58 +0000 https://www.prover.com/?p=19876 Prover is revolutionizing railway signaling with AI-powered design automation, leveraging formal methods and digital twins to enhance safety, efficiency, and modernization in the industry.

Inlägget Prover takes railway Signaling Design Automation to the next level with AI and formal methods dök först upp på Prover - Engineering a Safer World.

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Prover is dedicated to “Engineering a Safer World,” focusing on providing railway Signaling Design Automation with formal methods and digital twins. We are now taking the next step in accelerating this effort by using AI. As in so many other industries, AI has a huge potential to increase automation and efficiency.

The industry needs to be modernized, standardized, and digitized. The current process for developing and maintaining signaling solutions is slow and inefficient. Prover’s approach to Signaling Design Automation, based on formal methods and digital twins, has taken automation and efficiency to a new level. Our AI investments will continue that development and take this even further by leveraging the synergy between AI and formal methods.

Formal methods is a systematic approach that uses mathematical models to specify, develop, and verify software and hardware systems. They enable precise analysis and verification of system properties such as correctness, safety, and reliability. By applying these methods, developers can ensure their systems meet specified requirements, offering confidence in both system behavior and performance.

The relationship between formal methods and AI is mutually beneficial. Formal methods help ensure AI-generated content is accurate and safe by rigorously checking it against strict rules and properties. Simultaneously, AI, especially large language models (LLMs), can make formal methods more accessible and easier to use. LLMs can simplify the use of complex tools and the creation of necessary artifacts like formalized requirements and models. Additionally, AI can enhance the efficiency of formal verification tools, such as using AI-tuned tactics to guide the model checker during proof searches (read our blog for more information). This synergy enhances AI reliability while expanding the practical application of formal methods, driving innovation and safety in areas like railway signaling.

At Prover, we are working on how AI can bring new levels of automation and safety to railway signaling. By upgrading our existing offerings and developing new products that integrate LLMs, we aim to make complex processes like requirement engineering, design, V&V (verification and validation), and safety more intuitive and efficient.

AI technologies can help generate data and models, automate manual tasks (such as reviews and checks), and improve the usability and efficiency of formal verification tools, all while maintaining the highest safety standards.

Prover will attend InnoTrans 2024 in Berlin from September 24 to 27.

Visit us at booth 130 in hall 3.2.

At InnoTrans, we will demonstrate how AI can bring new levels of automation and safety to railway signaling.

  • We will demonstrate cases where AI can be used already today.
  • All the visitors will get access to Prover Labs and be able to use our AI tools themselves.
  • Book a meeting with us for a deep-dive session on how AI can take automation and efficiency to the next level!

Inlägget Prover takes railway Signaling Design Automation to the next level with AI and formal methods dök först upp på Prover - Engineering a Safer World.

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A guide on CENELEC EN 50716 https://www.prover.com/guide/cenelec-en-50716/ Wed, 20 Mar 2024 11:17:51 +0000 https://www.prover.com/?p=19051 In the realm of railway software development, adhering to industrial standards is not just a matter of compliance; it’s a cornerstone of ensuring safe, reliable, and efficient railway systems. The latest milestone along this journey is the introduction of the CENELEC standard EN 50716:2023.

Inlägget A guide on CENELEC EN 50716 dök först upp på Prover - Engineering a Safer World.

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Table of Contents

  1. Background and motivation
  2. Formal Methods become HR for all SILs
  3. Tool diversity
  4. Annex C
    • Lifecycle models
    • Modeling
    • AI/ML
  5. Improvements in management and organization
  6. Miscellaneous changes
  7. Conclusion

Background and motivation

In the realm of railway software development, adhering to industrial standards is not just a matter of compliance; it’s a cornerstone of ensuring safe, reliable, and efficient railway systems. The latest milestone along this journey is the introduction of the CENELEC standard EN 50716:2023. This new standard represents a significant leap forward compared to its predecessors, EN 50128:2011 (and its amendments A1 and A2) and EN 50657:2017 while maintaining a smooth transition from its predecessors and a closer alignment with the railway RAMS standards (EN 50126 and EN 50129).
This development holds significant importance for us at Prover, given our intimate connections with these standards, wherein we provide EN 50128-compliant software tools and applications. Thus, we have thoroughly studied EN 50716 and are excited to share our findings and thoughts here in this blog post. Join us as we delve into the nuances of this latest standard, exploring both its implications and potential impacts.

Formal methods become HR for all SILs

The “Highly Recommended (HR)” endorsement of the Formal Methods technique has now extended to also include lower SILs (e.g., SIL 1 and SIL2 which used to be only R).

As a longstanding believer and practitioner of Formal Methods, we are delighted to see the further endorsement of EN 50716 for the technology. Thanks to advancements in technology and much-improved usability over the years, which Prover has contributed to, Formal Methods have become the efficient and cost-effective approach for rail systems they are today.

Tool diversity

Originating from Clause 6.7.4.4 c of EN 50128, the “tool diversity” concept was introduced in Amendment A2:2020 of EN 50128 and then included in EN 50716. The concept of tool diversity concerns classes T2 and T3 of tools. Model checkers (such as Prover PSL) and testing-related software utilities fall into the class of T2 tools, which is specifically designed for verifying code or data. In contrast, T3 tools are involved in the creation or transformation of code, commonly represented by code compilers and generators. Essentially, tool diversity allows T3 tools to delegate (some) trust to T2 tools, see a quote from the clause:

“Use one tool to perform the function and subject its output to verification of results by an independent tool… for example a rule-checking tool to confirm that the output conforms to all relevant rules.”

This represents a significant evolution from our perspective, as it aligns seamlessly with the principles of our Signaling Design Automation (SDA) solution Prover Trident, which was established many years ago. The independent T2 and T3 tools of the Prover Trident tool suite are Prover Certifier and Prover iLock, respectively. The application software code generated from Prover iLock (based on some GA) can be checked independently by Prover Certifier for conformance to formalized safety requirements (ref. “conforms to all relevant rules”). Therefore, the evidence for Prover iLock working correctly and safely can be based on the use of Prover Certifier, which is highly trusted and certified by TÜV NORD as a CENELEC EN50128-compliant T2 tool for SIL 4 applications.

Annex C

This annex in EN 50128 has been replaced with Annex C “Guidance on software development” in the new standard EN 50716, which now consists of the following three very interesting sections:

  • “C.1 Lifecycle model examples”,
  • “C.2 Modelling”, and
  • “C.3 Artificial Intelligence and Machine Learning”.

Lifecycle models

Section C.1 provides guidance and examples regarding two lifecycle models, i.e., linear lifecycle models (such as the waterfall and v model) and iterative lifecycle models. Linear lifecycle models offer a structured and predictable approach to software development, widely considered the conventional choice. The iterative lifecycle models, on the other hand, prioritize flexibility and responsiveness (to changes), albeit requiring more frequent communication and coordination among team members.

There has been a growing interest in applying iterative models (or agile methodologies) in safety-critical systems including rail systems. We find it interesting that iterative lifecycle models are explicitly accepted by the standard. However, adopting these iterative lifecycle models requires careful consideration to balance flexibility and safety assurance, ensuring that safety and regulatory requirements are adequately addressed. Achieving such a balance can be facilitated by leveraging a model-based approach, adaptability (e.g., utilizing tool diversity as described above), and rigorous methods such as Formal Verification. The automation embedded in formal verification processes enables the assurance of safety with reduced verification times and great certainty. Prover’s SDA approach fits this profile perfectly.

Modeling

In Section C.2, the advantages of modeling approaches are explored, and guidance is provided regarding their application in the context of the standard.

At Prover, we are also excited by this new inclusion in the standard, as modeling (using our formal languages such as PiSPEC, LCF and HLL) serves as one of the cornerstone techniques in the Prover Trident solution and the Digital Twin approach. As highlighted in the excerpt from the standard below, the modeling approach brings even greater benefits when combined with Formal Verification (using formal proofs):

“Models can also reduce the need for certain activities, particularly when using formal proofs.”

AI/ML

Artificial Intelligence (AI), in particular Machine Learning (ML) as the most relevant among the AI areas, is discussed in Section C.3, with respect to the difficult challenges to be addressed in the context of applications in the scope of EN 50716. Because of the challenges (e.g., the V&V of ML components), the ML techniques have not yet become viable techniques for rigorous software development within the standard.

While we agree that it is natural to have such concerns about ML given its inherent characteristics (such as statistical modeling and interpretability), from a different perspective, we at Prover have started to explore how ML techniques can accelerate the adoption of Formal Methods. For more details, see our blog post on AI.

Improvements in management and organization

Following a significant refinement for Clause 5, the requirements on software management and organization have become more concise, succinct, and in some cases relaxed, thereby facilitating users to navigate standards with greater ease and flexibility.

One example of removing/relaxing unrealistic restrictions is the fact that changing the roles of Verifier and Validator is no longer prohibited (but is still discouraged) as outlined in Subclause 5.1.2.10 f of EN 50716, compared to Clause 5.1.2.4 of EN 50128.

The role of Integrator in EN 50128 is removed from EN 50716 and the responsibilities of the role fall under the roles of Implementer and Tester. Instead of the Integrator, the Implementer is supposed to manage the integration process, whereas the Tester is supposed to take responsibility for writing relevant specifications such as the Software Integration Test Specifications and the Software/Hardware Integration Test Specification.

The “Safety Authority” body is no longer relevant in EN 50716. For example, the Assessor has the flexibility to belong to any stakeholder organization without requiring the approval of the “Safety Authority”, as outlined in Subclause 5.1.2.5.

Miscellaneous changes

There are also some changes of various kinds we find interesting and worth highlighting here.

  1. EN 50716 switched to “Basic Integrity” defined in EN 50126 and stopped using “SIL 0”, which probably corresponds to one change described in the foreword: “Better alignment with EN 50126-1:2017 and EN 50126-2:2017, including definitions, has been made;”
  2. Some justifications and documents are no longer required for the T2 tools used for Basic Integrity. See e.g., Subclauses 6.7.4.2 and 6.7.4.3.
  3. Programming languages can also refer to diagrammatical or modeling languages rather than just textual programming languages (such as C, C++, Java, etc.). Additionally, Clause 7.3 (Architecture and Design) introduces criteria for choosing suitable programming languages, which in part replace the requirements related to the fitness for purpose of programming languages (Subclause 6.7.4.7 of EN 50128).
  4. In EN 50716, the term “application algorithms” has been removed from the title of Clause 8. This change may clarify that this clause primarily addresses the requirements for application data rather than generic or application software. The application data primarily consists of parameterization information for specific installations and the application software defines the intended behavior (or logic) of specific applications. Prover iLock is a complete development process for signaling systems, particularly application data and software. Hence it is part of our continuous endeavor to maintain its compliance with Clause 8 of EN 50716.

Conclusion

In summary, here at Prover we are leading the way when it comes to embracing the latest advancements within the standard, while continually enhancing our solutions and products to closely align with the requirements of EN 50716. Our dedication ensures that our customers and partners are fully equipped to meet these requirements and thrive in their endeavors.

Inlägget A guide on CENELEC EN 50716 dök först upp på Prover - Engineering a Safer World.

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Exploring the evolution of railway software standards with EN 50716 https://www.prover.com/quality/exploring-the-evolution-of-railway-software-standards-with-en-50716/ Fri, 23 Feb 2024 08:15:56 +0000 https://www.prover.com/?p=18834 In this blog post we discuss the significance of adhering to industrial standards in railway software development, particularly focusing on the introduction of the CENELEC standard EN 50716:2023 as a major advancement.

Inlägget Exploring the evolution of railway software standards with EN 50716 dök först upp på Prover - Engineering a Safer World.

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In the realm of railway software development, adherence to industrial standards is not just a matter of compliance; it’s a cornerstone of ensuring safety, reliability, and efficiency in railway operations. The latest milestone in this journey is the introduction of the CENELEC standard EN 50716:2023. This new standard represents a significant leap forward from its predecessors, EN 50128:2011 and EN 50657:2017, while maintaining a smooth transition from its predecessors and a closer alignment with the railway RAMS standards (EN 50126 and EN 50129).

At Prover, we’ve been deeply involved in the landscape of railway software standards, providing compliant tools and applications for years. Now, as we delve into the nuances of the standard, we’re thrilled to share our insights and discoveries with you.

One of the standout features of EN 50716 is the expanded endorsement of formal methods across all Safety Integrity Levels (SILs), a testament to the growing efficiency and effectiveness of these techniques for rail systems. Moreover, the concept of “tool diversity”, introduced in previous standards and further solidified in EN 50716, opens new avenues for trust delegation among T2 and T3 tools1, enhancing the robustness and flexibility of software development processes. These evolutions align seamlessly with our longstanding commitment to formal methods and the principles (regarding tool diversity) that have guided Prover’s product design for many years.

But evolution doesn’t stop there. Annex C of EN 50716 introduces fresh perspectives on software development, including guidance on two lifecycle models (linear versus iterative), modeling techniques, and the integration of Artificial Intelligence and Machine Learning (AI/ML). While iterative lifecycle models offer flexibility, they also demand careful navigation to maintain safety and regulatory compliance—a balance we’re well-equipped to address at Prover.

As for AI/ML, while their potential is undeniable, challenges in verification and validation of ML components within the context of EN 50716 remain. However, at Prover, we’re already exploring how these technologies can synergize with formal methods to drive innovation and efficiency.

Another notable change we wish to highlight before concluding this blog post is the major improvements made to software management and organization (clause 5) in EN 50716. These improvements eliminate outdated terminology, enhance clarity, and improve readability, thereby facilitating users to navigate standards with greater ease and flexibility while maintaining the same level of rigor.

1 Model checkers (such as Prover’s PSL) and testing-related utilities fall into the class of T2 tools, which is specifically designed for verifying code or data. In contrast, T3 tools are involved in the creation or transformation of code, commonly represented by code compilers and generators.

Read the complete guide on CENELEC standard EN 50716 here!

How safe and efficient are your rail control systems? Let’s find out!

Inlägget Exploring the evolution of railway software standards with EN 50716 dök först upp på Prover - Engineering a Safer World.

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Using AI to unlock the next breakthrough in rail control system performance https://www.prover.com/formal-methods/using-ai-to-unlock-the-next-breakthrough-in-rail-control-system-performance/ Wed, 15 Nov 2023 08:39:38 +0000 https://www.prover.com/?p=18645 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).

Inlägget Using AI to unlock the next breakthrough in rail control system performance dök först upp på Prover - Engineering a Safer World.

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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.

What’s next?

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.

How safe and efficient are your rail control systems? Let’s find out!

Inlägget Using AI to unlock the next breakthrough in rail control system performance dök först upp på Prover - Engineering a Safer World.

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Demonstration of Prover Studio https://www.prover.com/webinar/demonstration-of-prover-studio/ Mon, 07 Jun 2021 14:15:32 +0000 https://stage.prover.com/?p=13867 This short video introduces Prover Studio, an integrated development environment for formal specifications.

Inlägget Demonstration of Prover Studio dök först upp på Prover - Engineering a Safer World.

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DEMO VIDEO

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A quick look into our integrated development environment

This short video introduces Prover Studio, an integrated development environment for formal specifications. With support for the HLL, LCF and PiSPEC languages, it helps you manage complex specifications in a more productive way, keeping track of all your files and requirements. Used together with Prover iLock, it gives you an agile and test-driven process for developing and maintaining Generic Applications.

Agenda:
  • Syntax highlighting
  • Identification of syntax and semantic errors
  • Quick navigation with ‘go to definition’ and ‘find references’

Yes please, send me the recording!

Hosts
Fei Niu Prover

Fei Niu
Senior Developer, Prover

Inlägget Demonstration of Prover Studio dök först upp på Prover - Engineering a Safer World.

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