Formal Methods articles

Software formal verification in the context of CENELEC EN 50716: from model to sign-off verification

Sara Månsson — Fri, 21 Nov 2025 14:28:09 +0000

RECORDED WEBINAR

Formal methods

As rail control software gets more complex, the real challenge is to ensure that implementations meet safety and functional requirements efficiently and convincingly. EN 50716 (the successor to EN 50128/EN 50657) sets the software development and verification expectations for railway applications, clarifying how formal methods and tool qualification contribute to a robust safety case. This session takes a practitioner’s view of applying that framework to real systems and on‑board functions.

We’ll walk through a repeatable assurance workflow: capture requirements as verifiable properties, model behavior in HLL and its sequential extension sHLL, use model checking to explore relevant executions, and establish auditable conformity between specification and implementation. We’ll illustrate the approach using Prover’s toolchain (HLL/sHLL, Prover PSL, Prover Certifier) to make the approach concrete, while maintaining the emphasis on methods, evidence, and governance that can be adopted in any environment.

Agenda:

EN 50716 in practice. What changed from EN 50128/EN 50657, specifically regarding the role of formal methods within the lifecycle, and what auditors expect in terms of tool classification and evidence.
A pragmatic formal‑assurance workflow. From property‑driven requirements to HLL/sHLL models, Prover PSL model‑checking, traceability, and preparation of sign‑off evidence.
Software conformity by proof. How proof‑producing sign‑off with a T2‑qualified engine (e.g., Prover Certifier) demonstrates alignment between specification and implementation for SIL‑classed applications.
Quality & efficiency gains. Ways to shorten verification cycles, raise coverage beyond testing, and catch defects earlier—without disrupting your current development process.

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Speakers

Benjamin Blanc
Solutions Manager at Prover

Inlägget Software formal verification in the context of CENELEC EN 50716: from model to sign-off verification dök först upp på Prover - Engineering a Safer World.

AI, a key enabler for Signaling Design Automation

Sara Månsson — Thu, 09 Oct 2025 15:02:07 +0000

RECORDED WEBINAR

Formal methods

Recorded on November 5, 2025

At Prover, we address the demand for cost-efficient rail control delivery through Signaling Design Automation (SDA), based on Formal Methods. SDA is also central to the Open Signaling Initiative, promoting open, modular, and interoperable signaling systems.

With recent advances in AI, we can make SDA more accessible to the rail control community. Adoption challenges often lie in digitizing data, managing requirements, and mastering the technologies. Our goal is to simplify SDA and Formal Methods. In this webinar, we demonstrate how AI enables us to achieve this.

Agenda:

The vision for how AI will change the development of safe rail control systems
Introduction to Signaling Design Automation and Formal Methods
AI use cases in Signaling Design Automation
Demonstration: Requirement engineering with AI in Prover Studio
Interactive Q&A with Prover’s AI and product experts

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Speakers

Gunnar Smith
Chief Product Officer at Prover

Fei Niu
AI Innovation Lead at Prover

Inlägget AI, a key enabler for Signaling Design Automation dök först upp på Prover - Engineering a Safer World.

How to deliver 100% safe and compliant rail control systems without time delay

Sara Månsson — Thu, 14 Aug 2025 12:54:21 +0000

RECORDED WEBINAR

Formal methods

Recorded on September 10, 2025

As digital rail control systems evolve, so do the challenges in validating their safety. Manual testing and reviews are no longer sufficient; they are expensive, slow, and can’t guarantee complete safety. Meanwhile, the cost of missed errors or late-stage discoveries can cause severe project delays or system failures.

Prover’s Formal Safety Verification solution enables a faster, more efficient, and more reliable way to ensure your system meets all safety requirements. It is mathematically proven, highly automated, and compliant with CENELEC standards.

Watch this webinar to learn how formal methods can help you avoid costly project delays, eliminate safety gaps, and streamline your certification process.

What you will learn:

Why traditional verification approaches struggle with today’s complex rail systems
What formal safety verification is and how it works
How to use model-based development and mathematical proof to ensure 100% requirement coverage
How Prover’s new solution enables early issue detection, automation, and certified safety evidence
Real-world results from leading metros, railways, and signaling suppliers.

Yes please, send me the recording!

Speakers

Gunnar Smith
Chief Product Officer at Prover

Daniel Fredholm
Senior Consultant at Prover

Inlägget How to deliver 100% safe and compliant rail control systems without time delay dök först upp på Prover - Engineering a Safer World.

Prover and Region Stockholm lead model-based approach for traffic control systems

Amanda Öberg — Thu, 03 Jul 2025 09:42:31 +0000

Sweden’s rail and transit systems are facing a generational shift in both technology and expertise. At the same time, the need for more robust, high-capacity, and modern infrastructure has never been greater. To address this, the Public Transport Administration in Region Stockholm and Prover are leading a joint initiative that aims to fundamentally change how traffic control is developed, introduced, and maintained.

Purpose and goals

The main objective of the initiative, called MBATS (Modellbaserat arbetssätt för trafikstyrning/Model-Based Approach for Traffic Control Systems), is to enable a step-by-step digital transformation of traffic control systems by introducing a cross-functional platform and working method based on modeling techniques.

By using digital twins and formal modeling techniques, the aim is to:

improve decision-making through system-wide simulations
increase control over cost, performance, and time during system updates
reduce reliance on outdated, text-based, and manual processes
create a long-term platform for safe and scalable innovation

The approach helps ensure changes can be introduced gradually, without disrupting existing infrastructure or operations. Simulations and scenario analysis enable testing of solutions before deployment, increasing confidence and lowering operational risks.

Long-term effects include:

reduced climate impact through the reuse of existing systems
increased rail network capacity by 20–30% through smarter analysis
shorter lead times and reduced lifecycle costs
enhanced collaboration across departments and suppliers
stronger strategic control of technical assets and investments

The initiative will integrate existing modules and services from the Public Transport Administration’s IT environment to ensure compatibility and scalability. The value to the organization is stipulated through automated services to different users. New work methods are introduced step by step, together with user involvement and recollection of the challenges it entails.

Solution

The core is the introduction of new work methods and a user-friendly environment that enables structured system design, simulation, validation, and continuous improvement throughout the lifecycle of traffic control systems.

The platform supports multiple use cases: from early-stage analysis and investment planning, to operations, maintenance, and future upgrades. A major advantage is the ability to test and verify changes digitally, before they impact passengers or operations.

Background and Prover’s contribution

The innovative builds on the earlier DigiTS project, where Prover and the Public Transport Administration mapped out 20 years of digital transformation across Stockholm’s rail and metro network. The MBATS project is part of the Smart Built Environment program and directly supports its mission to accelerate the digital transformation of the built environment, with a focus on sustainable and interoperable infrastructure.

Prover leads the MBATS project and contributes with its expertise in traffic control systems, digital twin technologies, and formal methods. Prover will deliver several advanced modeling tools and the digital twin platform that is used in the solution.

These tools enable formal, traceable, and repeatable modeling of rail systems. Prover’s solutions help ensure that new configurations meet functional and safety requirements before anything is deployed.

Conclusion

Now, MBATS will integrate digital twins, rail system models, and data flows into a unified digital platform. This model-based approach allows changes to be tested and analyzed before implementation, significantly reducing lead times, costs, and risks while improving safety and system robustness.

By introducing a structured, model-based methodology for traffic control, MBATS lays the groundwork for a smarter, more sustainable, and more reliable transportation infrastructure. The results of the project will be shared with national and international stakeholders and contribute to broader industry learning and innovation.

Timeline and partners

Project start: 2025
Duration: 2 years
Partners: Region Stockholm Public Transport Administration and Prover
Funding program: Smart Built Environment
Key goal: Get in control of existing and future traffic control system

Inlägget Prover and Region Stockholm lead model-based approach for traffic control systems dök först upp på Prover - Engineering a Safer World.

Succeed with migrations of old signaling and traffic management systems

Jesper Carlström — Wed, 29 Jan 2025 07:50:34 +0000

Ondemand WEBINAR

Formal methods

Recorded on February 19, 2025

Much of the traffic management and signaling solutions in the railway industry relies on old technology, and the need for modernization and digitalization is growing. Migrating these systems that are highly dependent on each other is not easy. Many have failed due to over-exceeding costs and time plans.

In this recorded webinar, we presented a solution for overcoming the challenges by modernizing rather than replacing existing solutions. It offers a more sustainable and cost-effective alternative. It allows you to unlock the benefits of a new system more quickly, with minimal disruption to daily operations. By choosing modernization, you can extend the lifespan of your infrastructure while leveraging the advantages of advanced technology sooner.

This approach, based on a structured, proven process in steps and the use of digital twins, is suitable for both rail and metro control systems.

Agenda:

The challenges with migrating old signaling and traffic management systems
Based on real-world migration projects and decades of experience, we will present:

A proven process of migration based on modernization

A modern architecture for signaling and traffic management solutions

A complete traffic management solution

How to migrate a signaling solution based on digital twins and formal methods
Interactive Q&A with experts from Prover and Cactus Rail

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Speakers

Jesper Carlström
Chief Product Officer at Prover

Fredrik Bergström
CEO at Cactus Rail

Inlägget Succeed with migrations of old signaling and traffic management systems dök först upp på Prover - Engineering a Safer World.

Reachability analysis as a way to validate requirements and constraints

Jesper Carlström — Tue, 21 Jan 2025 08:07:16 +0000

A part of a modern standard methodology for formal verification is to do reachability analysis. At Prover, we use it as an integrated part of our process to validate requirements and constraints, as well as to find design flaws.

While proof obligations are properties that must always be true (which you are required to prove), reachability obligations are properties that should be possible to make true. They state that there must be a way for the system under investigation to reach a state where they are true. There must not necessarily be one state that makes them all true at the same time (which might be impossible), but rather one scenario per reachability obligation.

Constraints for proof obligations and reachability obligations

For train and metro signaling systems, safety is very important, so proof obligations have been used for a long time: state your safety properties as proof obligations and let a model checker prove that they are always fulfilled by the signaling system. For example, a safety property might say that whenever a train receives permission to proceed (e.g. by a signal or a radio message), all switches in the route must be locked in the correct position. Formal verification is sometimes performed based on assumptions about the environment. Perhaps about the behavior of trains, or about physical equipment such as relays. Such assumptions are added as constraints in the verification model. The name constraints comes from the fact that they can be viewed as constraining the search space for possible states of the system.

Correctly used, constraints exclude non-realistic scenarios and let the model checker focus on realistic scenarios only. A mistake in the formulation of constraints can, however, have very severe consequences. If too many scenarios are excluded, then they will be ignored by the model checker, even if they can happen and would be dangerous.

For reachability analysis, constraints contribute by restricting how reachability obligations are allowed to be reached. The scenario produced will fulfill the constraints. If this is not possible, the reachability obligation is called unreachable.

Ways to find erroneous constraints

There are a number of techniques that help identify erroneous constraints.

Bounded Consistency Checks. This technique, usually abbreviated BCC, has been available for a long time. It checks that constraints are consistent, meaning that they are not mutually contradictory. If they are inconsistent, they exclude all possible scenarios, including the realistic ones, which can be dangerous. Though useful, this check is also very coarse. It will only discover if all possible scenarios are excluded, not if some realistic scenarios are excluded. In other words, if the check shows that you have an inconsistency, you need to fix it, but if not, you have no guarantee that your constraints are correct. Realistic scenarios could still be excluded in the analysis.
Reachability analysis. You state Reachability Obligations (ROs) that express states that the system shall be able to enter. For example, you can state that every route can be used. The model checker will then try, for each route, to find a scenario that leads to the route being used. If that succeeds, it means that these scenarios can occur, but also that they are not excluded by any constraints. In particular, it follows that the constraints are consistent, so this check says more than BCC. Much more, in fact, because it does not only tell you that there is at least one scenario that is permitted by the constraints, it also tells you that there are scenarios that reach specific, realistic, intended states. You can use these reachability obligations as regression tests, that will tell you immediately if you happen to introduce a constraint that excludes scenarios where routes are used. You can also add reachability obligations for equipment that you know can fail, and where you want to watch that no constraint is introduced that will exclude such failure from the model. For example, you can express “the lamp is turned on by the signaling system but does not acknowledge back that it works” or “the switch is being thrown to the left but not detected in the left position”. If these scenarios are not reachable, it is a clear sign that some constraints are unrealistically strict.
Proof coverage. If a proof obligation is surprisingly shown Valid, or a reachability obligation surprisingly turns out to be Unreachable, it can be a sign of the presence of an erroneous constraint or a bad definition. This situation is difficult to debug, because a model checker cannot provide any counterexample to inspect in this case. It can, however, tell you which parts of the model were used when arriving at the conclusion. By inspecting them you might be able to understand where the conclusion came from.
Constraints softening. Another technique available to analyze surprisingly Valid proof obligations or Unreachable reachability obligations is to soften constraints. By pointing out constraints that you want to soften, you instruct your model checker to interpret them as soft constraints. Soft constraints are assumptions that are not forced but are rather used as preferences when selecting models. They do not affect truth values of proof obligations and reachability obligations (as proper constraints do); they only contribute to the selection of which scenario to produce as a counterexample for a proof obligation, or as an example for a reachability obligation. We think of them as noise reduction, because they can make models considerably less chaotic, and thus eliminate “noise” that distracts from the important aspects of the model. The model checker will produce a scenario where the soft constraints are fulfilled as much as possible. You can then ask for information about which soft constraints were violated in the model produced. If these violated soft constraints are softened proper constraints, then it’s a good idea to review them thoroughly, because they might be stronger than you intended.

Ways to validate requirements at early stages

Reachability analysis can also be used to validate requirements. In railway signaling projects, an ontology (for example an object model) and safety requirements are usually produced during the tender phase, while the design is made much later. Mistakes in the safety requirements, such as them being mutually contradictory, are important to find early, in order not to base development on faulty requirements that will have to be changed later, causing large extra costs. Tool support for finding such mistakes is crucial.

One option is to model all variables of the ontology (object model) as free variables, and to implement the safety requirements as proof obligations. They cannot be proved until a design is available, and any attempt at proving them will fail with a counterexample, because all design variables are free. What you can do, however, is to assume those proof obligations, that is, using them as constraints, and try to prove some reachability obligations, for example that all signals can clear (not at the same time, but individually). If the requirements are not mutually contradictory, and do not forbid signal clearing directly or indirectly, you will, for each signal, receive a scenario where the signal clears in the end, and where all safety properties are fulfilled in every step. This shows that the requirements can indeed be fulfilled. In fact, by inspecting the scenarios that lead to signals clearing, you should be able to find all steps that you expect to be necessary for the signal to clear, such as switches being thrown to correct positions, objects being locked, and so on. If those steps are not there to find, it might be a sign that some requirements about the missing steps are lacking.

How to do reachability analysis

There are two possible ways to do reachability analysis.

Any model checker can be used, if you are prepared to use a workaround. Say that you want to show that a system can reach a state where a lamp becomes lit. You formalize the opposite proof obligation “the lamp cannot be lit” and ask the model checker for a counterexample. If it finds a counterexample, you have proof that it is possible to find a state where the lamp is lit. This approach works but has three drawbacks.
- It requires a workaround. This is less intuitive and more error prone.
- It does not handle the potential issue of undefined values. For example, if you try to show that “1/x = 2” can occur for some integer x, you need to search for a counterexample to “1/x ≠ 2”. Actually, for x = 0 this statement is not even well defined, so x = 0 works as a counterexample. But this example does not work well as a reachability proof, because “1/0 = 2” is not true.
- The interpretation of the results is error prone. You need to remember that some proof obligations are supposed to be falsifiable, and that it is an error if they are valid, because they are formulated as opposite to what you really want to see. This can be confusing and lead to erroneous conclusions.
With a dedicated add-on for reachability analysis. The advantages of this approach are:

- Proof obligations and reachability obligations can be expressed simultaneously and proved simultaneously in one run.
- Having reachability obligations explicitly mentioned in input files makes them useful as a way to discover if the system becomes over-constrained by mistake (regression testing).
- Reachability obligations that can be undefined (involving partial functions such as division or arrays that can be out of bounds) are handled differently than with the workaround: reachability means that there is a scenario where these are both well-defined and true in the last state.

Prover PSL 6 comes with an add-on for reachability analysis. It also comes with proof coverage inspection and constraints softening, which were other features mentioned above.

The add-on for reachability analysis is provided for free to our Prover PSL users until March 2025. Try it today and share your feedback!

Inlägget Reachability analysis as a way to validate requirements and constraints dök först upp på Prover - Engineering a Safer World.

AI-driven railway signaling is now available via Prover Labs

Fei Niu — Mon, 02 Dec 2024 07:00:55 +0000

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.

How AI can take automation and safety to a new level in railway signaling

Fei Niu — Fri, 25 Oct 2024 09:43:03 +0000

RECORDED WEBINAR

Formal methods

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
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.

Prover and RATP strengthen collaboration

Benjamin Blanc — Fri, 04 Oct 2024 07:06:46 +0000

Following the joint presentation of a paper at the RSSRail 2023 conference, RATP and Prover have deepened their collaboration to perfect and industrialize the Integral Formal Proof (PFI) methodology. With the recent commissioning of the CBTC update on line 4 of the Paris metro, this approach has led to significant progress in system-level analysis, thereby enhancing passenger safety.

Prover’s expertise in formal methods, particularly through the use of the HLL (High Level Language) and its dedicated tools, combined with the constant commitment of the teams, has led to significant advancements. Our team has thus contributed to the thorough assessment of the tracking function of this CBTC, together with experts from Sig4 Engineering. As a tool provider, expertise provider, and long-time partner of RATP, Prover stands out for its leading position in the field of critical systems analysis, thus ensuring reliable and efficient solutions for transport infrastructures.

Inlägget Prover and RATP strengthen collaboration dök först upp på Prover - Engineering a Safer World.

Prover takes railway Signaling Design Automation to a new level with the launch of Prover Station

Jesper Carlström — Tue, 24 Sep 2024 07:00:42 +0000

Prover is dedicated to Engineering a Safer World, focusing on providing railway Signaling Design Automation (SDA) solutions that leverage digital twins and formal methods. With the launch of Prover Station as our next-generation platform, we meet the complex demands of design, verification, and validation (V&V) of rail and metro signaling systems.

The industry needs to be modernized, standardized, and digitalized. The current processes for developing and maintaining signaling solutions are slow and inefficient. Prover’s approach to SDA, based on digital twins and formal methods, takes automation and efficiency to a new level.

Our new web-based platform Prover Station focuses on critical parts of the design and V&V process, aiding them with digital twins for improved understanding and testing during the development, as well as for training support after delivery. Our extensive experience of formal verification led us to design a workflow for proof projects that will help you succeed in less time and with greater confidence. Progress and obstacles are presented clearly to aid the verification and validation work. We made it easier for non-experts to work with signaling design and for experts to become more efficient and communicate their insights. The result will significantly improve the overall SDA process.

Prover Station is currently available for on-premises installation and in a cloud edition.

Prover Station is a strategic platform that we continue to develop rapidly, aiming to cover the complete process. With capabilities for digital twins and proof projects in place, we now continue to develop what we find most important for efficient SDA projects, such as:

Creation of digital twin models
Extensive APIs for integration and automation of tasks
Third-party contributions that connect Prover Station to other tools
Data preparation capabilities

“We have leveraged our decades of expertise in Signaling Design Automation (SDA) based on formal methods to build a modern platform that will transform the industry,” says Jesper Carlström, Chief Product Officer at Prover.

Digital twins are a critical success factor for managing signaling design

In the digital transformation era, the ability to create and manage digital twins is becoming increasingly important for organizations in the railway industry. They are now used in model-based tendering, planning phases, design debugging, verification and validation, and post-delivery training.

Working practically with digital twins is a challenge, specifically if large complex systems need to be managed, like a complete railway line.

“The industry needs to be transformed through standardization, digitalization, and interoperability. Digital twins are a critical enabler of this transformation. I’m looking forward to an industry that finally can implement digital twins in real work, developing the signaling solution of the future, which will now be easier with the launch of Prover Station.” Says Anders Lindén, CEO Prover.

Prover Station provides state-of-the-art capabilities for setting up and configuring digital twins. Digital twins allow engineers and operators to understand what they have, to understand what they need to do, and to understand what the results became. You can monitor, analyze, and optimize system performance based on accurate models. By using digital twins, organizations can identify potential issues before they occur, reducing downtime and improving overall system efficiency.

One of Prover Station’s key strengths is its ability to connect multiple digital twins into a single, cohesive aggregate model that provides a holistic view of the entire signaling system, allowing for more effective decision making and system management. The aggregation process and configuration require no user programming.

The use of digital twins in Prover Station is not limited to monitoring and optimization, but also plays a crucial role in system design, testing, and maintenance. Engineers can use these models to try various ideas, helping to ensure that the final system design is robust and capable of meeting all operational requirements. By incorporating digital twin technology into the design process, Prover Station helps organizations achieve greater flexibility, scalability, and resilience in their signaling systems.

Formal methods are the standard methodology for signaling design

Formal methods are becoming more and more popular as the method to ensure safety in the railway industry. In particular, formal verification: you prove using automated mathematical logic reasoning that important safety properties are fulfilled by your system. To work effectively with such proof projects, you need tools that execute proofs and keep track of progress and results.

Prover Station helps you organize your formal verification work in Proof Projects. They allow you to have an overview of the ongoing work, follow progress, understand dependencies and report results. The workflow has been designed based on Prover’s comprehensive experience with success factors for verification and validation work. Engineers who are new to formal verification will appreciate the help to select verification strategies and understand what to do next, while senior verification engineers will discover that it has become easier to communicate ideas to colleagues.

Prover Station supports the entire proof lifecycle, from initial specification and modeling to final proof generation and validation. Prover Station’s capabilities include proving properties and showing why requirements are not fulfilled. This ensures that all logical errors are identified and rectified early in the design process, thereby reducing the risk of costly errors in later stages. Graphical illustrations provide overviews of status and aid in understanding details.

The formal verification process in Prover Station is highly customizable, allowing users to define and enforce their specific safety and performance criteria. The software’s powerful algorithms can handle the most complex signaling logic, ensuring that even the most intricate systems can be verified with confidence. By integrating formal verification into the design process, Prover Station helps organizations meet stringent regulatory standards and enhance their signaling systems’ overall safety and reliability.

Prover Station easily integrates into existing IT Environments

Prover Station can be used in the cloud but is also offered as on-premises software. We continue to expand its capabilities, and at every step, we ensure that it can be used via the web interface and in automated tasks using our APIs. External simulators can be connected via the MQTT protocol or OPC-UA, as can hardware for hardware-in-the-loop testing. If your software or hardware does not support these protocols, it is usually straightforward to develop the necessary adapters.

Conclusion

Prover Station is a comprehensive platform designed to meet the most demanding requirements of the railway and metro signaling industry. With its advanced capabilities in formal verification and digital twin technology, Prover Station empowers organizations to achieve higher levels of safety and reliability in their signaling systems, while also shortening development time. Whether you are involved in proof projects, system design, or ongoing system management, Prover Station provides the tools needed to succeed.

Ready to revolutionize your railway signaling systems?

Experience the power of Prover Station with a personalized demo. Read more about Prover Station here.

Request a demo

Inlägget Prover takes railway Signaling Design Automation to a new level with the launch of Prover Station dök först upp på Prover - Engineering a Safer World.