Advanced driver assistance systems bring increasingly sophisticated software into vehicles. Functions such as lane keeping, adaptive cruise control, automated emergency braking and sensor fusion rely on complex algorithms operating under tight real-time constraints. Consequently, automotive development organizations invest substantial effort to ensure that application software and hardware platforms comply with functional safety standards such as ISO 26262.

Yet one critical layer of the software stack often receives far less attention: the compilers and libraries that silently transform our software code into reliable, high-performance executable behavior. These tools operate largely out of sight, but they play a decisive role in determining how ADAS software performs on the road. In fact, no ADAS function can exist without them.

The silent force behind ADAS software 

Compilers translate high-level source code into machine instructions, while standard libraries provide essential functionality for numerical computation, data handling and timing. Together, they form the foundation on which application software is built, and their correctness is often taken for granted throughout the development lifecycle.

In practice, this assumption can be risky. Even when application code complies with coding guidelines and the target hardware is safety-certified, deficiencies in the toolchain can still undermine system behavior. These issues typically do not originate in the application logic itself, but in lower layers that are difficult to observe directly.

Unseen risks in the toolchain 

Compiler optimization is a prominent example. Optimization is essential for meeting performance and power consumption requirements in automotive systems, but it also introduces significant complexity. Changes in optimization paths can alter control flow, numerical precision or timing in ways that are not apparent from source code inspection. As a result, a compiler update or a change in optimization options may introduce new behaviors (and even errors) while the application code remains unchanged.

Standard libraries present similar risks. Library functions are widely assumed to be robust and well tested, yet they are subject to implementation choices and corner cases like any other software. In ADAS, where libraries are statically or tightly linked into the final executable, subtle deviations from expected behavior can propagate directly into system-level effects.

Because these problems originate below the application layer, they are not easily detected using conventional testing approaches. Integration testing may expose symptoms, but it rarely provides systematic coverage of toolchain behavior. As a result, determining whether the root cause lies in the application logic, the compiler or a library implementation can require extensive investigation. When such issues surface late in development, the associated cost and disruption can be substantial. Even more concerning, if they remain undetected until deployment, they may manifest as safety-critical failures, with potentially severe consequences.

Ensuring reliability through verification 

Managing these risks requires systematic verification of compilers and libraries. This involves demonstrating conformance to relevant programming language standards and consistent behavior across configurations, optimization levels and target platforms.

Structured test suites are central to this effort. By exercising both front-end language features and back-end optimization paths, they can reveal defects that would otherwise remain latent. Problems are not confined to parsing or semantic analysis; changes deep within the optimization pipeline or the code generator can also introduce unintended effects. Comprehensive testing helps surface these issues early, before they impact product development.

Supporting qualification and long-term confidence 

Beyond identifying defects, verification provides the objective evidence needed to support tool qualification. The ISO 26262 safety standard for automotive software requires confidence in the correct operation of the compiler and evidence that the standard library meets the requirements on which the application relies. By testing the compiler against the programming language specification, it is possible to verify that source code is translated into machine code in a well-defined and predictable manner. Likewise, requirements-based testing of the standard library demonstrates that its functionality conforms to the requirements relied upon by the software. Together, these activities provide objective evidence that the generated object code faithfully represents the source code.

Furthermore, the goal of qualification is not to prove that the compiler or library is completely free of errors – an unrealistic expectation for any complex software tool. Instead, the objective is to understand their limitations and known deviations, and to manage them in a controlled way so that they do not compromise functional safety. This understanding is captured in a safety manual that complements the verification results and defines the constraints, assumptions and usage rules for the compiler and library, ensuring they can be applied safely within the software development process.

This approach also supports long-term maintainability. Automotive platforms often have lifecycles spanning decades, during which compilers and libraries inevitably evolve. Systematic validation of updates enables controlled transitions without the need to re-qualify entire systems from scratch.

This benefit has been demonstrated in practice, where early identification of compiler issues has significantly reduced the effort associated with toolchain updates. By understanding tool behavior upfront, organizations can make informed decisions about changes and avoid costly surprises later.

Standard libraries follow similar principles. When assessing a new library implementation, behavioral comparison against a known baseline helps ensure that replacements do not introduce unintended or system-relevant changes.

The role of collaboration 

ADAS development involves many stakeholders: software engineers, functional safety engineers, validation teams and certification bodies. Ensuring that compilers and libraries behave as expected requires collaboration across all these roles.

Verification cannot be treated as an isolated activity, it must be an integral part of a broader safety strategy. When toolchain behavior is well understood and documented, communication between teams becomes clearer, assumptions are explicit and decisions can be justified with evidence rather than intuition.

Strong partnerships between tool providers, system integrators and safety experts further support this process. By sharing knowledge and aligning on verification practices, organizations can reduce duplication of effort and improve overall confidence in their development environment.

Conclusion 

Compilers and libraries may operate invisibly, but their impact on ADAS safety is substantial. Treating them as implicit assumptions rather than explicit verification targets introduces avoidable risk. As ADAS functionality grows increasingly complex, this risk will only increase.

By recognizing the role of the toolchain early and subjecting it to the same rigor as application software, automotive developers can build a stronger foundation for safety. Verification and qualification of compilers and libraries are not optional extras; they are essential steps to ensure that software behaves as intended – from code to road.

A feature in the next issue of Automotive Testing Technology International will investigate the challenges of continuous testing for software, the stages of a typical DevOps pipeline, how companies are measuring software readiness, and more. Read the March 2026 edition here. 

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The hub is expected to employ around 200 technical specialists that will be responsible for enhancing the MyToyota and LexusLink+ applications, which are used by more than two million European customers to access remote vehicle functions, battery‑charge monitoring and a wide range of ownership‑convenience services. The team will also contribute to the development of the cloud infrastructure that enables connected services for Toyota and Lexus vehicles in Europe, while additionally providing cybersecurity support.

“Toyota has been present in Poland for 35 years and this project strengthens Toyota’s commitment in this market,” said Thierry Boitel, vice president of research and development, Toyota Motor Europe. “Poland was selected as the location for the Toyota Digital Hub due to its strong pool of highly skilled specialists and the presence of leading technical universities.”

The establishment of the Toyota Digital Hub marks a further investment by Toyota in Wrocław. Since 2015, the Shared Services Centre has operated in the city, providing accounting and tax advisory services for all Toyota units in Europe.

“The Toyota Digital Hub is set to play an important role in Europe by expanding Toyota’s software development capabilities and positioning Toyota Motor Europe as a trusted partner in future global software‑defined vehicle development,” said Luis Lopes, vice president information and technology, Toyota Motor Europe.

“The decision to locate Toyota Digital Hub in Poland confirms our country’s strong investment appeal and its solid position as a hub for modern technologies,” said Andrzej Domański, Polish minister of finance and economy.

Recent news, Pony AI launches self-improving physical AI engine PonyWorld 2.0

During the pilot, ALLO demonstrated its ability to improve inbound logistics planning performance across TME’s complex European logistics network, reduce planning cycle times and support TME’s carbon neutrality objectives.

“The results of our pilot with Agillence have exceeded expectations,” said Jean-Christophe Deville, vice president of supply chain at TME. “ALLO has proven to be a strategic asset in our pursuit of a sustainable, cost-efficient logistics network across Europe. We are confident that this long-term partnership will continue to drive meaningful progress toward our carbon neutrality goals while delivering real operational value.”

“We are pleased to formalize our long-term relationship with Toyota Motor Europe,” said Srini Paruchuri, vice president of customer strategy and solutions at Agillence. “This partnership is a testament to the power of ALLO in addressing the unique logistics complexities of large-scale European automotive operations. We look forward to deepening our collaboration with TME as they continue to lead the industry in sustainable supply chain practices.”

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To address this, Pony.ai has launched PonyWorld 2.0, the latest upgrade to its proprietary world model and a major advancement in the core training system behind its autonomous driving stack. The key advancement is its ability to identify its own weaknesses and guide targeted improvements. The upgrade introduces three core capabilities: self-diagnosis, targeted data collection in areas where performance is still limited and more efficient training focused on the most challenging cases.

Since 2020, Pony.ai has developed PonyWorld not as a basic synthetic data simulation tool, but as a full reinforcement learning system spanning cloud-based training and in-vehicle deployment. As the system has matured, improving the Virtual Driver has increasingly depended on enhancing the world model that trains it, particularly its ability to accurately represent real-world dynamics and interactions.

“PonyWorld 2.0 is an important step toward a more self-improving approach to autonomous driving development,” said Dr Tiancheng Lou, founder and CTO of Pony.ai. “As AI systems become more capable, they can play a larger role not only in learning to drive, but also in guiding their own improvement – making L4 development more scalable over time.”

PonyWorld 2.0 is already being applied across Pony.ai’s L4 driverless fleet and R&D system.

After validating robotaxi unit economics in two major Chinese cities with its seventh-generation fleet, the company is accelerating commercialization across China and international markets, targeting over 3,000 vehicles and 20 cities globally by year-end, nearly half overseas.

New training for scalable autonomy

As driverless operations grow from hundreds of vehicles to thousands and beyond, it becomes both harder and more important to keep improving safety and performance without regression.

Pony.ai defines a true world model as more than a tool for generating virtual scenarios. It must establish what good driving means, accurately model the physical world and replicate realistic interactions between the AI driver and other road users across both edge cases and normal traffic.

PonyWorld 2.0 is designed to make that process more efficient. A structured intention layer enables the model to form an internal representation of why it made a decision, making large-scale self-diagnosis possible. The system can review its own decisions, compare intent with outcomes and identify the types of scenarios where additional learning is needed. It can then generate targeted data-collection tasks for human teams, which gather the relevant real-world samples, feed them back into the cloud, and help recalibrate the world model for more precise training.

In the early stages of autonomous driving, progress depended heavily on human engineers to design rules, label data and decide what to train next. PonyWorld 2.0 points to a different model. As AI systems become more capable, they can take over more of their own improvement cycle, while human engineers increasingly serve as operators of a directed data-collection loop shaped by the system’s own learning needs.

Pony.ai believes PonyWorld 2.0’s approach, combining high-accuracy world modeling, self-diagnosis and targeted improvement, could apply to broader physical AI systems that must learn safely and efficiently in real-world environments, extending beyond autonomous driving.

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To address this, OmniTrust and Synopsys have partnered to help development teams identify and fix critical security issues earlier in the embedded systems lifecycle by enabling secure boot validation within virtual ECU (vECU) environments.

The collaboration aims to close this gap between software and hardware by bringing security validation into the early stages of development. By combining Synopsys’s vECU solutions with OmniTrust’s embedded trust capabilities, teams can run production firmware in virtual environments and validate both expected and tampered scenarios long before hardware is ready.

This approach enables developers to observe and test secure boot behavior as part of standard software workflows, including automated regression testing. As a result, teams can detect vulnerabilities sooner, reduce integration risks and accelerate time to market.

OmniTrust provides core embedded security capabilities such as secure boot policy validation, firmware signature verification and cryptographic trust anchor management. When integrated into Synopsys’s virtual development environments, these capabilities enable developers to treat security enforcement as a continuous, testable part of modern software development.

“As automotive architectures become increasingly software-defined, automotive companies must integrate security validation as part of their software development,” said Marc Serughetti, vice president, product management and markets group, Synopsys. “Our collaboration with OmniTrust enables development teams to leverage electronics digital twins and integrate trust validation into their software development before hardware is available. Our joint customers can accelerate time to market and innovation with greater confidence in the security of their systems.”

“Secure boot and firmware authenticity are essential for system integrity, yet often checked later in development,” said Albert Rooyakkers, SVP of business development at OmniTrust. “Synopsys virtual ECUs enable early security validation, automated testing and authenticated software deployment in production.”

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Automotive development is evolving as software-defined vehicle programs introduce faster feature cycles and more complex system interactions while meeting strict requirements for safety, reliability and long-term maintainability. Gen AI is now part of engineering workflows. It can help increase development speed, but its non-deterministic behavior, lack of physics awareness and limited traceability make it difficult to apply directly to safety-critical systems. These characteristics make verification, certification and traceability challenging when outputs generated by Gen AI are introduced without constraints.

Model-based design addresses these issues through deterministic execution, executable specifications and physics-based simulation. MathWorks is bringing these strengths together by integrating Gen AI assistance directly into model-based design tooling, enabling engineers to benefit from accelerated workflows while preserving the rigor required for long-term reliability and certification of automotive software.

Simulation as the foundation of trust

Simulation is the foundation of trust in engineering workflows assisted by Gen AI. It provides a controlled environment where system behavior can be verified early and repeatedly. Model‑based design enables closed‑loop simulation within continuous development pipelines, enabling Gen AI‑assisted artifacts to be validated continuously in virtual environments long before software reaches hardware. Closed-loop simulation uncovers defects that emerge only from real‑time interaction between software, hardware and physical dynamics, such as instability, timing issues, saturation and integration errors. Unlike regular software tests that validate code logic in isolation, simulation validates system behavior against requirements under realistic operating conditions, catching safety‑ and performance‑critical issues much earlier.

In leading organizations, ‘shift left’ is not a one-time activity; virtual verification is embedded directly into continuous integration/continuous development (CI/CD) pipelines. Every change triggers automated builds and simulation runs, exercising models against representative scenarios and acceptance criteria. Verification becomes continuous, not episodic.

Scalable development for evolving E/E architectures

Automotive E/E architectures are transitioning from ECU-centric networks to zonal and centralized computing platforms. Software is no longer bound to specific hardware configurations but must now operate reliably across heterogeneous compute targets while remaining portable and scalable, from small controllers to high-performance vehicle computers.

Model-based design supports this requirement by separating system behavior and software intent from hardware implementation. Engineers develop executable models that serve as stable sources of truth. The models can generate production-ready code for a wide range of processors and operating systems, including platforms incorporating AI inference engines and hardware accelerators such as GPUs, DSPs and NPUs. This approach enables the development and validation of AI-enabled functions (e.g. virtual sensors) at the system level, reduces the need to reengineer algorithms for each target, and improves efficiency and consistency across platforms.

Improving collaboration through model-based design

Engineering organizations must transform their collaboration models to keep pace with increased complexity. Integrating simulation, virtualization and automated verification directly into CI/CD workflows supports rapid iteration across software, AI models and hardware acceleration strategies. This model-centric approach helps organizations operate more quickly while preserving robustness, safety and long-term maintainability in the era of software-defined and AI-driven vehicles.

Integrating AI into deterministic engineering workflows

AI is most effective in automotive development when embedded within a deterministic modeling framework. Within model-based design tools, GenAI-generated content is automatically tied to established interfaces, data definitions and architectural constraints. Model Context Protocol (MCP) capabilities empower engineers with AI assistance while preserving the rigor, repeatability and certification readiness.

Long-term maintainability and certification readiness require deterministic behavior, transparent audit trails and verification evidence that accumulates throughout the lifecycle. Model-based design naturally supports these goals by linking requirements, models, test suites and generated code. Continuous simulation produces verification data throughout development rather than only at the end of a program. When artifacts generated by Gen AI follow the same workflows, they inherit this structure. This ensures that productivity gains do not come at the cost of safety, quality or compliance, and that Gen AI can be adopted at scale.

Conclusion

Gen AI and model-based design offer a structured path to accelerate automotive software development while maintaining trust, safety and engineering rigor. Model-based design provides determinism, physics-based validation and traceability. Gen AI adds efficiency and supports faster iteration when integrated within these boundaries.

This combination enables earlier insight into system behavior and deployment across diverse hardware architectures. The model-centric approach ensures consistent collaboration across engineering teams, and promotes reuse and consistency across global programs. Gen AI-enabled model-based design provides a scalable and reliable foundation for developing robust and certifiable automotive systems.

In the September 2024 edition of ATTI, Secondmind’s chief product officer, Morgan Jenkins, discusses the power and limitations of AI

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Oliver Blume, CEO of the Volkswagen Group, said, “We are accelerating toward the future. With the successful completion of the winter tests, our joint venture once again demonstrates the speed and precision of its work. The close integration between the joint venture, our brands and the group follows a clear objective: to excite people with products and technologies that set new standards. This is how we are driving development forward across the Volkswagen Group – with the ambition to become the global automotive tech driver.”

Winter endurance testing

The program consisted of two phases. In Arizona, engineering teams from the brands and the joint venture worked together to finalize key software functions and prepare the reference vehicles for the winter tests in Europe. In Sweden, the systems were then subjected to stress testing under extreme weather conditions with snow and ice. The teams examined, among other things, the interaction between hardware and software for functions such as all-wheel drive, traction control and driving performance. Over-the-air (OTA) functionality was also validated. In total, the joint venture and brands conducted hundreds of tests and validation cycles. Individual approval drives in Germany and Sweden, led by the brands’ development teams, marked the completion of the winter testing program.

The results show the SDV architecture operates reliably in harsh winter and dynamic driving conditions. This milestone also lays the foundation for the next development phases across the joint venture and individual group brands. The Volkswagen Group will deploy this SDV architecture in electric vehicles across Western Hemisphere markets, delivering highly automated driving features and advanced infotainment, with continuous updates delivered over the air.

Qualification program

In parallel, the Volkswagen Group brands are strengthening their software capabilities for the SDV. Volkswagen Passenger Cars is expected to rapidly launch a long‑planned qualification program at the beginning of May where software specialists will spend several months at RV Tech locations, including Palo Alto, California, to deepen their knowledge of the joint venture’s architecture and code. Upon returning to Wolfsburg, these specialists will serve as internal experts and bring this expertise back into their development departments as multipliers. This will help to integrate brand-specific functions more quickly into future production models. Audi and Porsche are also preparing to launch similar training programs.

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The agenda covered simulation, data management, diagnostics and test automation, with discussions underscoring interoperability, simulation credibility and data‑centric development. These themes emerged across sessions on ASAM standards OpenDrive, OpenScenario, OSI, ODS, SOVD, XIL, OTX, CMP, and digital‑twin integration through the Asset Administration Shell (AAS).

Cross-standard harmonization and the need to align toolchains across domains, suppliers and development stages remain priorities, with speakers citing inconsistent interpretations of ASAM specifications as a cause of fragmentation. Updates included co‑simulation workflows, higher‑fidelity OpenX modeling, interoperable data management, modular measurement architectures, digital‑twin concepts, ODD taxonomies and simulation‑quality assessment.

Regional updates and roadmap

Updates from ASAM’s ambassadors in China, Korea, Japan and the USA were shared by CATARC technical director Bolin Zhou, IVH CEO Daeoh Kang and ASAM Japan representative Yoshiaki Shoi. Priorities included co-simulation and merging standards in China; sensor simulation and ASAM SOVD in Japan; and study groups in South Korea supporting global application of standards, such as ASAM OpenDrive.

BMW IT specialist Michael Schwarzbach outlined the 2026 Technical Steering Committee roadmap, highlighting ODD-based testing, collaboration improvements, harmonized standards and a common ontology.

OpenX updates

ASAM technology managers Ahmed Sadek and Yash Shah (below) presented ASAM OpenX updates. Previous OpenX models defined traffic participants independently, creating toolchain inconsistencies. New concepts for ASAM’s simulation standards include combining ASAM OpenDrive with the Quantifying Simulation Quality (QSQ) initiative, and ASAM OSI adding high-fidelity sensor simulation support, such as spectral irradiance and radar waveforms. “No standard is developed in a silo,” Shah said. “We think feature-based, then collect standards experts for harmonization.”

Simulation integration

Clemens Linnhoff, founder and CTO at Persival, demonstrated co-simulation between Scenario Player and Sensor Model, with all assets linked in ASAM OpenScenario as a single source of truth.

ASAM ODS, MDF, CMP and digital twins

Technica Engineering technical fellow and head of media relations Lars Völker outlined Capture Module Protocol (ASAM CMP) improvements. “Before 2022, in‑vehicle DAQ was vendor‑specific and non‑modular. CMP 1.0 introduced modular DAQ and support for heterogeneous technologies. New use cases support raw Ethernet and define message transport across the vehicle system. Scaling HIL and test setups enables an elastic measurement infrastructure.”

Using slides from the Industrial Digital Twin Association (IDTA), Stefan Romainczyk, senior product manager at Peak Solution, said, “Today’s digital twins are proprietary and one lifecycle element, whereas future twins have a complete lifecycle with efficient scaling. With ASAM ODS, AAS can create a comprehensive data profile for digital twins – standardized interoperability improves predictive maintenance, time and cost.”

SDV diagnostics updates

Following the launch of ASAM SOVD 1.2 in February with 29 global OEMs and suppliers, Vector Informatik manager Tobias Weidmann presented at the seminar the latest activities in the development of the standard. ISO 17978-4 Remote Access covers how to access vehicle information via authorization and defining the access path; possibilities include functional communication, new access methods to log information via a streaming interface, and large file handling via third-party service providers.

ODD taxonomy and AV deployment

Andreas Richter (below), engineering program manager – Operational Design Domains, Volkswagen Commercial Vehicles, outlined ASAM OpenODD implementation within MOIA America, VW’s autonomous‑mobility affiliate, formerly known as ADMT. VW Commercial Vehicles is the first group brand to introduce SAE Level 4 autonomous driving using the ID Buzz platform with integrated third‑party automated‑driving systems. Testing is underway in Hamburg and Munich in Germany, Oslo in Norway, and in Austin, Texas.

Richter noted that the industry is not always clear on ‘ODD’, ‘taxonomy’, ‘service area’ and ‘scenarios’. “To bring autonomous driving to life, we have to agree on the same terms,” he said, calling for ODD definitions that are unambiguously readable by humans and machines, supported by geodata analysis and enterprise‑ready tools for ODD and scenario management.

“ASAM OpenODD offers a taxonomy-agnostic, modular model to represent ODDs in different technical formats, [and] support development, storage and processing in a machine and human-readable way,” he explained. “Originally intended for scenario-based testing, ODD definition is now required in more process steps for developing, testing, approving and operating ADS. The ODD as a single point of knowledge ensures OEMs and authorities share definitions.”

VW’s internal elaborated ODD taxonomy demonstrates how modular definitions support understanding across organizations and regulators. The ODD management toolchain validates taxonomies and module definitions, supports multilingual concepts and connects to scenario‑creation and requirements‑management workflows. Using STIEF (scenario-accompanied, text-base, iterative evaluation of automated driving functions), engineers can preload scenario definitions via natural‑language inputs, while geodata analysis identifies new operational areas and generates challenging test routes.

Simulation credibility

In an ASAM QSQ update, Automotive Artificial Intelligence’s general manager Basit Khan highlighted the challenge of trusting simulation for virtual homologation.

“No standardized quality metrics exist for simulation frameworks, especially ADAS/AD sensors,” he said. “Many contributors – OEMs, suppliers, research and tool vendors – have different priorities and vocabulary. Through working groups spanning use cases, camera, lidar, radar and vehicle dynamics, our goal is to drive cross-sector innovation by building a unified standard upon proven, existing components. By harmonizing established concepts, we can create a practical framework that ensures simulation reliability without reinventing the wheel.”

Research and collaborations

Fraunhofer IOSB’s research group leader Jens Ziehn reported on how ASAM helps scale R&D results, for example where ASAM’s OpenDrive, OpenLabel and OpenScenario are used to deliver interoperability and reusable data across diverse acquisition sources in the AVEAS Brave10K project to scale automated driving in public transportation.

SAE’s Ed Straub outlined ASAM’s collaboration with SAE J3259 (ODD taxonomy), while ASCS’s Alexander Walsh emphasized the complementary role of ASCS and ASAM in simulation, AI and HPC.

Industry collaboration continues at Vehicle Tech Week Europe

Vehicle Tech Week Europe, represented by ADAS & Autonomous Vehicle International, Automotive Testing Technology International and Automotive Interiors World, served as ASAM’s media partner.

Launching this June in Stuttgart, Germany, the three-day ‘festival of engineering’ will unite the full vehicle‑technology ecosystem – from EV and battery testing to autonomous‑vehicle development, UX/HMI, materials engineering and in‑cabin innovation – creating cross‑disciplinary value at a time when the industry faces intense technological and regulatory pressure.

ASAM is an association partner of Vehicle Tech Week Europe, and Yash Shah and Andreas Richter will continue their discussions about ASAM OpenX evolution and ODDs at the event.

Learn about Vehicle Tech Week’s partnerships here.

Find out more about ASAM’s International Conference, which will take place on November 4 and 5, 2026.

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By making this work available as an early preview, Codethink is inviting organizations interested in applying open source approaches to functional safety to review and begin working with the mapping while the work continues to mature. 

“This preview release reflects our belief that trust in software must be engineered in the open. We want organizations working at the cutting edge of safety-critical software to be able to review, evaluate and begin applying these mappings as they evolve,” said Paul Sherwood, chairman of Codethink. 

The Eclipse Trustable Software Framework defines six tenets – provenance, construction, changes, expectations, results and confidence – each aligned to the objectives of IEC 61508. This structure enables organizations to connect everyday software development practices with the rigor expected in safety-critical systems. 

Codethink plans to contribute the mapping to the Eclipse Trustable Software project once the company has achieved a successful functional safety assessment of CTRL OS using the TSF. This future contribution will build on the baseline assessment achieved last year and represents the next step in validating the framework in a production software environment. 

Until that milestone is reached, the mapping is being made available as an early preview for those interested in exploring open, transparent approaches to safety and compliance. 

“Our goal is simple, by demonstrating compliance with the Trustable Software Framework, organizations should be able to demonstrate alignment with the world’s most important safety and regulatory standards. Open collaboration is the fastest way to get there,” said Sherwood.  

The June edition of ATTI – read online here – will include a software feature investigating the latest in integrated testing, continuous integration and continuous development (CI/CD) and predictive testing

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Signal Plot is a free, downloadable extension for CanKing 7.4.0 or later that allows engineers to track multiple signals simultaneously, zoom into events, compare values using cursors and switch between live and pause modes. The extension provides engineering-unit graphing directly within the CanKing workspace, making it particularly useful for HIL bench bring-up and on-site debugging.

“Our goal with Kvaser CanKing has always been to give engineers the clarity they need with as little friction as possible. Signal Plot does exactly this; turning raw CAN and LIN data into clear, meaningful visuals in real time. It’s a simple yet powerful upgrade that delivers an immediate boost in understanding and efficiency,” said Martin Sventén, CEO of Kvaser.

The update is designed to provide engineers with faster, more intuitive insights into system behavior, making real-time monitoring and debugging of automotive networks simpler and more efficient.

The extension is the first built on Kvaser’s CanKing Extensions SDK, which was launched last year, to enable users to create web-based GUI extensions such as dashboards, gauges and custom interpreters without modifying the core application. The SDK leverages React and is compatible with both Windows and Linux systems.

CanKing 7.4.0 also adds support for LIN message logging and replay using MDF4.x files. Improved MDF4.x compatibility ensures that LIN data frames are correctly recognised, enabling the Message Logger and Message Replay nodes to function seamlessly across both CAN and LIN traffic.

CanKing 7.4.0 was unveiled at at Embedded World 2026 in Nuremberg, Germany.

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