Vehicles today are continuously evolving software platforms, which puts new pressure on how testing is structured across the lifecycle. To keep up with frequent releases and OTA updates, the optimal use of SIL, HIL and vehicle integration (VI), also called vehicle-in-the-loop, becomes a strategic element rather than a tooling detail.

In SDVs, functions are updated long after SOP, so relying on late-stage vehicle tests alone is no longer viable for safety or schedule. There is considerable pressure to maintain a rapid, continuous release cadence. Testing must be pushed earlier into development while still providing defensible evidence for safety cases and regulatory expectations over multiple software generations.

A layered approach – SIL, HIL and VI combined with on-road testing – provides a progression from fast, scalable virtual (model) checks to highly realistic, driver-in-the-loop evaluations. When those layers are integrated into a coherent architecture, they enable continuous regression across every release and OTA campaign. From my experience, I place great value on regression testing.

If you have read any of my work, you will know my penchant for a common lexicon. SIL enables feature exploration before production on a virtual ECU (subsystem) or host, connected to simulated components, sensors and communication networks (models). It is optimized for early virtual system integration testing, enabling rapid execution of thousands of scenarios before hardware and full production software are available.

HIL adds realism by connecting real ECUs or domain controllers to real-time components and network models on a test bench – a vehicle in the lab. This enables verification of timing behavior, network load, diagnostics and safety mechanisms under controlled yet representative conditions, well before full vehicles are built. From experience, securing time on the vehicle is not trivial, and we need to have some confidence in the product and system before VI.

VI testing takes place in a real vehicle on a proving ground, with its perception and control systems interacting with a controlled virtual environment. VI bridges the gap between lab rigs and road tests, enabling safe, repeatable execution of complex, hazardous scenarios that would be difficult to stage in the real world, in real time.

SIL, HIL and VI deliver the most value when they are treated as a progression rather than as competing options. SIL is ideal for early, rapid fault discovery, software refactoring and large-scale scenario sweeps during early development, when interfaces are still fluid and hardware is not yet fixed.

Once software stabilizes and hardware is available, HIL becomes the workhorse for ECU and domain-level integration, confirming that real electronics and networks behave as expected under realistic loads, failures and transients. VI and structured on-road campaigns then take over for full system behavior, human-machine interaction and vehicle dynamics in complex traffic, while reusing core scenarios defined earlier in SIL and HIL.

In an optimized SDV strategy, scenarios and requirements are progressively elaborated through learning enabled by SIL, HIL and VI. A lane change with a cut-in vehicle, for example, is first debugged in SIL, then checked for timing and network behavior in HIL, and finally executed in VIL on a controlled track and then on-road tests to confirm full-system performance.

Because SIL and HIL rely on virtual elements, sensors and environments, the credibility of their results depends on model veracity. Model veracity includes fidelity (the extent to which the model details represent the actual system), validity range (the range of applicability) and quantified error relative to physical measurements.

A practical approach is to treat model correlation as a formal activity. Engineering, rig, dyno and track tests are used to calibrate models; error bounds are computed for key outputs, such as forces, temperatures, signal delays and sensor artifacts; and these bounds are documented as part of the test environment definition. This makes it clear which requirements can be verified with confidence in SIL or HIL, and which still require VI or real-world testing due to model limitations.

When SIL, HIL and VI are used as a coherent stack, with clear definitions, effort to obtain strong model veracity and disciplined configuration management, they become an enabler for continuous delivery in SDVs. High-risk, safety-critical scenarios can be re-executed selectively in SIL and HIL for each code change. At the same time, VI and on-road checks provide final confirmation before wide OTA deployment. The result is a test strategy that aligns with the SDV business model: rapid, frequent software evolution, anchored in a reusable body
of trustworthy test evidence spanning virtual benches, real hardware and real vehicles.

Requirements, specifications, simulations, implementations and tests in modern automotive development are often managed in different tools and environments. MCP reduces the need for manual handoffs by enabling AI agents to interact with dSPACE tools through standardized interfaces while keeping engineers in full control of reviewing and approving all activities and results.

dSPACE’s MCP-enabled tools enable AI agents to support engineering workflows spanning requirements analysis, software development, simulation preparation and validation across VEOS, ConfigurationDesk, SystemDesk, Bus Manager, ControlDesk and real-time hardware. The platform also feeds validation results back into the development process to support iterative engineering.

“AI delivers the greatest value when it can actively support engineering workflows rather than operate outside them,” said Jann-Eve Stavesand, director product portfolio management at dSPACE. “With MCP-enabled dSPACE tools, engineers can connect requirements, specifications, implementation and validation activities through AI-supported workflows while maintaining full transparency, traceability and engineering control. This allows engineering teams to streamline their validation workflows and transition more efficiently from requirements to verified implementations.”

By connecting AI Agents with simulation, configuration, measurement and validation tools, MCP-enabled workflows help reduce manual setup effort and streamline engineering processes. Engineers can transition more quickly from requirements and specifications to software validation on real hardware, while maintaining full traceability throughout the process. Validation results can be fed back directly into development workflows, enabling continuous refinement and accelerating the path from concept to validated implementation.

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Simple Energy has expanded its use of the Siemens Xcelerator portfolio with Teamcenter X for product lifecycle management and Designcenter X for vehicle engineering. The company is also using Teamcenter X’s product configurator to manage vehicle variants and bills of materials, supporting collaboration across engineering teams while improving development efficiency.

“As our products scale in complexity, we’re strengthening the systems behind how we build them,” said Kiran Poojary, chief technology officer at Simple Energy. “With Teamcenter X and Designcenter X, we’re able to manage complex variants and large assemblies with greater speed and control – enabling us to accelerate development without compromising agility.”

“Simple Energy’s expansion of its Siemens’ software footprint demonstrates how customers can scale their digital foundation as product complexity increases,” said Mathew Thomas, vice president and managing director for India, Siemens Digital Industries Software. “Through close collaboration, we are supporting Simple Energy with a fully integrated vehicle engineering and lifecycle management digital thread that helps accelerate innovation and support its growth targets.”

Simple Energy initially adopted Teamcenter X to centralize product data management and improve engineering collaboration. As its electric vehicle programs expanded, the company extended its use of the software to manage enterprise bills of materials (BOMs) and vehicle variants using a configurable 150% BOM approach, enabling specific production BOMs to be generated for individual vehicle configurations.

To support increasingly complex vehicle development, Simple Energy has also replaced its legacy 3D CAD software with Siemens’ Designcenter X, integrated with Teamcenter X. The platform supports advanced surface modeling, large assemblies and 3D electrical routing, while Siemens says its value-based licensing model enables the software to scale with the company’s growth.

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This next phase of Nigel AI introduces prompt-based code generation within NI LabView and automated sequence generation in NI TestStand, enabling engineers to move from design to validation more quickly while maintaining the rigor required in mission-critical applications.

As product complexity increases and validation cycles grow more demanding across industries, engineering teams face pressure to deliver higher throughput without sacrificing quality.

Emerson’s approach embeds context-aware AI directly into the test environments engineers already use, helping streamline development while ensuring robust security, alignment with regulatory requirements such as the Cyber Resilience Act (CRA) and strong engineering oversight.

“In one year, Nigel has evolved from an AI advisor to AI author, showing the strength of the NI platform and its ability to integrate the most in-demand technologies quickly for the benefit of our customers,” said Ritu Favre, president of Emerson’s test and measurement business.

“By enabling purpose-built AI to generate code and test sequences within workflows, we’re accelerating productivity while ensuring engineers remain in control of validation and decision-making.”

With this release, Nigel AI is now available across the broader NI LabView+ Suite, bringing AI-driven workflows to NI InstrumentStudio, NI FlexLogger and NI VeriStand. The expansion brings context-aware intelligence into interactive measurements, sensor-based datalogging and hardware-in-the-loop test environments. Engineers can apply AI capabilities consistently across the full test lifecycle – from initial development through system validation and deployment.

Nigel AI is purpose-built for engineering environments and operates directly within structured test workflows. It leverages modular instrumentation, open software systems, configurable workflows, engineering code and structured data to produce outputs aligned with real-world system constraints.

Related news, Applied Intuition launches Dana agentic platform for physical AI

“We believe physical AI will become one of the defining technologies of this century,” said Qasar Younis, co-founder and CEO of Applied Intuition. “Our ambition is to help bring intelligence to a billion machines, and Dana is the platform we built to make that possible.”

Unlike general-purpose AI tools designed primarily for digital workflows, Dana is built for the complexities of machines operating in the physical world. Dana comes with all the platform capabilities needed to build and deploy safety-critical physical AI applications, including data, visualization and tooling, as well as the evaluation, traceability and governance these systems require. The platform was designed to work across industries and with a wide range of use cases including software-defined vehicle development and advanced driver assistance systems (ADAS), truck fleet management, robotics and intelligent in-vehicle experiences.

Dana enables customers to deploy Applied Intuition’s reference applications for areas including autonomous driving and fleet operations, or develop their own applications. The platform supports both natural language and command-line interfaces to simplify development tasks and accelerate software iteration. It can also be integrated with enterprise tools such as Slack and Jira to help connect engineering and operational workflows.

Applied Intuition has used Dana internally since last year and says the platform has reduced some stages of vehicle development from months to days through agent-driven workflows. The company has also provided early access to selected customers, including Komatsu and Isuzu Motors, with Isuzu using the platform to support development of Level 4 autonomous commercial trucks.

“We’ve been impressed by how Dana can streamline complex engineering workflows and accelerate development,” said Yasuhiro Yazawa, director, Isuzu Motors, Japan. “Dana gives our engineering teams greater confidence to develop, track and deploy safe autonomous-vehicle capabilities at a much faster pace.”

“Applied Intuition has been a valuable technology partner as we continue advancing the digital capabilities that support the next generation of mining equipment and solutions,” said Peter Salditt, CEO, Komatsu Mining. “Dana represents another step forward, bringing intelligent, agentic capabilities into our engineering workflows to help our teams innovate faster, improve efficiency and ultimately create greater value for our customers’ operations.”

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In 2010, Damian Harty was enjoying an especially rewarding time in his career as technical specialist in dynamics at Prodrive, the renowned UK engineering consultancy and motorsport preparation outfit. Having previously been involved in the development of the all-conquering Subaru Imprezas in the World Rally Championship, he had turned his attention to the new (and disappointingly short-lived) Mini Countryman WRC initiative. At that time, Harty wrote an article for Vehicle Dynamics International, ATTI’s former sister title, about the Mini’s development, in which he memorably noted, “There will always be a bigger rock. However much suspension travel there is, it will all get used up. Whatever loads are designed for, they will be exceeded.”

That there will always be a “bigger rock” neatly summarizes much of the difficulty of automotive testing, no matter which part of the vehicle is under consideration. Sixteen years later, during a chat with Harty, ATTI began by asking whether it is now easier than it was before to know where the limit of testing should lie. “I don’t think so, because the challenge is not so much about the vehicle itself, it’s about what the vehicle will encounter,” he replies. “And for as long as you have human drivers and, let’s say, imperfectly calibrated software drivers, it’s still difficult to anticipate the corner cases with which the vehicle will be presented.

“That was what I meant by ‘There will always be a bigger rock.’ So, the requirement to understand where we draw a line in the sand, and then what happens when we inevitably accidentally overshoot that line in the sand, still exists, I think.”

Harty notes that Sir Alec Issigonis, the designer of the original Mini, initially believed that the car’s combination of small size, light weight, front-wheel drive and hydrolastic suspension provided drivers with greater safety margins – but later realized that drivers would fully use up whatever margins you gave them.

“Someone will always go the other side of that line in the sand, so we still need to understand what happens there,” he continues. “We then try to make a value judgment about where we draw the line, such that, if you’re just below it then things emerge undamaged, or if you’re above it, things are damaged in a way that we can predict.

“A further difficulty is that you’re trying to stay inside the herd. If your line is drawn in more or less the same place as everyone else’s, then you’re probably okay. But if it is drawn a lot higher than everyone else’s, your products cost more than they need to; a lot lower than anyone else’s line and you will be on the receiving end of some pretty unpopular sentiment. I think that finding where to draw the line is no easier or more difficult than it ever was.”

A blend of human experience and previous data remains the key to determining how demanding a test should be, according to Harty. “One of the challenges that I understood very clearly in the powersports industry is that as your products improve, people move the line,” he observes. “The speed that people are willing to carry over a certain surface goes up, for example. As you make products better, people’s expectations increase. It’s not a static thing at all.” In short: the test engineer’s work is never done.

Recipe for success

Looking ahead to this year’s ATTI Awards, which will take place at Automotive Testing Expo Europe, Harty notes that test engineers are continuing to marry new techniques with established tools. For the latter, Isaac Newton’s F=ma remains his go-to for a back-to-basics understanding of what is happening to a vehicle, component or subassembly. He also expresses surprise at how little FEA has evolved in the past 30 years, computing power aside. For new methodologies, he highlights automotive software testing as an area where tools are advancing especially rapidly – but cautions that the industry’s understanding of how the product is being used, and therefore where the focus of testing should lie, is still evolving.

“Testing is about trying to find stuff before your customers do,” he explains. “The two areas where that’s most difficult tend to be durability and anything to do with software. The durability challenge is pretty much the same as it has always been. I think that for metallic components, we’re quite well served [with tools and knowledge]. But as soon as you switch to polymers or composites, for example, we’re grasping in the dark as to what they’ll do under repeated loading. That holds us back from using different materials for critical structural applications.

“Meanwhile, the scope for software is evolving all the time,” he continues. “When you put things in the hands of the public, you have the million-monkeys, million-typewriters problem, where they are generating scenarios that you maybe didn’t test for, so all the energy and effort is on the software side. Personally, I’m not so interested in whether the cellphone connectivity works properly or not, because it’s unlikely to be life threatening if you get that wrong. But there is a lot of functional stuff where many systems are interacting and there’s a whole load of corners to poke into. I feel like there is a lot of unmapped territory there that we’re still finding our way into as an industry.”

Harty believes that help in finding such corner cases will be one area in which AI will benefit the testing industry. “One of the things I like about AI is that it’s like talking to someone who isn’t necessarily all that clever but is hugely widely read. For the corner cases, I think it will be easier to poke more deeply into the corners, find and flag an issue, and specifically test for it.”

No two the same

Knowing what to test for will remain an issue for test engineers more generally, he believes, and will continue to be a moving target as customers exploit the margins to which Issigonis referred. Harty has been reminded of this in his recent work with TVS, India’s largest motorcycle manufacturer, rekindling a working relationship that began in the 1990s.

“For me, the biggest misconception about testing is the illusion that we really know what people are going to do with our products,” he confirms. “I think people underestimate how much of an honest best guess is used in all sorts of testing. There’s an erroneous belief in precision that’s misplaced when it comes to putting products out in the wild and understanding what people are going to do with them.

“When I began working with TVS in the 1990s, the engineers talked about how one product, a 50cc moped, was used as a load carrier. I did not understand what they meant: after all, it has a 50cc, two-stroke engine that makes just over one horsepower and can barely do 30mph [50km/h]! But when I came to India, I saw endless examples of multiple sheets of 8 x 4ft [2.4 x 1.2m] plywood, bags of concrete and more, all piled up in incredibly imaginative ways on mopeds with someone walking beside them, using the engine to just walk the load to where they wanted to go. I had no conception that the product could be used like that, but these engineers understood their market. The load case for the pedals was not a human, it was as many sheets of 8×4 ply as you could balance on it – a very different number of kilograms.

“That is an example of the sort of thinking that is quite difficult, especially if you’re coming up with a novel product,” he concludes. “It’s hard to imagine how people might conceive of using your product. That, I think, will continue to keep us interested and amused going forward.

“There is a glorious quote from Douglas Adams in Mostly Harmless: ‘A common mistake that people make when trying to design something completely foolproof is to underestimate the ingenuity of complete fools.’ He’s saying that whatever you think, there’s no guarantee that’s how everyone else will think. It’s a fascinating idea.”

This article was first published in the June 2026 edition of Automotive Testing Technology International

The results of this year’s Automotive Testing Technology International Awards will be announced at Automotive Testing Expo Europe next week – secure your ticket now!

As cellular networks transition away from legacy 2G and 3G infrastructure, auto makers face mounting pressure to deliver compliant Next-Generation eCall (NG-eCall) implementations that utilize packet-switched 4G and 5G networks. Because regional network modernization moves at varying speeds, vehicles must operate reliably across mixed-network environments. According to Keysight, its certified Hybrid eCall solution addresses this challenge directly, enabling in-vehicle systems (IVS) to dynamically switch between legacy and NG-eCall systems based on real-world network availability.

Building on Keysight’s previously achieved EN 17240 validation, this new certification ensures continuous alignment with evolving international standards.

Lourdes Sánchez, connectivity director at DEKRA, said, “At DEKRA, we are committed to supporting the automotive industry in the transition toward next-generation emergency communications through testing and certification services aligned with the latest European standards. Our collaboration with Keysight in validating its Hybrid eCall capabilities according to EN 18052:2025 has enabled the verification of critical aspects related to interoperability, reliability and regulatory readiness, helping accelerate the adoption of safer and more connected vehicle solutions for the future.”

The certification covers a range of critical real-world testing scenarios, including emergency call setup to validate rapid and stable connections across evolving network infrastructures, minimum set of data (MSD) transmission to ensure accurate delivery of vehicle location and telemetry data to emergency responders, voice communication to verify clear and uninterrupted call quality under realistic network conditions, and network fallback to confirm reliable operation when transitioning between NG-eCall and legacy network environments.

Thomas Goetzl, vice president and general manager of Keysight’s Automotive & Energy Solutions, added, “The automotive industry is at a critical juncture as cellular infrastructure evolves. Auto makers need proven, reliable testing methodologies to avoid compliance bottlenecks and deployment delays. Achieving this DEKRA certification underscores our commitment to delivering real-world-ready solutions, giving manufacturers the confidence to scale their connected vehicle platforms globally while meeting the highest safety standards.”

Keysight will demonstrate its Hybrid eCall and NG‑eCall testing at the ETSI NG‑eCall Plugtests 2026.

In related news, Hyundai Mobis joins S-Core project to advance SDV software platform development

To this end, the company has joined the Software Defined Vehicle (SDV) Working Group under the Eclipse Foundation and will participate in the S-Core project to develop an SDV software platform in earnest.

The S-Core project is a global initiative launched primarily by European companies in late 2024 to standardize foundational technologies such as software platforms and middleware. It is the first open-source-based software platform development project to meet ASIL-B, the automotive industry’s functional safety standard.

Currently, a total of 13 companies are participating in the project and prioritizing the implementation of core technologies required for SDVs, as standardized foundational technologies are essential for accelerating the development of applications such as autonomous driving. Under the common objective, participating companies are also working to prevent redundant investments while improving system stability.

The most distinctive feature of the S-Core project is that it applies an open-source development approach, previously used primarily in the IT industry, to the mobility sector. Participating companies disclose some of their software technologies, enabling developers around the world to freely use and improve them.

Another reason companies disclose coding technologies that constitute intellectual property is the potential to create both tangible and intangible added value. By encouraging more developers to use their software, companies can increase the likelihood of their technologies becoming global standards.

The technology Hyundai Mobis plans to disclose is a so-called ‘container solution’ that minimizes interference between software within the Linux operating system. The technology effectively creates partitions between a wide range of software in SDVs, packaging them individually so they can operate quickly without affecting one another.

The container solution is known to be more than 10 times faster than existing technologies in automotive controller environments. The company has also secured an always-on integrity assurance function to prevent software tampering caused by external intrusions and other risks.

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The release extends the capabilities of Candera CGI Studio across the entire workflow from design and validation to deployment, and enables development teams to create and scale modern user interface concepts more efficiently across industries, hardware platforms and a wide range of use cases.

“With Candera CGI Studio 3.16, HMI development gets smarter and faster, combining AI-assisted tools, flexible workflows, and broad platform support,” said Roland Winkler, senior product development manager at Candera.

Improvements and updates

The Scene Composer experience has been enhanced with a new View Editor that simplifies automotive HMI workflows by allowing developers to preview scene combinations and manage transitions within a structured framework.

An Embedded Player enables seamless switching between design and runtime simulation inside Scene Composer, while a standalone Player remains available.

Multi View in the Scene Editor adds up to four synchronized views with selectable camera angles, improving inspection of complex 3D interfaces and enabling quick switching between single and multi-view modes.

Localization and documentation workflows are improved with built-in XLIFF 2.0 support. Developers can import, merge, edit and export localized text in a standard XML-based format, making CGI Studio easier to integrate into existing localization toolchains.

The new AI Docs Agent provides in-workflow support by delivering tailored answers in any language, helping users find information more quickly and reducing day-to-day friction. Each response also includes direct links to relevant Candera CGI Studio documentation, enabling users to access source material immediately.

Solution templates and advanced 3D

Another highlight of Candera CGI Studio 3.16 is the expansion of ready-to-use automotive HMI templates. The new In-Vehicle Infotainment (IVI) template includes building blocks for navigation, weather, media, phone, HVAC and digital twin scenarios. It supports both 2D and 3D use cases, with advanced visualizations and customizable application screens.

The new Instrument Cluster Solution Template integrates advanced driver assistant system functions such as adaptive cruise control (ACC), lane departure detection (LDD) and side assist alongside essential dashboard elements like speed gauge, media, telltales, time, odometer, temperature and turn-by-turn guidance. These templates help teams accelerate prototyping and shorten time to value.

The update enhances 3D capabilities with full glTF 2.0 support and features such as clear coat rendering. It also adds HDR rendering with tone mapping and automated import of image-based lighting cube maps for global illumination. Together, these improvements enable more realistic HMI visuals across automotive, industrial and embedded applications.

Engine and platform improvements

On the platform side, Candera CGI Studio 3.16 expands deployment flexibility with native rendering support for Apple iOS, enabling developers to build applications for Apple devices using Xcode and Metal.

The release also introduces Vulkan support as a modern graphics backend for 3D GPUs, improving CPU and GPU efficiency on Linux and Android platforms.

Another key addition is Software Rendering, which enables Candera CGI Studio 3.16 to run on platforms without a GPU. This enables platform-independent rendering across a wide range of targets, including ESP32-S3, ESP32-P4, Traveo II Body MCU, Raspberry Pi Pico and Linux-based systems. The renderer supports transformations, blending and visual effects while achieving up to 60 FPS depending on the platform and configuration, with low memory requirements.

Additional engine optimizations round out the release. Candera CGI Studio 3.16 now supports the new Monotype Spark Engine, designed for low memory usage and high performance. The update also includes newer versions of FreeType and HarfBuzz, delivering significant cache improvements for more efficient text rendering.

Candera CGI Studio 3.16 also streamlines behavior configuration through automatic code-size optimization using the SC plugin and CMake integration. These improvements help developers reduce memory footprint, improve performance and simplify overall project setup and configuration.

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AGL SoDeV is an open-source reference platform for building software-defined vehicles. It combines the AGL Unified Code Base (UCB) with Linux containers, VirtIO, the Xen hypervisor, Zephyr RTOS and other Linux Foundation projects in a single pre-integrated package that runs on automotive systems-on-a-chip (SoCs), on virtual machines or in the cloud. SoDeV enables auto makers and suppliers to decouple software development from hardware availability, accelerating SDV time-to-market.

As the automotive industry moves toward software-defined vehicles (SDVs), demand is increasing for shared, open-source software platforms. AGL brings together auto makers, Tier 1 suppliers and technology companies to develop and maintain a common software stack to reduce fragmentation, shorten development timelines and support innovation across vehicle systems.

The addition of EMQ, Lineo Solutions, MediaTek, VA Linux Systems Japan and Very Good Ventures expands AGL’s expertise across areas including automotive semiconductors, connectivity, Linux engineering, in-vehicle user interfaces and embedded software tools that support large-scale SDV development.

“We’re excited to welcome these new members to the AGL community, each of which brings distinct and valuable expertise to the table,” said Dan Cauchy, executive director of Automotive Grade Linux. “The ecosystem around our SoDeV reference platform continues to grow, reflecting the breadth of innovation that open-source collaboration makes possible. The automotive industry is undergoing a fundamental shift to software-defined vehicles, and AGL is proud to be at the center of it.”

Available in latest AGL software release

An initial version of the AGL SoDeV reference platform is available in the latest AGL UCB software release, Ultimate Unagi, enabling developers to build and test an SDV system on Renesas Sparrow Hawk reference boards and cloud-based processor environments.

Announced in December 2025, the AGL SoDeV Reference Platform combines the AGL Unified Code Base (UCB) with multiple open-source projects hosted by the Linux Foundation to support the consolidation of ECUs, hardware abstraction through virtualization, and cloud integration for software-defined vehicles.

“The AGL platform represents exactly the kind of production-focused, community-driven approach to SDV software that our customers and partners need,” added Masahiro Sano, the chairman and CEO of Lineo Solutions.

In related news, Mayr Power Transmission to highlight advanced drivetrain testing solutions at Automotive Testing Expo Europe 2026