The June 2026 issue is now available online! Packed full of news, interviews and features, including:

Cover story – Laboratory digitalization: As a central component of a data-centric operation, lab digitalization can be challenging, especially when transforming to ISO/IEC 17025 practices

Tech insider – AI-driven SDV project: Researchers at the Technical University of Munich are developing a centralized SDV architecture that uses AI and digital twins to simulate, test and upgrade EVs

ATTI Awards: Panelist interviews: Vehicle dynamics guru Damian Harty and Mahindra Automotive North America’s manager of body structures engineering, Prasad Kulkarni, discuss what keeps them awake at night

Software testing: ATTI explores how the sector is getting to grips with CI/CD pipelines and provides the latest on a unique test technique aimed at automated, predictive and scalable deployment

EMC analysis: Selective simulation of components will be key to reducing risk and avoiding wasted resources, according to specialists

Euro 7 testing outlook: Where is the industry in its quest to meet the latest engine emissions requirements, as well as brake and tire PM limits?

Agricultural vehicle analysis: Their sheer size and weight make them a different animal from passenger cars. With increasing automation to consider as well, how does this influence their testing?

Automotive Testing Expo Europe preview: The must-sees for this year’s exhibition in Stuttgart, including speaker highlights and technology debuts

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“For the BMW Group, the use of industrial data is a key factor in translating artificial intelligence into value creation,” said Dr Franz Decker, CIO and senior vice president of the BMW Group. “By combining our engineering datasets with Mistral AI’s model training capabilities, we are building specialized AI which supports complex development tasks.”

Complexity and data volume in crash simulation

Each week, BMW runs thousands of virtual crash simulations, generating vast amounts of engineering data. Over time, this has resulted in a historical dataset of over one petabyte of crash simulation data that provides highly detailed insights into vehicle structures and material behavior, forming a unique foundation for training an industrial AI model.

“As Industrial AI becomes the new frontier for AI, we are proud to partner with the BMW Group” said Marjorie Janiewicz, chief revenue officer of Mistral AI. “This collaboration shows how industry specific AI models can help solve complex engineering challenges such as crash simulation.”

Large industry model as a technical foundation

To scale this approach, the BMW Group is focusing on large industry models. These are AI systems trained on industry-specific engineering and simulation data from vehicle development and safety testing. Unlike general‑purpose AI systems, LIMs embed domain‑specific knowledge directly into the AI model. This requires not only industrial data, but also deep domain expertise and technical environments that enable AI systems to learn directly from BMW’s development processes.

Related news, Next-generation Helm.ai models deliver full-HD 360° synthetic driving environments

The foundation models address what is often referred to as the industry’s data wall, where the cost and effort of collecting real-world edge-case data can slow development. Traditional generative world models typically produce video at sub-HD or VGA resolutions (around 0.4MP per camera). In contrast, Helm.ai generates full-HD (2MP) output that matches the resolution of modern production vehicle cameras, helping to reduce the sim-to-real gap in training for Level 2 and Level 4 autonomous driving systems.

Scene transfer versus fully synthetic generation

Helm.ai’s platform provides auto makers with a pipeline for data augmentation and creation.

GenSim-3 (high-fidelity scene transfer) enables development teams to restylize real-world video synchronously across six-camera, 360° surround-view setups. The model alters parameters such as weather, illumination and object appearance at full-HD (2MP) resolution. Additionally, the latest model introduces improvements in environmental texture, surface reflectivity and light behavior on complex materials.

VidGen-3 (fully synthetic generation) generates highly realistic driving sequences completely synthetically. By simulating complex environments, human-like agent behaviors and traffic logic from scratch, VidGen-3 bridges geographic and environmental data gaps at scale.

The 5X pixel density advantage

The technology’s key breakthrough is the fidelity of the multicamera generative simulation.

By producing full-HD (2MP) video, Helm.ai provides significantly more visual information than traditional generative datasets. Because modern production vehicles use high-resolution camera systems, training data is more effective when it matches that resolution. Lower-resolution synthetic data can create a domain gap when used to train full-HD perception systems. By generating data natively at 2MP per camera, Helm.ai aligns training inputs with real-world sensor output, supporting more consistent model performance in deployment.

To accommodate diverse sensor and training requirements, engineering teams can optimize for dynamic, high-speed validation with three-camera setups at 30fps, or maximize spatial context with a full six-camera, 12MP surround view at 5fps.

The virtual sensor twin

Unlike CGI-based video generation methods, Helm.ai’s models are designed to simulate hardware-like sensor output by incorporating certain physical characteristics of real camera systems. This includes reproducing effects such as sensor noise patterns, lens flares and exposure-related artifacts. This produces training data that more closely reflects real-world camera behavior, helping perception systems learn under conditions similar to those encountered in actual driving environments.

High fidelity on lower compute

While other generative world models rely on the massive computational scaling of thousands of GPUs to generate sub-HD video, the full-HD (2MP) resolution milestone was achieved using a highly optimized cluster of just a few hundred advanced GPUs.

“We are moving the industry from standard ‘AI video’ to authentic, hardware-accurate sensor emulation,” said Vladislav Voroninski, the CEO and founder of Helm.ai. “By leading with a full-HD (2MP) standard and a 12MP total aggregate capability per timestep, we have solved the resolution bottleneck that has historically limited the utility of generative AI in safety-critical systems. By optimizing our compute architecture, we are giving our partners a high-performance platform to validate their autonomous stacks using synthetic data that perfectly matches the fidelity of their actual production sensors.”

In recent news, Candera unveils “smarter and faster” CGI Studio 3.16 HMI development software

“Our customers expect a straightforward path from simulation model to microcontroller, and the new integration with Matlab and Simulink delivers exactly that,” said Brad Rex, senior director of the system solution team, user experience group at Renesas. “By working with MathWorks, we’ve removed the need to assemble toolchains and device drivers by hand so teams can simulate and validate designs earlier, iterate faster and reduce integration effort across ECU and industrial‑control projects.”

The Renesas RH850/U2A microcontroller – widely used in automotive ECUs – provides the deterministic performance and safety-critical features required for EV motor control, ADAS and body electronics. Engineers developing traction motor control for electric vehicles can deploy field‑oriented control and regenerative braking algorithms directly from Simulink to RH850/U2A‑based ECUs. This shortens the time from concept to vehicle‑level testing, supports smoother torque delivery during rapid transients and speeds calibration across drive cycles – without writing initialization code or custom build scripts.

Said Anuja Apte, India product marketing manager at MathWorks, “Our collaboration with Renesas strengthens the level of interoperability that engineers expect when using Matlab and Simulink. By providing a direct path from Simulink models to optimized microcontroller deployment, we help engineering teams move from design to hardware more efficiently while staying integrated with the broader toolchains they rely on. This approach reflects the MathWorks Connections program, which brings partners and customers together to accelerate innovation and reduce time-to-market within a widely adopted engineering and scientific platform.”

In related news, OmniTrust and Synopsys collaboration enables earlier security validation of embedded systems

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.

Recent news, Michelin reveals software-based tire digital twin platform

“Thanks to its universal digital twin, Michelin not only listens to the vehicle; it whispers (words of wisdom) in its ear in real time!” said Philippe Jacquin, Michelin Group executive vice president of research and development and a member of the group executive committee “Every tire, whatever the brand, has its very own embedded intelligence. By making its tires smarter, Michelin is bringing a new dimension to the role of tire manufacturer and is once again proving its commitment to enhancing safety for all road users.”

Safer driving experience

Michelin’s digital twin is a dynamic virtual replica of a tire. It continuously analyzes and predicts the state of tires at a given time, factoring in tire pressure, wear, load, grip and driving conditions and comparing these with in-vehicle data.

It directly interacts with the embedded systems to optimize their performance, helping to enhance safety by predicting maximum grip, preventing aquaplaning, boosting the effectiveness of ADAS systems such as ABS, monitoring tire pressure and detecting any overloading. This means the vehicle can anticipate grip, improve its stability, optimize fuel consumption and adapt braking distances by as much as several meters.

The integrated system provides real-time support based on tire condition, delivering practical benefits during driving. For drivers, this can contribute to a smoother, safer and more predictable driving experience without requiring changes to driving behaviour.

By providing a continuous stream of vehicle-based data, the system can support predictive maintenance and help extend tire life. Maintaining tires in optimal operating condition may reduce material use and lower environmental impact across the tire lifecycle. The approach is also intended to align with the increasing adoption of software-defined and autonomous vehicles, where performance, functionality and user experience can be updated over time.

Universal embedded system

Michelin’s 100% software-enabled embedded intelligence-driven system makes use of existing in-vehicle data without requiring additional tire-mounted sensors. It is compatible with all tire brands and models and can be fitted to all types of vehicles.

According to Michelin, the solution has been developed in collaboration with Brembo, Hyundai, QNX, ETAS and Sonatus, covering the full spectrum of innovation from fundamental research through to industrial integration at scale.

In a recent partnership with Brembo, the latter’s Sensify intelligent braking system was used to demonstrate the use of Michelin’s digital twin technology in ABS performance. By integrating tire condition data into braking algorithms, the system has shown potential to improve braking performance, including shorter braking distances of up to 4m and increased stability during heavy braking.

In related news, Univrses AI technology integrated into Pirelli Cyber Tyre platform

The recall follows independent crash testing carried out by Euro NCAP last year, during which the MG3 experienced a front-seat rail failure in a frontal crash test. This allowed the driver’s seat to become unlatched during the impact.

Following a detailed technical review, MG agreed with Euro NCAP’s findings that the issue could also occur in real life, and addressed the root cause with an improved design that was verified by Euro NCAP and eliminates any risk of the seat unlatching. The manufacturer swiftly implemented the change to production vehicles. MG has subsequently developed a repair for existing cars and worked with authorities in Europe and the UK to initiate a recall and repair campaign for affected MG3 models already sold to customers.

Owners of an MG3 built before August 2025 have been strongly advised not to ignore or delay the repair, and to contact their local MG dealer to check if their vehicle is affected and have the recall work performed.

“This was a significant safety flaw that Euro NCAP uncovered, and we were pleased with MG Motor’s robust response,” said Dr Aled Williams, program director at Euro NCAP. “Owners of the MG3 and MG3 Hybrid+ who haven’t heard about the vehicle recall or are still to take action should contact their local MG dealer, and they will confirm whether their car is one of the affected models and arrange repairs, if necessary, at no cost to the owner.”

In recent news, Audi A2 e-tron racks up the miles during validation testing

Type 01 development has been directly inspired by technology used by the company’s Formula E team – Jaguar TCS Racing – integrating technology proven on track by the World Championship-winning all‑electric team.

The prototype’s responses have been fine‑tuned using lessons learned from Jaguar TCS Racing’s all‑wheel-drive control software and fast‑switching silicon-carbide‑powered inverters.

Regenerative braking, range and fast‑charging capability have also reportedly been enhanced using technology developed on track.

“To achieve our vision for Type 01, we not only harnessed our Formula E know‑how but also embedded the competition mindset of Jaguar TCS Racing in our engineering teams. This helped us develop innovative solutions for our luxury four‑door GT that drives like no other electric car,” said Jaguar managing director Rawdon Glover.

The Type name has been associated with Jaguar since the Le Mans-winning C-Type in 1951. It represents a combination of performance and driver engagement alongside refinement, as seen in models such as the E-Type and F-Type. In Jaguar Type 01, the 0 represents electric propulsion and zero tailpipe emissions, while the 1 signifies its position as the first Jaguar of a new era. The vehicle was designed, developed and built in the UK.

Jaguar’s four‑door GT will be revealed later this year.

In related news, BMW unveils Vision BMW Alpina design study

“It was a great start yesterday (May 19) with the launch of our smart prototyping concept with lots of positive feedback from customers and partners,” said Tanneke Reinders, executive VP at HBK. “From this morning’s presentations we really see ‘smart testing’ coming alive. The key is transforming from data creators to data interpreters – that’s the main shift for us. It’s about connecting data in one ecosystem. When we talk about data, especially the amount of data and fragmentation of it, we want to support.”

This year there were multiple tire-focused presentations, highlighting the critical role of the tire and its relationship with the vehicle. It seems tire and vehicle developers are working closer together than ever to refine their use of simulation and tighten the integration between the digital and real worlds. This was exemplified in the opening presentation by Alessandro Capobianco, R&D specialist for tire models and vehicle/DIL simulation at Bridgestone, and Eric Walter, vehicle dynamics CAE engineer at VW (both above), who provided an insight into their vehicle/tire co-development work.

Another of ATTI’s highlights from the summit was the Nissan and Horiba Mira presentation (right), in which the two speakers, Leo May, lead simulation engineer, Nissan Motor Corp, and Stratos Stratoudakis, a consultant at Horiba Mira, detailed how they are working together to prioritize virtual development by moving it further forward in the cycle, with the ultimate aim of reducing development time by 25%.

Marc Hedrich, a development engineer at event exhibitor Team Rosberg Engineering, said of the company’s attendance at the summit: “We are presenting our expertise in providing simulation models. The focus for us is to learn new things because software is ever evolving and you don’t want to be left behind. I like this year’s approach of trying to get the best data and not the most. I’ve learned already how others such as Mercedes-AMG are using these tools. It’s cool to connect with companies that are doing similar things and others that are doing things we’ve never heard of.”

This morning concluded with a presentation from the rarely heard-from Tesla (top), which gave insights into the company’s NVH analysis using a simulator.

Francesco Calabrese, who was attending as an exhibitor representing Fraunhofer ITWM as well as the institute’s spin-off, Virtual Tire Technologies, said, “It’s a good event to push the word for using more simulation with the goal of spending less money on prototypes. What I like is the excitement in this field as it grows – every year there are significantly more people which is a sign people are understanding the importance of it.”

Another OEM getting to grips with driver-in-the-loop simulation was Alpine. Rémi Auman, expert leader in simulation for ride and handling, noted that his team is targeting three areas for 2026 (see image below): enhancing driver immersion – an ongoing challenge for all developers; increasing the use of sim-workbench services; and improving multibody models. For comfort studies, Auman stated that the auto maker’s models are quite mature, so the next steps will be to adjust the settings and, “maybe in the future, we’ll be able to achieve real-time simulation,” he said.

With the conference’s emphasis on data harnessing, Cosin Scientific Software, which has been a partner of VI-grade for more than 15 years, launched its fresh approach to tire data management. With an increasing amount of users, the company has run into scaling problems when distributing high-quality input data. Developing countries especially cannot afford high-quality input data. To address this, the company has created a marketplace with a digital rights management system where tire data providers can market their data through Cosin’s software on the platform.

Cosin MD Gerald Hofmann explained, “Up until now, tire data has been sold as data files. This means once the data is out your hands, then it’s out in the wild. You can’t build a business model on this, which is why this data is very expensive. By putting the data under a digital rights management scheme, then the data provider can sell it using pay-per-use models.”

According to Hofmann, the development of this tire data library was an unexpected but welcome development for visitors: “We are very excited to see how this will be adopted through workflows which are currently very static. We’re hoping to break this up by giving access to a variety of input data. Users can allocate budget for a library of tires to choose from during the development cycle.”

AI specialist JuliaHub exhibited its latest technology, Dyad 3.0, which combines scientific machine learning with Gen AI. According to JuliaHub head of business development Europe Michael Hoffman, its name comes from its dual capabilities in performing controls and system modeling.

Asked what the company’s USP is, Hoffmann emphasized that having a single AI agent that can replace multiple tools is a powerful offering: “When we started Dyad we had AI mind – it wasn’t an afterthought where we had to do a plug-in. This means that the agent knows our architecture, and it only has to deal with one language.”

As expected, Dyad was met with both excitement and skepticism at the HBK SPS.

“We feel when talking about smart testing, we can add value,” added Hoffmann. “You have the measurements and we have the people to get the mathematical models or to refine their simulation models. For example, our value proposition to Rebel Dynamics [a servo platform partner of VI-grade], is that they have a mathematical model of their system, but they also have measurements, and there are differences between the two. We could help them minimize those differences so they can achieve better control.”

Related news, rFpro develops Japanese test route digital twin for virtual vehicle development

In open-air trials in Andøya, Norway (September 2025), the software demonstrated an ability to suppress spoofed signals and maintain signal integrity, supporting more resilient positioning for safety-critical applications.

The trials were conducted as part of Jammertest, an internationally recognized test campaign hosted by Norwegian authorities to assess GNSS performance under realistic jamming and spoofing conditions.

Focal Point Positioning collected live RF data using a test vehicle equipped with a roof-mounted GNSS antenna and a LabSat 4 recorder, alongside an inertial measurement unit (IMU) and commercial off-the-shelf GNSS receivers. The recorded signals were then replayed into multiple receivers at the company’s Cambridge facility for further analysis.

Laurence Bennett, an algorithm engineer at Focal Point Positioning, explained, “Jammertest was an ideal opportunity to evaluate GNSS performance under realistic compromised conditions and understand how this technology can withstand the effects of spoofing.”

Bennett continued, “We demonstrated how Supercorrelation enables receivers to distinguish between genuine and inauthentic signals, improving resilience without the need for additional, complex hardware.”

Analysis of the results showed a clear performance difference between receivers with and without Supercorrelation processing. During meaconing attacks, where legitimate GNSS signals are rebroadcast to mislead receivers, conventional processing was unable to isolate the authentic line-of-sight satellite signal, which was overwhelmed by stronger, delayed spoofed signals. In these conditions, receivers risk tracking the false signal, potentially compromising positioning integrity.

In contrast, Supercorrelation processing was able to recover and track the authentic signal while significantly reducing the impact of meaconed signals. Supercorrelation uses long, motion-compensated coherent integration to strengthen genuine satellite signals and suppress delayed or inauthentic ones.

As a software-based approach, it can be deployed via firmware upgrade, enabling scalable integration across GNSS-enabled devices.

“As sectors such as automotive, mobile and critical infrastructure become more reliant on GNSS, resilience to this kind of disruption is becoming increasingly important,” added Bennett. “Spoofing and meaconing attacks pose a growing risk to positioning accuracy, particularly in safety-critical applications where compromised signals can have significant operational consequences.”

Focal Point Positioning plans to publish further technical analysis of its Jammertest 2025 results in the coming months, supporting ongoing collaboration with chipset partners.

In related news, Audi A2 e-tron racks up the miles during validation testing