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.”

Related news, Voluntary safety assessment by Einride published for cab-less autonomous heavy-duty trucks

With the new BAC module for CAN-FD/LIN, these interfaces can now be tested for functionality during production. The Bus Access Cable (BAC) is connected to one of the five slots on the Scanflex multi-port bus I/O Module 9305. This enables access to the complex test functions of the Scanflex boundary scan controller, which then takes over the simultaneous generation and dynamic distribution of the vectors and control sequences to the interfaces.

What is Scanflex? 

Scanflex is a modular JTAG/boundary scan controller. Based on state-of-the-art multi-core processors and FPGAs, it enables users to execute test and programming technologies from embedded JTAG solutions. Its multifunctional architecture enables these technologies to be combined flexibly and with high performance on a single platform.

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Testing at the Arjeplog facility focused on traction, power distribution and vehicle control on snow and ice, allowing engineers to evaluate system limits and refine control strategies under extreme conditions.

A key area of development is the new AMG Race Engineer system, which integrates hardware and software to enable detailed adjustment of vehicle dynamics. Three rotary controls allow drivers to tune response, agility and traction.

The Response Control adjusts electric motor response to accelerator inputs; Agility Control modifies cornering behavior by varying torque distribution, enabling transitions from understeer to controlled oversteer. A nine-stage Traction Control system regulates slip, building on technology used in previous AMG models. These functions are fully available with ESP disabled, targeting track use.

The model also introduces AMG Performance 4MATIC+ all-wheel drive in an all-electric configuration, using three axial flux motors. The system enables fully variable torque distribution between front and rear axles, as well as torque vectoring across the rear wheels. Sensors monitor wheel slip and adjust torque delivery in real time to maintain traction and stability.

The braking system combines carbon-ceramic discs at the front with steel discs at the rear in a hydraulic composite setup. Mercedes-AMG says this delivers consistent pedal feel across regenerative and friction braking scenarios.

Suspension development has focused on the AMG Active Ride Control system, which uses interconnected hydraulic elements in place of conventional anti-roll bars. Combined with three-chamber air springs and adaptive dampers, the system enables a wide range between comfort and dynamic performance while reducing body roll and improving cornering precision.

The vehicle’s drive system features three innovative axial flux motors and directly cooled battery cells, and was also tested under Arctic conditions. The battery uses directly cooled cells grouped into laser-welded modules, with a non-conductive coolant circulating around each cell. This allows rapid heating to optimal operating temperature and efficient cooling under load, supporting repeated high-performance use.

Mercedes-AMG’s winter test program forms part of a broader validation process. The entire test program for validating a new model comprises over 500 individual tests, supplemented by the specific tuning of driving dynamics and the ESP system.

In related news, Mercedes-AMG leverages Track Sport Concept for next-gen GT3 and Black Series

The new steering feel benefits driving dynamics, maneuvering and parking. With steer-by-wire, the steering effort required from the driver can be further reduced and the driver no longer needs to adjust their grip on the steering wheel when turning. Vibrations caused by uneven road surfaces, which were previously transmitted to the driver via the steering wheel, can now be eliminated.

Steer-by-wire also reshapes the interior. A flatter steering wheel rim opens up space for the driver, improving their view of the driver display and making it easier to get in, or out.

Suspension specialists have tuned the steering ratio for different driving conditions, which works in conjunction with standard rear-axle steering – at higher speeds, the rear wheels steer in the same direction as the front wheels to improve stability.

The steer-by-wire system has completed over 1,000,000km of testing (over 621,000 test miles) on test benches, test tracks and in overall vehicle validation in road traffic. To ensure it meets the brand’s strict safety standards, the design uses a redundant system architecture in addition to high-precision sensors and powerful control units. These two signal paths ensure steering capability is always guaranteed. Lateral control is also possible through rear-axle steering and targeted wheel-specific braking interventions via the ESP.

In addition to the optional steer-by-wire system, the Mercedes‑Benz EQS continues to be equipped with electromechanical steering as standard.

Airbag structure and safety standards

In the EQS, Mercedes‑Benz uses the steer-by-wire technology for a flattened, more compact steering wheel and combines it with a newly developed airbag structure. Since the airbag can no longer support itself on a closed steering wheel rim, an internal support and folding architecture takes over the controlled shaping during deployment.

The airbag’s routing, folding pattern and mounting points are designed to ensure stable and consistent deployment, even without the upper steering wheel rim. The system remains integrated into the steering wheel hub and meets the brand’s safety requirements despite the revised design.

Related news, Rivian-Volkswagen joint venture completes SDV architecture winter testing

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

In related news, Agentic AI transforms McLaren Automotive’s entire engineering process

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.

Related news, Voluntary safety assessment by Einride published for cab-less autonomous heavy-duty trucks

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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The analysis covered Mazda vehicles from model years 2015 to 2023, examining six ADAS bundles and four standalone systems. Technologies assessed included automatic emergency braking, lane departure prevention, high beam assist and driver monitoring systems.

According to HLDI, larger bundles of safety features, typically incorporating newer-generation systems, were associated with greater reductions in claim frequency.

The most basic bundle, which includes front AEB with forward collision warning, reduced property damage liability (PDL) claims by 13% and bodily injury liability (BIL) claims by 9%. The most comprehensive bundle, adding features such as pedestrian detection, adaptive cruise control, rear AEB and driver attention alert, reduced PDL claims by 39% and BIL claims by 21%, although the latter was not statistically significant.

Two systems, front AEB with pedestrian detection and rear AEB, delivered the most notable additional reductions: updated AEB systems improved prevention of vehicle-to-vehicle crashes, while rear AEB was particularly effective in reducing low-speed parking collisions.

Standalone systems also showed a measurable impact. Blind spot monitoring combined with rear cross-traffic alert reduced PDL claim frequency by nearly 10% and BIL claims by 13%. Other features, including curve-adaptive headlights and head-up displays, were linked to smaller reductions. Traffic sign recognition did not show clear benefits in this dataset, which HLDI attributed to system limitations or lower adoption rates.

While some ADAS features were associated with increased claim severity due to the cost of replacing sensors, overall losses, combining claim frequency and severity, were generally lower for vehicles equipped with Mazda’s safety systems.

“Another important factor is that crash avoidance systems primarily eliminate crashes that occur at slower speeds,” said Matt Moore, chief insurance operations officer at HLDI and the Insurance Institute for Highway Safety. “That takes low-dollar claims out of the equation and skews the average cost upward.”

Mazda said the findings support its approach to integrating multiple safety technologies across its vehicle line-up.

“As this independent analysis demonstrates, continual improvement of driver assistance technologies has real world impact,” said Jennifer Morrison, director of vehicle safety strategy at Mazda North American Operations. “We remain committed to advancing both the performance and availability of these systems in our pursuit of zero fatal crashes.”

The study highlights the role of bundled and continuously updated ADAS features in improving real-world safety outcomes.

Related news, Mercedes-AMG leverages Track Sport Concept for next-gen GT3 and Black Series

The VSSA is intended to support transparency and regulatory engagement as Einride expands commercial operations. The company said it continues to work with the US Department of Transportation, National Highway Traffic Safety Administration and international authorities.

The document details the safety framework underpinning Einride’s autonomous technology platform, which combines its AI-based optimization software, Saga AI, with its in-house autonomous driving system, Einride Driver. These systems support the company’s Freight capacity as a Service and technology licensing offerings.

At the core of the VSSA is Einride’s cab-less, cargo-only electric truck, designed specifically for driverless operation. The vehicle incorporates redundancy across steering, braking, power, sensing and compute systems to enable fail-safe and fail-operational performance.

Einride’s safety approach is based on a documented safety case aligned with industry standards, including UL 4600, ISO 26262 and ISO/PAS 21448. This framework defines the vehicle’s operational design domain (ODD), performance requirements, fallback strategies and lifecycle safety processes.

The company states that its autonomous system combines machine learning-based driving with an independent deterministic safety layer. It also uses a redundant sensor suite including cameras, radar and lidar, alongside high-definition mapping.

The VSSA outlines procedures for minimal risk maneuvers and safe-state transitions when system or environmental limits are reached. Validation methods include simulation, hardware- and vehicle-in-the-loop testing, proving-ground trials and site acceptance testing prior to deployment.

Einride also describes its safety management system (SMS), which draws on practices from aviation and defense sectors. The system governs risk management, safety assurance, continuous monitoring and includes a fleet-wide grounding policy overseen by an independent safety and security function.

Additional areas covered in the assessment include cybersecurity and data protection aligned with ISO 21434 and ISO 27001, crashworthiness and post-crash behavior, event data recording, emergency response planning, and first-responder training.

In related news, eFreight Autonomous secures funding to explore the feasibility of autonomous HGVs on UK roads

“We are developing the most extreme Black Series ever,” said Michael Schiebe, a member of the board of management for production, quality and supply chain management at Mercedes-Benz Group AG, and chairman of the board of management of Mercedes-AMG. “At the same time, we want to set the next record-breaker in motorsport with the future GT3. The foundation for this is the Concept AMG GT Track Sport – a technology demonstrator that was more than just a concept from the very start. It is our unambiguous commitment to maximum performance, a promise for the racetrack and the road alike. At AMG, we develop vehicles to exceed expectations. And that is exactly what the future Black Series and the new GT3 will do.”

Uncompromising performance

The road-legal variant of the concept car serves as a homologation model for the GT3 successor.

Since October 2025, the prototype has been undergoing extensive dynamic testing on test tracks and racetracks. In addition to the in-house development track in Immendingen, this has included Bilster Berg, Portimão and Monteblanco. With the start of testing on the Nürburgring Nordschleife, the next stage of development is now underway.

The next generation in customer racing

The Mercedes-AMG GT3 is the latest step in Mercedes-AMG’s customer racing program, which began in 2010 and first competed in 2011 with the SLS AMG GT3. The current GT3 model, introduced in 2016 and updated in 2020, has continued that program.

A successor is now in development, led by Affalterbach Racing, a subsidiary of Mercedes-AMG, in collaboration with Mercedes-AMG Motorsport. The new GT3 is undergoing testing, with a focus on performance, safety and competitiveness.

“With the unveiling of the concept, we can now officially name the new Mercedes-AMG GT3, with which we aim to continue the success story in customer racing for Mercedes-AMG Motorsport,” said Christoph Sagemüller, head of Mercedes-AMG Motorsport. “Our goal is to once again present a vehicle that sets the benchmark. We have already gained important insights from the initial tests. We are now entering the next phase of development and will also be testing on racetracks relevant to GT Sport.”

From track to road

The Mercedes-AMG’s Black Series models – a designation introduced in 2006 – represent the highest-performance variants in the company’s range. The concept builds on AMG’s origins in motorsport. The company was founded in 1967 by Hans Werner Aufrecht and Erhard Melcher, with a focus on applying racing-derived technology to road cars.

The Mercedes-Benz SLK 55 AMG played a key role in this development. Its use as an FIA Formula 1 safety car and its adaptation for a racing series helped shape the track-focused approach that led to the first SLK 55 AMG Black Series.

This motorsport-to-road philosophy continues with newer concepts such as the AMG GT Track Sport, and underpins the development of future models, including the next AMG GT3 and AMG GT Black Series.

In related news, Mercedes-AMG Petronas F1 to use Microsoft cloud and enterprise AI technologies to enhance racing performance