More than 250 solution providers are expected on the show floor, with exhibits spanning vehicle testing and validation, ADAS and autonomous vehicle development, NVH measurement and analysis, emissions and powertrain testing, simulation and virtual development tools, and data acquisition and test automation. As part of Vehicle Tech Week North America, visitors also gain access to two additional events, Intelligent Vehicle Expo and Automotive Design & In-Cabin Expo, broadening the range of opportunities for cross-disciplinary conversations across the vehicle development lifecycle.

The Future of Automotive Testing Conference 

Now in its third year, The Future of Automotive Testing Conference expands to a full three-day, free-to-attend program in 2026 and brings together perspectives from OEMs, suppliers, testing laboratories, research institutions and regulatory bodies to discuss where testing is headed next.

Here, Automotive Testing Technology International editor Rachel Evans highlights the key sessions she is looking forward to attending at the show.

Thermal management is becoming a central part of vehicle architecture as OEMs transition toward hybrid and electric powertrains. In the ‘Thermal management: A fireside chat‘ session on day one of the show, experts from Ford, General Motors and Stellantis will explore how thermal systems are evolving beyond conventional cooling to manage energy flow across the vehicle. Their discussion will cover the role of simulation, validation and testing in developing more efficient thermal strategies, with a focus on maximizing range and safety. Speakers include Gursaran D. Mathur of Ford, Sowmya Jayaraman of General Motors and Aamir Khawaja of Stellantis.

In the ‘Closed-Loop Physics & Actuation – Bridging High-Fidelity Vehicle Dynamics with SIL/HIL Architectures‘ panel on day two, experts from Zoox, General Motors, IPG Automotive and Cayman Dynamics will examine the challenges of bringing high-fidelity vehicle dynamics into SIL/HIL testing. The discussion will cover real-time tire modeling, latency in steer-by-wire and brake-by-wire systems, active suspension and autonomous motion planning, and validating vehicle stability before physical track testing.

In day two’s ‘Why ECU reliability testing misses real humidity exposure‘ session, Tesla will explore why conventional ECU qualification testing can fail to replicate the humidity exposure vehicles experience in the real world. Field reliability engineer Shrikant Pawar will examine how slow, cumulative moisture exposure can contribute to electrochemical migration and dendritic short circuits in PCBs, despite components passing standard humidity and thermal testing.

Rachel’s final pick is the ‘Model-in-the-loop testing for reliable automotive control software‘ session on day three, which will explore how MIL testing can identify control logic and requirements issues before software reaches an ECU or test vehicle. Caterpillar‘s lead controls engineer Anusha Pothineni will discuss how simulation can be used to test normal, boundary, fault and safety-related conditions, with practical examples of model-based control software.

See the full conference agenda here.

Vehicle Tech Week North America 

This year’s expo marks the debut of Vehicle Tech Week North America, which brings three specialist conferences under one roof: The Future of Automotive Testing Conference, Intelligent Vehicle Conference and Automotive Design & In-Cabin Conference. Attendees will be able to attend sessions in their discipline while also dipping into adjacent industry topics.

Free expo passes for Automotive Testing Expo North America, which include access to the exhibition and the full conference program, are available now at testingexpo-usa.com.

Vehicle Tech Week North America 2026 will take place at the Vibe Credit Union Showplace in Novi, Michigan, on October 27-29, 2026.

The work is the first application of Project Coeus and is being carried out by Perkins’ Customer Machine Engineering Team at its research and development facility in Peterborough. The demonstrator will undergo testing at Perkins’ proving site to assess the technology, including its ability to meet the machine’s peak load demands and transient response requirements.

At the core of the hybrid power unit is an entirely new spoke architecture heavy-duty electric motor, custom designed and developed in-house at Equipmake’s facility in Snetterton in Norfolk, UK. The company has been responsible for integrating this new motor-generator unit (MGU) – which including the company’s silicon carbide inverter technology and all associated power electronics – with a spark-ignited Perkins engine to boost performance. Based on the Perkins 1200 Series platform, the 1206 hydrogen hybrid IOPU delivers a combined 397kW (508hp) and 1,938Nm at 1,200-1,500rpm.

Packaging this level of electrified performance in a unit designed as a direct replacement for an existing diesel IOPU is a key aim of the project. The plug-and-play solution is intended to help off-highway OEMs transition to lower-carbon fuels and electrification without requiring fundamental changes to their machines.

Led by Perkins, a wholly owned subsidiary of Caterpillar, Project Coeus is a three-year program involving Perkins, Equipmake and Loughborough University’s Wolfson School of Mechanical, Electrical and Manufacturing Engineering. The project received £11.14m (US$14.78m) in UK government funding through the Advanced Propulsion Centre (APC) in 2023, including £3.24m (US$4.3m) awarded to Equipmake.

The project focuses on four fuels – ethanol, methanol, biomethane and hydrogen – all of which use spark ignition. Perkins is developing a configurable spark-ignited combustion platform on which the hybrid system is based.

Ian Foley, Equipmake CEO, said, “This is a proud moment for everyone at Equipmake. Our role in Project Coeus has been to deliver a custom-designed heavy-duty electric motor – paired with our own silicon carbide inverter technology and all associated power electronics – and integrate that with a Perkins engine. The resulting hybrid industrial open power unit can cope with the extreme demands of off-highway applications and is packaged so it can sit within a genuine drop-in replacement for a diesel power unit. Moving from the test cell into a machine as demanding as a high-performance shredder is exactly the proving ground this technology needs.

“Off-highway OEMs want to decarbonize without compromising productivity or re-engineering their machines from scratch, and hybridization combined with lower carbon fuels such as hydrogen offers a practical route to doing that. This milestone shows the strength of UK engineering collaboration, and how the electrification expertise we have built in sectors such as bus and coach can be applied to the global off-highway market.”

EXPLORE, Why subsystem obsession gets in the way of developing great vehicles

The difficulty is that these characteristics are not quantifiable engineering targets. They are descriptions of an intended customer experience. Unless they can be translated into measurable, technical requirements, they remain little more than aspirations.

This is where attribute target cascading comes in, enabling engineers to translate the desired characteristics of the vehicle into usable, measurable targets. Using our own in-house-developed software tools, we convert subjective attribute requirements into objective engineering targets. This provides the link between what a manufacturer wants customers to experience, and the requirements engineers must deliver at vehicle, system and component level. This addresses one of the most persistent problems in vehicle development: optimizing subsystems at the expense of the complete product. And with greater vehicle complexity, this issue is becoming more prevalent among automotive, aerospace and defense OEMs alike.

Engineering teams naturally focus on their own areas of expertise. Steering engineers, suspension specialists and tire suppliers all tend to focus on their own areas of the vehicle. The development of these systems in isolation may seem rational when viewed at a component or technology level, but customers never experience a steering system, damper or tire independently. They experience the finished vehicle.

The automotive industry has developed a comprehensive range of tools and techniques to measure vehicle performance. The challenge in a time-constrained development plan is ensuring that the right things are being measured at the right point in the process.

The attribute target cascade process addresses this challenge by establishing a clear hierarchy of intent. High-level corporate and customer-led attribute requirements are translated into full vehicle targets, which are cascaded to the subsystem and component level. The process is evident in the way manufacturers preserve distinct product characteristics across generations of vehicles. A brand renowned for driver engagement will prioritize attributes such as steering precision, transient response and body control. A manufacturer focused on refinement may place greater emphasis on isolation, stability and ride quality. These characteristics do not emerge by chance. They result from thousands of engineering decisions aligned around clearly defined objectives.

The vehicle dynamics attribute illustrates this point well. Ride, handling and steering feel are often discussed as separate attributes, yet engineers understand that they are inseparable. Suspension kinematics can influence steering performance. Compliance affects both ride quality and handling precision. Tires shape transient response and refinement simultaneously. Increasingly, software determines how these systems interact.

As a result, vehicle development is less about achieving perfection within individual systems and more about managing trade-offs intelligently. The objective is not to maximize every performance metric. It is to deliver the right balance of characteristics for the intended application.

This challenge is becoming more acute. Electrified powertrains, advanced driver assistance systems (ADAS) and software-defined architectures have introduced additional layers of complexity into the vehicle. Development teams have become more specialized, while simulation tools allow decisions to be made earlier than ever before. These advances undoubtedly improve efficiency, but they also increase the risk of local optimization. Sophisticated simulation cannot compensate for poorly defined targets. If the attribute targets are wrong, higher-fidelity models simply enable engineers to arrive at the wrong answer more quickly.

The same caution applies to benchmarking. Competitive analysis remains an essential development tool, but copying isolated performance metrics without understanding the broader vehicle concept can be counterproductive. Characteristics that contribute positively within one architecture may undermine another.

Successful products are rarely defined by the excellence of individual components alone. They succeed because every subsystem contributes to a set of coherent overall objectives. As vehicles continue to evolve, that principle will become increasingly important. The temptation will be to optimize whatever can be measured. The discipline lies in remembering what customers actually perceive.

Great cars are not created by assembling outstanding components. They emerge when every engineering decision supports a clearly defined product vision. In an era of increasing technical complexity, maintaining that systems-level perspective may prove to be one of the industry’s most valuable engineering capabilities.

EXPLORE: Rethinking NVH testing for high-performance EVs

Most recently, he was technical director of Audi Sport’s Dakar project, where he was responsible for the technical development of the Audi RS Q e-tron program. The vehicle won the 2024 Dakar Rally overall with Carlos Sainz and Lucas Cruz.

Prior to Audi, Pascali held senior engineering positions at McLaren Automotive, Porsche and Centro Ricerche Fiat, building extensive experience in vehicle dynamics, chassis development and advanced vehicle technologies. During his time at McLaren Automotive, where he served as head of chassis and vehicle technology, Pascali was named vehicle dynamicist of the year 2017 by Vehicle Dynamics International.

Development and validation strategy 

In his new role, Pascali will lead VI-Grade’s focus on full-vehicle development and validation, with its roadmap placing greater emphasis on software-defined vehicles, ADAS, energy management and integrated electronic systems.

The company plans to combine Software in the Loop (SiL), Hardware in the Loop (HiL) and Driver in the Loop (DiL) technologies, linking virtual models and driving simulators with physical electronic control units and vehicle systems. The approach will enable OEMs to validate software releases, assess system interactions and evaluate vehicle behavior from the driver’s perspective.

“I am excited to join VI-grade at a time when the way vehicles are developed is undergoing such a fundamental transformation,” said Pascali. “Working closely with test drivers has taught me that a vehicle cannot be judged through isolated measurements alone. Objective data and simulation are essential, but so is the driver’s assessment of how every system works together. That is why VI-grade’s human-centric, full-spectrum approach resonates with me. I see a major opportunity to bring the complete driving experience into virtual development and to test real electronic control units together with virtual vehicle models through Hardware in the Loop. Combining these capabilities with Driver in the Loop and physical testing can help our customers make more confident decisions earlier in development.”

Pascali’s engineering approach has included close collaboration with professional test drivers, combining objective engineering data with subjective driving assessments. In previous vehicle development roles, his own evaluations, alongside those of the chief test driver, contributed to final vehicle sign-off.

This approach aligns with VI-Grade’s focus on evaluating vehicles as integrated systems and considering the overall driving experience across different engineering disciplines.

“Leonardo brings an exceptional combination of deep engineering expertise, international leadership and first-hand experience developing advanced road and competition vehicles,” commented Alessio Lombardi, global sales director – simulation, VI-Grade / HBK.

“His understanding of both virtual and physical vehicle development makes him ideally positioned to lead VI-grade into its next phase and further strengthen the role of simulation within HBK’s broader Smart Testing strategy. We are delighted to welcome him to the team.”

Explore, Inside the engineering challenge of FMVSS 127

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

Cover story: Rethinking female occupant protection: The THOR-5F is the first commercially available ATD designed from the outset around full female anthropometrics. What impact will it have on the way female occupants are assessed?

OEMs navigate Euro NCAP 2026: At this year’s Automotive Testing Expo Europe in June, Ford and Nissan execs discussed how they’re handling the expanded protocols

DAQ, management and analysis: With the destructive nature of passive safety testing limiting the opportunities to acquire data, the pressure to get it right is immense

AI-backed simulation: With developers today facing immense pressure, many of them are embracing artificial intelligence with open arms. How is it being exploited in the safety sphere?

EV simulation challenges: Cell-to-body structures are calling for novel simulation approaches to scrutinize their crashworthiness, as CTTI learns

Camera development and integration: CTTI investigates how much more performance can be extracted from cameras, and how the equipment is being integrated into today’s increasingly complex evaluation setups

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Download the Automotive Testing Technology International App NOW to read the latest issue! The App is free and gives you access to a library of back issues.

Federal Motor Vehicle Safety Standard No. 127 is the USA’s first mandatory federal standard requiring automatic emergency braking (AEB) on all new light vehicles. Finalized by the National Highway Traffic Safety Administration (NHTSA) on May 9, 2024, with compliance required from September 1, 2029, FMVSS 127 is described as one of the most stringent mandatory AEB standards enacted or proposed in a major vehicle market.

The white paper covers six key areas: the regulatory requirements of FMVSS 127; the engineering challenges associated with meeting those requirements; the political and legal landscape at the time of publication; the economic cost-benefit analysis presented by regulators and challenged by industry; comparisons with equivalent international requirements; and potential compromise positions that could support future rule-making.

Key findings

FMVSS 127 introduces requirements that exceed those of existing mandatory AEB standards globally, including complete collision avoidance at 100km/h, night-time pedestrian detection using low-beam illumination only, and a 100% pass rate across individual test runs.

NHTSA’s 2023 pre-rule testing found that four of five production vehicles failed at least two test scenarios, primarily those involving night-time pedestrian AEB. One vehicle, the 2023 Toyota Corolla Hybrid XLE, passed all scenarios, providing evidence that the requirements can be achieved in production vehicles.

The paper argues that compliance could require sensor-fusion architectures beyond current camera-radar systems, with thermal infrared cameras identified as a potential near-term technology for improving night-time pedestrian detection.

The rule is also subject to an active legal challenge in the US Court of Appeals for the DC Circuit by US trade association and lobbying group Alliance for Automotive Innovation, filed in January 2025. At the time of writing, the September 1, 2029 compliance deadline has not been formally altered, although its future remains subject to legal and regulatory developments.

NHTSA’s Final Regulatory Impact Analysis estimates a total annual compliance cost of approximately US$354m, with a cost per statistical life saved of approximately US$978,000 against a federal Value of Statistical Life (VSL) of US$11.6m in 2023 dollars. Industry estimates, based on the additional multi-sensor hardware potentially required, place compliance costs as high as US$4,200 per vehicle, equivalent to approximately US$63bn annually across the US fleet.

The paper also identifies several potential compromise positions, including a statistical pass rate across multiple test runs, a phased approach to night-time pedestrian AEB implementation and speed-tiered performance thresholds. These approaches could provide alternatives for future rule-making while retaining the core safety objectives of the standard.

Regardless of the final form of FMVSS 127, the paper highlights the increasing demands being placed on AEB systems, particularly at higher speeds and in low-light conditions. This is likely to have implications for the sensor technologies, validation infrastructure, test targets and data-acquisition methods used to develop and verify future ADAS.

Download Hulshof’s FMVSS 127 white paper here

Read more on FMVSS 127 in Sensing Change in the April 2025 issue of ATTI‘s sister title, ADAS & Autonomous Vehicle International

Under the agreement, ProLogium’s Gen4 battery technology will undergo electrical, thermal and safety testing at Mercedes-Benz facilities and specialist external testing institutes. The results will be used to assess the technology’s suitability for potential integration into future vehicles.

ProLogium’s Gen4 battery platform is designed around five core attributes: safety, performance, energy density, scalable manufacturing and cost competitiveness. Its safety architecture combines a non-flammable inorganic electrolyte, ceramic separator technology and ProLogium’s proprietary active safety mechanism. The platform is also designed to support fast charging, high power output and performance at low temperatures.

Vincent Yang, founder and CEO of ProLogium, said, “Over nearly a decade, our collaboration with Mercedes-Benz has evolved from early cell development and validation to testing our latest Gen4 technology for future vehicle applications. For next-generation batteries, true value comes from turning years of innovation into scalable manufacturing and commercially viable products. With Mercedes-Benz’s early investment and support for our European expansion, together with the strong support of the French government and the European Union, we are advancing toward large-scale production in Europe and building a manufacturing footprint to serve global markets.”

“At Mercedes-Benz, we are committed to shaping the future of electric mobility through technological leadership and innovation. Our long-standing partnership with ProLogium is a strong example of how we bring the best expertise together to advance next-generation battery technologies,” said added Jörg Burzer, member of the board of management, Mercedes‑Benz Group, chief technology officer, development and procurement. “By combining ProLogium’s pioneering solid-state battery approach with Mercedes-Benz’s deep battery development, testing and integration capabilities, we are taking the next step in exploring advanced battery technologies for future electric vehicles.”

Since 2016, the partnership between the two companies has progressed from early hybrid solid-state Gen3 development and joint testing to potential future vehicle platform planning. Mercedes-Benz has also become a long-term global automotive partner and strategic investor in ProLogium, extending the relationship beyond product validation.

The collaboration has included validation of multiple lithium-ceramic battery formats, including pouch cells and ProLogium’s next-generation prismatic cells, as well as its Bi-Polar technology.

ProLogium’s Taoke Gigafactory in Taiwan has completed GWh-scale manufacturing validation and supports C-sample production, Demo Car programs and future commercial production.

ProLogium’s planned Dunkirk Gigafactory, with a future capacity of up to 44GWh, is expected to establish localized European manufacturing and supply-chain capacity, replicating the proven production model developed in Taiwan. Once the first phase of this factory is completed, ProLogium will be able to provide commercial cells to European customers at a scale large enough to support electric vehicle platforms on the continent.

Related news, IAAPS integrates DiL simulator with 1,800kW powertrain test facility

Artificial intelligence is becoming an integral part of automotive development, promising faster test generation, automated analysis and more efficient validation workflows. Yet as vehicles become increasingly software-defined, the real challenge is no longer adopting AI, it is ensuring AI can be trusted within safety-critical engineering processes. The next evolution of automotive testing will depend on combining AI with engineering context, governance and end-to-end traceability.

The automotive industry is undergoing a profound transformation. Software-defined vehicles, ADAS, electrification, connected services and over-the-air updates have dramatically increased software complexity while shortening development cycles. Every software release must be validated against thousands of requirements across multiple vehicle domains and stringent functional safety and cybersecurity standards. As a result, verification and validation (V&V) teams are under growing pressure to accelerate testing without compromising quality or compliance.

Artificial intelligence is helping address this challenge. Engineers are already using large language models to review specifications, generate test cases, summarize technical documentation and support engineering analysis. While these capabilities improve productivity, they expose a critical limitation: generic AI understands language but not the engineering relationships that determine whether a vehicle is safe, compliant and ready for release.

AI needs engineering context

Automotive testing is built on traceability. A seemingly small software modification can affect requirements, system architecture, safety goals, cybersecurity controls, verification procedures and regression test suites. Determining those impacts requires understanding how engineering artifacts are connected throughout the product lifecycle.

Generic AI has no inherent awareness of these relationships. It may recommend a new test case or rewrite a requirement, but it cannot determine whether verification remains complete, whether a safety requirement is still satisfied, or whether a change introduces gaps in compliance. Those answers exist within the engineering lifecycle, not inside the AI model.

This challenge is becoming even more significant as SDVs receive continuous software updates throughout their lifetime. Verification teams need AI that can reason using trusted engineering information rather than isolated documents or manually supplied prompts.

Engineering intelligence for modern verification

This need is driving the emergence of engineering intelligence, an approach that combines AI with engineering knowledge, lifecycle relationships and traceability.

Instead of treating AI as a standalone assistant, engineering intelligence connects it to requirements, risks, test cases, defects, software changes and verification evidence. AI can then analyze engineering information in context, helping teams identify affected test cases, locate missing traceability, evaluate change impacts, and detect potential coverage gaps before they become costly defects.

One area where this delivers immediate value is regression testing. As software updates become more frequent, rerunning every test after every change is increasingly impractical. AI that understands engineering relationships can prioritize regression testing based on actual system impact, reducing unnecessary validation effort while maintaining confidence in software quality and functional safety.

Engineering intelligence also improves collaboration across systems engineering, software development, testing, cybersecurity and functional safety. Instead of searching across disconnected tools, teams gain faster access to the engineering knowledge needed to make informed decisions.

Trust requires governance

For safety-critical automotive systems, trustworthy AI depends as much on governance as it does on intelligence.

Standards such as ISO 26262, ASPICE, ISO/SAE 21434, and ISO 21448 (SOTIF) require engineering decisions to remain traceable, reviewable and supported by objective evidence. AI-generated recommendations must therefore remain linked to authoritative engineering data and subject to human review.

Organizations also need clear policies defining what information AI can access, how engineering data is protected, and how AI-generated outputs become part of existing approval workflows. Human oversight remains essential, not because AI lacks value, but because engineering accountability cannot be delegated.

Connecting AI to the engineering lifecycle

Providing AI with meaningful engineering context requires secure access to lifecycle information rather than isolated documents. Technologies such as the model context protocol (MCP) provide a standardized way for AI systems to retrieve governed information directly from engineering applications.

Instead of manually copying requirements, test specifications or safety analyses into external AI tools, engineers can allow AI to securely access approved engineering information while respecting permissions and organizational governance. This enables AI to analyze requirements alongside verification evidence, understand relationships between tests and risks, and support impact analysis without disrupting established engineering workflows.

Engineering intelligence in practice

Engineering Intelligence is already moving from concept to implementation. Visure Solutions supports this approach through Vivia, its AI assistant, and the Visure MCP Server.

Vivia provides contextual AI assistance directly within the engineering lifecycle, helping teams improve requirements quality, analyze traceability, assess change impacts and support verification activities using the engineering information already available within the project.

The Visure MCP Server extends these capabilities by securely connecting enterprise AI platforms to governed engineering information managed within Visure. Rather than relying on isolated prompts, AI retrieves trusted lifecycle data directly from engineering repositories while respecting organizational permissions, approvals and audit requirements. This enables organizations to adopt their preferred AI technologies while ensuring responses remain grounded in authoritative engineering knowledge.

Looking ahead

The future of automotive testing will not be defined simply by faster automation or more powerful AI models. It will be defined by how effectively AI can support engineering decisions within the rigorous processes that ensure vehicle safety, quality and compliance.

Engineering intelligence provides that foundation by combining AI with engineering context, traceability, governance and human oversight. For automotive verification teams, this means faster impact analysis, smarter regression testing, higher-quality requirements and more efficient validation, all while preserving the discipline required to develop safe, reliable software-defined vehicles.

Watch this webinar on engineering intelligence: High-Velocity Engineering with Visure MCP: AI Agility, Traceability & Governance

Read more about Virtual Vehicle Research’s SDV Virtual Assurance Framework in the September edition of ATTI

IAAPS will integrate an Ansible Motion Delta S2 simulator with its 1,800kW four-wheel-drive powertrain test facility, bringing simulated vehicle behavior, human driver inputs and physical powertrain hardware together within a single real-time development environment. This will enable complex development and validation work to begin earlier, helping to reduce technical risk, physical prototype testing requirements and overall development time.

The simulator has already supported race preparation involving F1 Academy driver Rachel Robertson. IAAPS is now engaging with organizations across motorsport, automotive and the wider transportation sector to realize the capability’s broader potential.

For motorsport customers, the simulator supports vehicle setup optimization, calibration, strategy development and scenario testing. Drivers can also use the platform for track familiarization, race procedure practice, coaching and consistency training.

By evaluating more options before arriving at the circuit, teams and drivers can make better engineering decisions while reducing their use of valuable track time, tires and vehicle mileage. The controlled environment can also help develop driver confidence and improve communication between drivers and engineers.

For motorsport teams and technology partners 

The new IAAPS DiL simulator gives motorsport organisztions and individual competitors access to professional simulation tools and engineering support without the cost and complexity of operating their own simulator facility.

IAAPS also provides integrated engineering services covering real-time simulation, x-in-the-loop methodologies, physical testing, control systems, data analysis and vehicle development.

“This capability gives customers a flexible platform for a diverse range of applications, from race preparation and driver optimization to automotive and wider transportation development,” said Professor Rob Oliver, managing director of IAAPS.

“By combining simulation with our advanced facilities and engineering expertise, we can help customers develop and validate complex systems earlier, more efficiently and with reduced technical risk.”

For OEMs and technology suppliers 

For automotive manufacturers and technology suppliers, the DiL simulator accelerates vehicle development by moving testing and validation into a controlled, rig-based environment earlier in a program.

Driver behavior, propulsion system response and wider vehicle functions can be evaluated before a complete physical prototype is available. This supports more efficient development and validation of internal combustion, hybrid and battery electric vehicles while reducing dependence on physical prototypes and on-road testing.

The Delta S2 simulator features a six-degrees-of-freedom motion base, an immersive projection-based visual system and an interchangeable cockpit. It forms part of IAAPS’s broader portfolio of research, engineering and testing services for the development of future propulsion systems, technologies and advanced validation methods.

Recent news, Holon joins project to help with AV operating area approval bottlenecks

For most of my career, my job had a very simple definition of success: less. Less noise, less vibration, less harshness. And we got extremely good at it. Sound pressure level, loudness, sharpness, roughness, transfer path analysis – mature, standardized, reliable tools, all built to answer one question: how much unwanted sound is there, and where is it coming from?

Then the engine went away, and so did the premise. In a high-performance EV, a lot of what the customer hears is there because we decided to put it there. Sound stopped being a defect and became a feature. The moment that happened, we inherited a problem I don’t think our profession has really faced up to yet.

Here’s the simplest way I can put it: excitement has no unit of measurement.

I can measure an EV’s active sound at 60km/h to ¹⁄₁₀ of a decibel. I can give you its loudness, its sharpness, its tonality, its order content. What I cannot give you is whether the driver felt anything. When we were developing the driving sound for the Ioniq 5 N, there was no target to converge on, because none exists. We weren’t minimizing a quantity, we were trying to provoke a reaction. Normal NVH work hands you a number and a direction. This hands you neither.

That is hard enough. But there’s a second issue underneath, and I think it’s the more serious one.

Our instruments measure channels separately. A microphone takes the sound. An accelerometer takes the vibration. Vehicle dynamics and powertrain control sit in different data streams, usually looked after by different teams, quite often in different buildings. Each domain gets validated against its own targets and signed off on its own terms. But the person in the driver’s seat doesn’t experience channels. They experience one event.

This isn’t a philosophical point. When we were integrating sound with virtual gearshift and haptic feedback, what separated a convincing shift from an unconvincing one usually wasn’t the quality of any single element – each was excellent on its own. It was the alignment between them. Let the audible event and the physical one drift apart by a few tens of milliseconds and the whole illusion falls over. The driver can’t tell you what’s wrong. They just say it feels fake.

Now think about how that gets caught in a normal test campaign. The sound passes. The haptics pass. The control strategy passes. Every domain reports green, and the product is still wrong. We validated the parts and missed the thing we were actually selling.

So if I could change two things, they would be these. First, treat synchronization as something you measure. As far as I know, the timing relationship between sound, haptics and vehicle behavior is almost never written down as a requirement with a tolerance, instrumented, and signed off like any other engineering target. If the multisensory experience is the product, that relationship is a specification, not an artistic detail, and it belongs in the test plan with a number attached to it.

Second, start recording what people say. There is a lot of enthusiasm at the moment for AI that can predict perceived quality, and I share it – analysis of driver reactions, biometrics and expression will eventually give us a much better read on emotional response than we have today. But an algorithm can only learn a correlation someone bothered to record. In most organizations the objective data is archived beautifully and the human verdict that went with it isn’t archived at all. That is the real bottleneck. It isn’t the state of machine learning, it’s that we aren’t collecting the data those systems will need.

None of this needs technology that doesn’t already exist. It mostly needs a decision that the subjective half of the job deserves the same discipline we have always given the objective half.

We spent several decades getting very good at measuring what we wanted to remove. I suspect the next decade will judge us on how well we learn to measure what we chose to create.