In today’s Formula 1 landscape and indeed across other top-tier motorsport series, driver-in-the-loop (DIL) simulators have evolved from peripheral driver training tools into mission-critical components of performance development, writes Ian Haigh, solutions group manager at Ansible Motion
Just a decade ago, DIL simulators were used primarily for driver circuit familiarization and rudimentary setup work. Now, they serve as tightly integrated, virtual race circuits, capable of simulating aerodynamic upgrades, fine-tuning chassis setups and validating race strategies with remarkable fidelity.
In an era of restricted track testing and ever-increasing vehicle complexity, these simulators enable teams to iterate rapidly, with controlled, repeatable conditions that mirror real-world racing scenarios. Just look at how DS Penske in Formula E is embracing multi-sim setups to consider drag and other inter-car factors.
But as engineers in the paddock know well, deploying a DIL simulator that actually delivers usable correlation is no easy feat. When it works, the rewards are substantial. When it doesn’t, the pitfalls can be costly.
A public admission
Adrian Newey’s recent appointment at Aston Martin Aramco Formula One Team as managing technical partner has come with characteristically candid assessments. Among the topics he’s addressed openly has been the shortcomings of the team’s DIL simulator program. Newey said the following to The Race at the Monaco Grand Prix in May 2025:
I think it is fair to say that some of our tools are weak, particularly the driver-in-the-loop simulator. It needs a lot of work because it’s not correlating at all at the moment. It is a fundamental research tool – not having that is a limitation. But we’ve just got to work around it in the meantime and then sort out a plan to get it to where it needs to be. But that’s probably a two-year project in truth. So, we’re going to be a bit blind on that for some time. We have just got to try to use experience and best judgement. How successful that will be, time will tell.
He added that while this two-year recovery process is underway, the team would have to lean heavily on engineering intuition and experience:
You can have the best motion system in the world, but if you don’t have the modeling to go with it, and correlation with the aero model, correlation with the tire model and so on, it won’t be of any use.
Motion commotion
While specific details about the simulator in question remain undisclosed, from our vantage point [at Ansible Motion], it pains us to hear comments such as Newey’s because we understand from experience, the scale of the challenge the team is facing. It seems that many early-stage assessments of DIL simulators focus on motion systems as defining the capability of a DIL simulator. Newey’s comment about “You can have the best motion system in the world, but if you don’t have the modeling to go with it … ” speaks directly to this point and it may indicate that the team fell into this subtle trap prior to Newey’s arrival.
Motion machinery is certainly a visually dominant part of a driving simulator installation and we know that automotive engineers naturally gravitate toward motion systems because they are more “relatable” than many of the other DIL simulator systems. But while it is certainly important, motion capabilities do not singularly define the quality of a driving simulator or its capacity for correlation.
DIL immersion is a multi-sensory endeavor and motion is just one of the players. In other words: motion is but one sub-category of one type of sensory stimulation that addresses just one category of human senses. If we stand back to look at DIL simulators from a big picture perspective, across the entire spectrum of correlation requirements, is motion cueing any more or less important than vision cueing, or auditory cueing or any other type of sensory cueing? Probably not. All of these sensory triggers must work together seamlessly, in a convincing way for driver and engineer.
A moving experience
The purpose of a driving simulator’s motion machinery is not to move a car around or to replicate the motions of a real car. The purpose of the motion machinery is to move a human being around, to convince them that they are interacting with a real car. This is an entirely different task.
DIL simulator correlation – which Newey correctly identifies as the cornerstone to making progress – can therefore be a trickier proposition than might first be imagined. It starts by viewing the simulator as a whole – not just as a motion machinery kit – in the context of measurable driver interactions. We speak from experience on this, as we’ve been tackling it for over 15 years.
When we talk about correlation in DIL simulation, it’s not just about ensuring that the physics model aligns with telemetry. It’s about ensuring that the driver’s physical and cognitive responses in the simulator align with those in the real world.
From concept to practice
To make DIL simulators viable tools for strategic decision-making, whether for evaluating tire degradation, energy deployment or aerodynamic tweaks, teams must go well beyond basic hardware benchmarking. Engineers must grapple with multiple data streams, for example:
- Simulator platform telemetry – useful for understanding motion/vision cue tuning, but isolated from track-based comparison unless multiple simulators are in play.
- Vehicle model outputs that can be compared to track data – such as velocity, longitudinal/lateral accelerations.
- Model outputs that cannot be directly measured on track – for example, slip angles, damper loads.
- Supplementary data – driver biometrics and neurophysiological feedback.
This makes DIL simulator correlation an inherently layered task. Newey is absolutely correct to say that modeling fidelity and how each sub-model integrates, is paramount. A tire model that is validated in isolation can unravel in the simulator if it doesn’t harmonize with the road surface or contact patch behaviour.
Real-world strategies for virtual validation
In a recent, separate blog article, titled F1 Driving Simulator Correlation, we mention a few practical techniques that have been shown to deliver results for race teams aiming to sharpen their simulator performance, and we discuss nuances such as the difference between frequency response and bandwidth.
DIL simulation is not plug-and-play. It requires system-level thinking, an understanding of human sensory integration and a relentless focus on correlation across every component – from aero to tires, from motion to perception.
While Aston Martin’s current challenges are very public, they are by no means unique. But with the right investments – whether that’s in people, tools and process – they’re certainly solvable. After 15 years of focusing purely on this, we know that mastering the virtual world is often the key to mastering the real world.
Read the full article on the Professional Motorsport World website.
