Ever wondered how regenerative braking in hybrid and electric cars is calibrated by engineers? Or how the changeover from machine to human driver will be formulated in the future? These and many other vehicle development conundrums are being solved by technology developed by a company based in Hethel, Norfolk, UK – that’s not Lotus. Ansible Motion, formed in 2009 by Kia Cammaerts, already counts major North American and Japanese OEMs among a client base that also includes the added glamour of a raft of Formula 1 teams.
Ansible Motion was formed to provide state-of-the-art driver-in-the-loop (DiL) simulators that addressed the weakness of existing hexapod-based simulators that have existed since the 1950s. In the words of Cammaerts, “Hexapods have fundamental limitations in automotive use, because their motion space is limited due to their parallel construction you have to superpose different motions on all six actuators. For example, if you execute a braking manoeuvre the hexapod will move backwards to the the limit of its forwards and backwards motion, but then following on from that if you want to simulate turn-in you can’t as you have to unwind the actuators back to the point before the braking movement and then initiate the turn-in simulation. So you run out of motion space very quickly and the working motion space of a hexapod is typically 10-20% of its maximum motion.” With these known limitations, Ansible Motion worked on a clean-sheet design of a DiL simulator that would connect expert human drivers to a set of immersive technologies to better inform the experience of testing vehicle dynamics.
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Ansible’s simulator is centred on a model of the human vestibular system mated to industry-unique motion control systems. This is designed to stimulate the brain’s perception of movement, which is complex and inherently non-linear. Ansible Motion’s ‘stratiform’ motion system simplifies the actuation requirements by placing the cabin atop layers of precision-controlled actuators. The first stages provide ground plane cueing, while upper layers generate the pitch, roll and ride motions. This results in a much lower centre of gravity than hexapods, which means the forces are easier to manage. The focus on the human vestibular system is key as Cammaerts says, “With motion cueing, you can provide small cues to your vestibular system and transferring the larger motions to your imagination via vision tracking. So, if we turn-in, we can provide an onset of the turn-in motion, which is sensed very quickly by your brain, and then we render the turning as you carry around the corner on the screen, and your brain’s visual processing takes over and says, ‘Yes, I’m going round a corner’, and the motion sensors damp down and don’t provide much information and distraction to you. We can quietly remove that motion signal without you noticing. That’s vestibular concept cueing.”
At the moment, Ansible Motion has the “engineering-class” market to itself, leaving the Hethel-based company inundated with requests from both OEMs and Tier 1 suppliers keen to get their hands on the simulators. The company designs and manufactures the simulators itself, which are then accompanied by some third-party software tools and integrated by Ansible’s proprietary software toolchain. Lead time for a simulator is between six and nine months, with every simulator built by Ansible thus far being far from off-the-shelf.
The reasons demand is mounting for the simulators are two-fold. Firstly, the simulators help get products to market cheaper – with fewer development mules required – and faster due to the higher volume of experiments that can be run and the richer information that’s fed back to development engineers. Ansible Motion estimates that the return on investment horizon for one of their simulators is less than a year.
“What we’re dealing with is the perceptual threshold, which is a fraction of the service level you experience. What’s important is to deliver a signal that is above your perceptual threshold.” Kia Cammaerts
The increasing demand for the company’s solutions also stems from the three current and pre-eminent automotive megatrends: electrification, autonomous and connectivity – elements the company freely concedes weren’t really on their radar when its simulators were launched. As Cammaerts recalls, “Back then, our main use case was a racing application. That led to an observation that the human driving experience, even for super-skilled racing drivers, is governed by extremely low signals. You’d think that a driver capable at braking with 4G would laugh at a system that could deliver a fraction of that for a quarter of a second, but they’re fine with it. What we’re dealing with is the perceptual threshold, which is a fraction of the service level you experience. What’s important is to deliver a signal that is above your perceptual threshold.” In effect, the human mind scales the inputs from the simulator based on its perceptual threshold. Reaction to motion is very similar to that of hearing as it’s non-linear – hearing responds to anything from a whisper to a scream, despite large differences in the energy levels of the two extremes.
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By GlobalDataWhile motor racing use cases and, indeed, those for chassis engineering, require feedback from very skilled human drivers, those involving testing electrified vehicles, connectivity and autonomous driving do not. If, for example, you want to ascertain whether an ADAS system is helpful or irritating, that’s where Ansible Motion’s simulators come in – as cost-effective experiments can be set up with a sample of ordinary drivers in a way that can’t realistically be achieved at a proving ground. Feedback can be gathered from bio-signals in the human drivers such as skin temperature, heart rate and breathing rates. In the case of electrified vehicles, one example is very much at the forefront of demand for the simulators states Cammaerts, “The switchover from regenerative to foundation braking, the handover if you’ve got electric and internal combustion prime movers, and the ability to switch between them, how that feels, and how that interacts with other driver operations such as gear changes, throttle changes and application of brakes is increasingly important. While those can be studied in great detail in offline simulations, or in desktop simulations where a human is in the loop, so to speak, with a gaming steering wheel and gaming pedals and looking at a monitor, it is surprisingly difficult to engineer a quality feel from a system, from a complex system, without actually getting feedback from a human who’s dynamically attached to that powertrain.”
While autonomous cars are undoubtedly on the horizon, two factors are often overlooked: travelling in vehicles often induces motion sickness and, because of differences in vehicle manufacturers’ brand attributes, autonomous driving will not be delivered homogeneously. On the first point Cammaerts observes, “At the moment studies into the causes of nausea in autonomous vehicles have not been done. But we can look at the mitigations that are available for autonomous-induced motion sickness. You can interactively moderate the driving style depending on the human requirements at the time – for example, do people experience more discomfort when looking at their phones? Can we moderate the acceleration profile to work around the discomfort? Or are there other cues such as visual, haptic, scent or aural that we can introduce to the cabin to mitigate nausea? There’s a world of research to be done on that.”
The autonomous car as chauffeur will define, in large measure, the brand experience of the vehicle.
Regarding the autonomous drive experience being far from homogeneous, Cammaerts presents a persuasive argument, “The autonomous car as chauffeur will define, in large measure, the brand experience of the vehicle. Firstly, there’s the safety aspect. If one brand is perceived as safer than another it will drive sales. Second, there’s the actual perception of the autonomous drive experience and how that fits with brand perceptions.”
How automakers manage to maintain their dominant position in the automotive value chain, while embracing the opportunities that new mobility business models, autonomous driving and electrification are presenting will be key to their future success. Instilling some kind of tangible brand recognition into how brands handle electric and autonomous driving is one way they’ll keep their position at the top of the pyramid. There may not be any instant answers to this seemingly intractable problem for the automakers, but it does seem that Ansible Motion can provide at least part of the solution.
