Arm Viewpoints: Aston Martin Formula One™ Team’s Charlie Blackwall on racing electronics (part 1)

Brian: [00:00:00] Hello, and welcome to a special episode of the Arm Viewpoints Podcast. I’m Brian Fuller, editor in chief at Arm, and today we’re sharing the first of the two-part conversation involving Charlie Blackwell, head of electronics for the Aston Martin Aramco Formula One team, and John Kourentis, Arm’s director of automotive go-to-market.

This was recorded live during a fireside chat this summer in Cambridge. It’s a fascinating deep dive in automotive electronics in one of the world’s most exciting sports F1 racing. In this episode, they discuss how the arm Aston Martin Aramco partnership came together and why F one’s massive data rich environment is the perfect AI test bed.

The mind-bending realities of racing telemetry. Where strict racing rules affect engineering and where the gray spaces for innovation line on car [00:01:00] AI and virtual sensors hit stop automation and much more. So without further delay, let’s listen in to part one.

John: Good morning everyone. My name’s John Kourentis. I lead the automotive go to market team for the for the EMEA region.

Really privileged to stand here this morning to co-present with. Aston Martin, the relationship and the engagement that we’ve got with Aston Martin, Aramco F1. So Charlie and I are gonna kick off today with a summary of the relationship, the engagement that we’ve got between Arm and Aston Martin, Aramco F1.

Hopefully lift the covers a little bit on what it’s like to. Owned the e architecture inside of Formula One Race car. So without any further ado, I’m pleased to say I’m joined by Charlie Blackwall, Head of Electronics Aston Martin Aramco Formula One team. And we’ll be lifting the lid a little bit on the on the relationship.

We announced the partnership at CES in, in January of this year. The nature of the partnership is official AI compute platform partner for Aston Martin Aramco F1. So we’re exploring, how we can apply that. [00:02:00]Those types of technologies to help improve performance of the team. So I’m not sure if everyone here, if one fans hope, hopefully you are.

But it is the biggest global sport on the planet. It is absolutely phenomenal the reach that, that it has particularly with the Netflix series, drive to Survive has captured the hearts and minds of a totally new set of fans. A really driven the driven interest in sport. But it is a one and a half billion global F1 TV audience.

And, closely approaching a billion global F1 fans. So it is from an arm perspective. So it really did make sense in terms of, the nature of the relationship that we can have with a high-performance sports team like at Aston Martin Aramco F1, with the kind of global reach that the sport has was one of obviously the deciding factors in terms of us driving the partnership.

There were other factors around this too. The technology. The technology is fantastic and actually if you look at some of the stats here. An F1 car has 300 ish sensors scattered around the [00:03:00] vehicle. When you look at what happens in the wind tunnel, thousands of sensors in the wind tunnel collecting hundreds of gigabytes of data just on a race weekend.

There’s about a hundred gigabytes of data but think about all of the testing and everything that happens, and simulation, everything. The amount of data generated by this sport that the teams then have to mine and use to make decisions on is tremendous. So clearly, DA data is the fuel of artificial intelligence.

So it really lends itself to, to really interesting applications for ai. But it’s not just about technology. For us, the partnership is much broader than that. Equity and inclusion was a really key component to the partnership. And actually, signing off on a program like this goes right to the top.

(Arm CEO) Rene (Haas) had to sign the partnership off. And it was actually this area that red a, was super, super interesting. It was this part of the partnership that really got the deal over the line. If you think about the challenges that we have in our engineering-based businesses to attract diversity into our engineering teams [00:04:00] and into our businesses, that’s replicated in motor sport as well.

Predominantly, engineering companies at heart. Coupled with motor sport means it’s generally a very male dominated environment, right? One of, one of the key pieces of work that we are doing with Aston Martin is really trying to drive diversity into, technology, engineering or groups and bringing those into our company.

Technology integration. Obviously Charlie and I will talk about it, talk about that in a lot more detail is super interesting. I think it’ll all be really interested in that too. And there, there’s obviously the business impact. It is a high glamorous sport when you’re at the when you’re actually at the race weekend.

I’ve had the pleasure of. Actually being hosted by Charlie in the Aston Martin garage. While he’s explaining to me the design of the steering wheel that he was responsible for, with all of the mechanics and the drivers around you is really some experience I have to say in terms of diversity and equity.

Pete Jessica Hawkins is one of the female drivers of. The Aston Martin team, and she was a really key component to, to [00:05:00] building out our partnership and the relationship. She’s an ambassador for the F1 Academy and for the female talent within Aston Martin Aramco. She’s also now an ARM ambassador as well.

And we’re really fortunate to have Jessica working closely with us. And, we had our global engineering conference just a few weeks ago in Birmingham in the U.K. Jessica came along on a panel discussion and was sharing kind of her thoughts, her experiences of what it was like as a woman trying to pave the way in Motorsport.

Very tough environment to do, to be successful in as a woman in the time that she was doing it. Just yesterday, I actually saw on Instagram, Mabel, that we had some partnerships happening at the Aston Martin facility at Silverston. Some of the armed graduates were there.

An event that Aston Martin were hosting to bring, young engineering talent into the organization. And we did a cross compilation of some of the arm new graduates up there doing some demonstrations of some arm technology and doing some talks to the kind of new.

[00:06:00] Engineering talent that, that Aston Martin were bringing in. So we are sponsoring Jessica specifically on a path to Lamont as well. So she’s currently competing in the GT three World Challenge. The goal there is to get Jessica racing in Lamont and Arms doing a lot to support her on that journey.

And we are really grateful to have that element of the partnership’s really important onto the technology. I think that’s why most of us are here to understand that. So we’ve been working with Charlie and the team for about a year now exploring what the potential partnerships can be can look like from a technology perspective.

Obviously AI is a significant component of that. We’re looking at a number of different areas. So here’s just a couple of, here’s just a, like three examples of the kinds of things that, that we’re exploring. We’ll go into these in a bit more detail. So there’s a lot of sensor technology that goes into the wind tunnel.

Aston Martin have just invested in a brand-new state-of-the-art wind tunnel. Probably the. The most state-of-the-art probably in this one. They’ve obviously got, Adrian EY has recently joined the [00:07:00] organization is driving the design of the, of next year’s car. There’s a tremendous amount of technology that goes into that, that, that wind tunnel platform, and that’s one of the areas that we’re that, that we’re supporting Aston Martin on.

There’s a whole bunch of onboard sensors as well thermal cameras for managing tires and a whole bunch of other things. So we’ll be talking about that in a bit more detail. And there’s a really interesting redesign of the whole pit stop break that’s happening in Aston Martin as well, which is in testing around now.

And we’ll be touching on that in a bit more detail. So we’ll go through these in the, in, in the q and a with John. Okay. So without further ado, Johnny, I’d like to welcome Charlie on board. Yeah. So we’ve been. We’ve been talking for about a year, right? So something like that.

Charlie: I think. Yeah, I think it was s Silverstone last year where I, this showed around the garage and we started talking about how we can, or how we already use arm technology and then how we can move forward with it to ultimately make the car go faster.

So fast forward a year right to now. How’s the partnership going? It’s been really good. I think there was a meeting that we had probably [00:08:00] about six months ago where some engineers from joined up with my team and we just had a brainstorming session. We talked about all the different things that we wanted to do.

We talked about the problems that we have on the car, how we’re trying to get data, how we’re trying to do, improve our instrumentation and so on. And we then had this discussion with your guys about why don’t you do this? We’ve got this and we’ve got all of these other systems that we don’t usually look at.

Formula one’s very sort of tech heavy, but at the same time it’s very, I’d say archaic in some ways. We know what we do at the same time every time. And it’s, it’s important to have a hundred percent reliability, we stick to things we know, and then suddenly we’ve got, hang on, why don’t you do this?

Here’s some new ideas, here’s some new technology. Here’s how you can bring it forward. He can bring in these new systems. And yeah, that was exciting. And so from that then the whole load of ideas have spawned and now we’re trying to work out how to do them. It’s the

John: fun part. So I think a lot of people in the room probably have some.

Exposure to the electrical [00:09:00] electronic architecture of vehicles where this is the automotive part, and most of our guests are from. The automotive industry From the road car, right? Yeah. For pass passenger vehicles. So tell us what’s it like in your role as head of electronics for an F1 team?

Give us a kind of glimpse into the role and the responsibilities that you have.

Charlie: Yeah. So I look after a team of 45 people and essentially we design all of the electronics, all the control software, all of the system, electronic systems that make the car work. Along with that, we have all of our test facilities, so things like automated test.

Equipment. Equipment. We’ve got hardware and loop simulators. We’ve got driver and loop simulator. And then there’s a whole load of software and loop capability as well. We look after the mechanical design of all the electronics that go into the car. All the warren harnesses all of the physical lights.

Then there’s all the control software. So what controls the power unit, the gearbox, the driver interface. We design a steering wheel, all the electronics that go into that. And then how we test it all and. But it’s not just limited to the car. We also do everything for test and [00:10:00] development department.

We do all of the electronics for the wind tunnel. So it is huge. The scope of work’s massive. Yeah. We’re involved in everything and obviously pit stop rig as well, that’s another project for this year. So yeah, it’s busy. Very busy and constantly jumping from one project to the next. For instance, we’ve got, so within.

F1 architecture. There’s standardized systems that we have to use to prevent us from doing fun things like talk vectoring and traction control and all that sort of stuff. So we got supplied in ECU. We have to integrate that into our car. And then there’s standardized parts of the software and we’re always trying to maximize our capability around that.

So make the most of the ECU work out where the loopholes are, work out where the gray areas are, work out how to maximize everything to get the most performance out of it.

John: And so there’s controls over that ECU, but there’s also the data that gets pulled off of that ECU is restricted in terms of the path that takes back to the teams as

Charlie: well.

Yeah, so you see there was a picture early, telemetry coming through. So we’ve got live telemetry that comes from car, goes back to all servers and then comes into the garage [00:11:00] and we can see all that live telemetry and it’s near enough real time. So there’s a delay of about a second or so.

There’s a restriction on what we’re allowed to do. Again, we’re not allowed to do garage to car. ’cause they don’t want us to change the settings. We have to tell the driver what to do. So that’s where we’re the steering wheel. They’ve got lots of configuration that they can do on that. But yeah, so we, in terms of telemetry, it’s about, I don’t know, five mega, five megabits a second.

And of data that come across, as I say, we’ve got a. 300 sensors or so, we have a restriction on what we’re allowed to, or how much we’re allowed to log, how fast we’re allowed to log it. But there’s also an area where we can work outside of that. Yeah. Which is something that we’ve been discussing exactly.

To take advantage of. Which would effectively the idea is to control is to have restriction on the control electronics, which actually control the power unit and control the gearbox, that sort of thing. But there’s a whole world outside of that is very much a gray area where we can take advantage of it and go, okay, this, these are the ways that we can get the data that we need.

These are the insights that we need into how the [00:12:00] cars operate.

John: So that control system is uniform across the whole grid, right? All of the teams have that whole the same control system, the same restrictions around that. And the area that we’re working on is right, okay. Outside of that, right? What can we do?

What can we do outside of that? So that’s the focus of the onboard electronics, right? So what type of technical integrations are we talking about? I’ve put a few examples up there. Maybe let’s dig into some of those a bit.

Charlie: Yeah. So I think the, you know what, one of the, one of our challenges is making a car as light as possible.

So every year there are, the regulations change to make the cars safer, which is really important, but that means the car gets heavier and heavier. Yeah. So we’re always looking at ways to bring the weight down. First thing that anyone says to me when they say, oh, let’s take the weight outta this, take sensor off.

Take off the instrumentation because we don’t actually need it. And you go actually you do need it. Because that’s where you, that’s where you build all your correlation. That’s where you have your models, your, models of the car, your models in the wind tunnel, CFD. We then need to correlate it all with the track.

However. There are certain areas where you’re like we could [00:13:00] virtualize these. We could have a kinematic model that we run on the car. We could have a, a machine learning model which we can run on the car that represents certain vehicle dynamics and that sort of thing. And we’ve tried to build kinematic models and run on the ECU, but we run out to processing power, or we’re restricted in terms of the platform that we can use because of the software restrictions.

So what I discussed with you is, okay, what have you got? What can I put, if we have a separate unit, what can we put into that? What can we, what inputs can we put into that? What models can we fit on that? And from there we come up with a strategy to virtualize some of our sensors. So a good example is we have a, an optical speed overground sensor and it’s, we use that for measuring longitudinal and a lateral velocity, which is really important when understanding tire energy formula.

One’s really dull actually. It’s all about tires and how you manage tires. And part of that is understanding. The thermal behavior of the tire and modeling that. And of course the tires are nonlinear, so they’re very complicated to model. But part of that is understanding how much it’s slipping. And if you can imagine if you go around a corner, [00:14:00] tires are slipping sideways, you’re going a straight line.

The tires are slipping longitudinally and trying to understand that you need to measure it. So we have this sensor, but it’s really heavy. It weighs 600 grams. And so we said, okay, how do we replace that with something else? And as I say, we had a kinematic model that uses lots of other sensors on the car, but there are limitations to it.

And so I said can we do a machine learning model? Yeah. And put that running real time on the car. And he said, yep, we can do that. Let’s do it. Let’s do it. And so that. That’s one of the sort of areas that we’re looking at. It’s totally outside of the regulations. It doesn’t have any control impact on the car, but it allows us to use the real time data from the sensors into the machine learning model.

Yeah. And then that can come back over the telemetry feed rather than having it coming. The sub sort of subsample data that comes through telemetry and then running that off car and then having to, it’s then not leading edge. You’ve got a 32nd delay, which then has an impact on making strategy calls.

So it

John: really translates to eight. Weight saving in the car. Yeah. Which is time. Speed. [00:15:00] And decision-making capability. Exactly. For the race strategy as well. Super interesting. You think about, how the work that we are doing in terms of just advising on some technology actually has a direct impact on the on, on the race strategy that potentially on unfolds at the weekends.

And we saw actually, saying tires are really boring, Charles Leclerc was a good example of that on the weekend, probably. In terms of how. Tires could actually change the whole outcome of a race for a

Charlie: driver. Absolutely. There was the, at the last race, there was the Alonzo train and he was driving exactly at the right pace to make the tires last as long as possible to maximize our pit stop strategy.

And he did it perfectly. He held everybody up. The leaders then pitted, and then he started driving and that ruined their strategy. It prevented everyone else from doing a two stop. And then we got the one stop, we got the position that we wanted, and that was all down to time management.

He’s a wide L Fox.

John: Yeah, it’s great. Yeah okay, so then we’ve got some speed overground, ground sensor. Is there anything more you can do in terms of sensor technology around the [00:16:00] vehicle? Anything else?

Charlie: Yeah, absolutely. So the other thing we’re looking at again, comes down to tires is it’s very difficult to actually see what the tires are doing.

So we, we measure the surface temperature, we measure the internal carcass temperature. But there’s lots of things that happen on the actual surface, which you can’t detect. ’cause a car’s driving around circuit things like graining, blistering if you’re starting to get micro tearing and a tire and that sort of thing.

And so we said, okay, we already have a camera that points at it for thermal measurement. Why don’t we use that? Why can’t we plug that into a model and teach it? What does graining look like? Yeah. What does a blistering look like? And so again, that’s the next step. So what we’re trying to do is build this platform of an onboard AI unit.

Yeah. And then and then be able to then load models or pro or do an offline simulation, offline learning program, the model, and then run that on the car and have that real time data coming in. Yeah. And again, the pro trouble with the video data is we can’t send that over to telemetry because the bandwidth isn’t high enough.

Yeah. So we do it all on car and then you can then parametize it or say, this is the level of graining. [00:17:00]And looking at some of the network technology on board the vehicle as well as part of that.

Charlie: Yeah, so again making use of automotive ethernet and on, which is o we can use that, we just can’t use it in the control loops.

We touched on the pit stop break

John: as well. Yeah. That’s really, it’s a super interesting area. If, I don’t know if anyone’s been to a live form in a one race, when you see the teams actually. Executing a pit stop in real life, it is something to behold. It’s absolutely amazing. You’ve got this, race car harling down the pit lane, and it looks slow on tv, but he’s going, they’re going 80 kilometers an hour down the pit lane, right?

And you’ve got some guy at the front jack standing there being driven up by an F1 car. As soon as that Jack goes up, the team flies into motion and taking all four wheels off, putting new 2, 4, 4 new wheels on. Around the two second mark, right? Generally between two and three seconds. The world record set by McLaren, what?

1.83, 1.84 seconds at the last race? Yeah they did

Charlie: 1.8 seconds.

John: 1.8 [00:18:00] seconds to remove going from, a state the moment the car becomes stationary to removing all four wheels, getting four wheels on, it’s amazing the Aston Martin team of redesigning that whole pit stop rig and the gun systems.

And maybe you could talk, tell us a little bit about what’s going on there.

Charlie: So the, as you said, the key to it is the synchronization and making everything work. So before we had pit stop systems, basically car come in, gun the wheel off, and. Take the wheel off. Someone fumble around with wheel nut, try and put that on.

So there’s a whole mechanical electrical system where what we’re trying to do is we’re trying to automate as much of it as possible. So effectively what happens is the car comes in as before it’s even stopped, we started gunning off the wheel. And within the wheel gun, we’ve got a whole load of sensors that detect that the wheel nut’s come off.

Then once the wheel nuts come off, that sensor says, okay, shuttle it. So it spins the other way. So as literally as he’s pulling the gun away, it then auto shuffles to reverse the direction, and then he just goes straight back on again. It automatically guns the wheel back on again. And then it detects that the wheel is on, that it’s [00:19:00] toed up.

It’s not cross threaded, it’s gone the right depth. So that, and that’s from a safety perspective, to make sure that the wheel is on properly. It then goes through to our control system. And it says once all four wheels are gunned on, it automatically drops the jacks and then the light goes green, the cut driver drives away.

And so within that, there’s a whole load of sensing technology within the wheel gun. And then there’s systems within, so we’ve got a control system as well, which is runs a state machine, which then obviously takes all the states, all the different guns, and then that feeds back into the controls that drop the jacks and then also into the light gantry as well.

John: Next year’s going to be super exciting. I don’t know how. Much the audience, we into F1. There’s some big regulation changes coming in in next year. So in terms of arrow and other aspects, there’s new ECU coming on the cars, right? Yeah. The controlled ECU, right? The, so that, that’s upgraded.

So there’s quite, quite a few new things. So there’s a lot of focus right now, I guess in the team about right preparing yeah, this year’s car’s designed right, you can make some tweaks and make some error adjustments, but really I guess a lot of the focus is

Charlie: on. What’s coming? I’d say [00:20:00] 95% of the teams focused on next year.

Yeah. And I think most F1 teams are in that position now where we have what we have for this year. Basically, that’s what we’re going to run for the rest of the year. And everything’s focused on next year. We’ve got some other interesting things as well. We’re a changing engine supplier, so moving from buying Mercedes engines and Mercedes gearboxes to then becoming a works Honda team.

So we’ve got works Honda Engines next year. We’re building our own gearbox for first time in 15 years. That’s running on a dyno at the moment. We’ve got gearbox and a dyno at AF one. We’ve got a gearbox on a dyno and secure attached to the new power unit. So yeah, lot, lots of change. It’s really exciting.

Brian: For more information about how arms, automotive, technology, and expertise are already driving the next generation of vehicles, head to arm.com/markets/automotive. Thanks for listening.