Takayuki Nakamoto of Nissan Technical Center Europe

0:00 Introduction to the Podcast and Guest

Today in The Garage, we’re recording live at CES 2026 at the Sonatus booth. My guest today is Takayuki Nakamoto, who’s Director of Electrical, Electronics, and ADAS engineering department at Nissan Technical Center Europe or NTCE for short. Today’s conversation, discuss how NTCE develops vehicles manufactured in Nissan UK for sale in Europe and around the world.

0:24 Efficiency

We discuss the challenges for developing vehicles to production and how we can accelerate the vehicle SOP cycle using advanced AI tools. It’s an exciting conversation. Let’s go!

Welcome to The Garage. I’m John Heinlein, Chief Marketing Officer with Sonatus. We’re here recording live at CES 2026, and our guest is Takayuki Nakamoto from Nissan Technical Center Europe or NTCE for short. But you go by Terry.

Yeah. Please call me Terry.

Terry, welcome to The Garage. Glad to have you.

Thank you for having me.

1:04 Terry’s Background and Experience

You’ve got to start by telling us a little bit about you personally.

I’m Takayuki Nakamoto. My nickname is Terry. Everyone calls me Terry in the office. So feel free to to call me Terry.

So I’m Director of Electrical, Electronics and ADAS Systems Engineering Department in NTCE, Nissan Technical Center Europe. We are based in the UK. And a little bit about my background, I originally started working in Nissan in Japan as a BCM engineer, body control module engineer. And then I’ve been in body electronics almost until throughout my career.

Okay.

I’ve experienced BCM, body electronics, body control module. I did the battery quality, power management, IKey keyless entry, etc. I also did the systems engineering promotion in the within the company between with Nissan and Renault.

After that, my career slightly changed to the more connectivity side. I became a manager as a of the AIVC development. And then after that, I decided to come to the UK as EE manager, and now here I am as a director of EE and ADAS.

UK is such an interesting change from moving from Japan. We’re going to get into that in a little bit about when we talk about NTCE. Yeah. But first, tell us about a fun fact about you.

Well, I’m a runner, a little bit extreme than an average runner, to be honest. So I’m I’m joining a 46 mile trail race.

Forty six miles! What is it?

72km, seventy Something like that.

Yeah. Incredible. And it’s not just me. My one of my senior engineers, Sarah, who is with me in the CES, is also joining this race. So needs some management team or needs some project member for the Sonatus project is a little bit extreme.

That’s fantastic.

A trail ultra marathon is already incredible, and then to do it on the trail adds additional level of of difficulty.

So hats off to you.

It’s a big challenge for me as well.

I what’s your longest to date so far? Longest race?

Longest one is called the Strongman Triathlon racing. Which is slightly less than the Ironman racing, but that’s the longest I’ve done.

That’s wonderful. Well, I’m a runner and an athlete as well, although I’m not a very good runner. You’re a much better runner than I am. But I’ve done a a lot of racing, and I did a trail half marathon some years ago.

I’ve done one marathon, but but nothing like remotely what you guys are doing. Hats off to you for such a distance. You’re welcome to join.

3:43 Overview of Nissan Technical Center Europe

So you’ll have to tell us about Nissan Technical Center Europe. And what’s the responsibility of of that facility and your your role?

Yeah. Sure. Let me start with the kind of a history of NTCE. We started almost forty years ago as a part of Nissan Global R&D Center.

We are originally more linked to the production site up in the Sunderland, but now it has grown and playing a big role, major role as a global R&D, developing cars like Qashqai, Juke, LEAF, and selling those cars to not just Europe, but also to Middle Middle East, Africa, Oceania, and so on. So that’s the the role of our company. And, I mean, that’s how we started, and how we grew bigger up to now.

And in terms of the role, our main role is really to put all the components and integrate those as a vehicle And making sure everything works as intended.

And I think because as you have vehicles sold around the world, there are different localization standards. Localization is not just language. Sometimes localization means language, but there can be different regulatory standards, safety standards in different regions around the world. You help to handle those specifications, I think.

Yeah. That’s that’s one of the biggest role as well. We call it as a frontline role, but we also after this SOP of the car, we are also in charge of taking care of the quality aspect because we get many feedbacks from the market. And our job is to understand the the issues or the market concerns and then feeding back to our design so that the issue could be fixed as quickly as we can.

It’s wonderful. And I think you have an incredibly long title with, like, four different things in your title. So you have to give us your full title because you touch on many different subsystems, I think.

So, again, my title is Electronics, Electric, ADAS, Systems engineering extremely long, but it involves almost so many almost all the the components, which is driven by the electricity.

So starting like BCM, USM, ACU, harness, alternator, sensors, switches, and plus ADAS system components is in my … So nothing important.

Nothing important at all. Nothing. That’s incredibly diverse responsibly. How do you I I don’t know how you manage all that.

I have a great team. That’s how how how to manage.

6:22 Digitalization Initiative at NTCE

Well, I know one of the initiatives you have is something you call “digitalization”. And I wonder if you can explain what some of your goals are and and some of the initiatives you’re trying to drive.

Yeah. It’s a very good question.

When you hear digitalization or digital initiatives, it sounds like it’s a customer facing features. But for us, for NTCE, that’s more about how we develop vehicles.

It’s about using using more data, automation, simulation tool, AI, etc., to make our development efficient, consistent, and high quality in the end. And that’s what it’s about. We are trying to drive, we are trying to introduce up to date technologies to our development processes

So that we can make it better and, yeah, make the better quality car with confidence.

That’s great. It’s I assume that includes things like digital twin where that’s appropriate and so on like that.

Exactly.

And the Sonatus project is part of that as well.

7:26 2026 Nissan LEAF

I’m glad you mentioned that.

You know, we as we’re looking in the podcast, as we look right out there, right outside the podcast studio is this incredible Nissan LEAF. It’s a 2026 brand new Nissan LEAF. It’s been completely redesigned. But the reason that that car is here and the reason you’re visiting with us here this week and and you’ve been presenting alongside us is a project we’re doing with NTCE and yourselves and your colleague, your senior engineer Sarah, who’s also with us, been a phenomenal collaborator, to show how our technologies can help in the preproduction phase that you’re responsible for.

7:59 Challenges in Vehicle Development

So I thought to discuss that. Before we get into what problem what we’ve done in our solution, I wonder if we could start with setting up and understanding what’s the kind of the problem? What’s the conventional approach you use today historically to test? And what are some of the drawbacks and downsides to the conventional approach?

Okay. At the high level, our biggest challenge is managing complexity

within the limited time time development period.

Right.

So as you know, like, EE and ADAS, it’s becoming more and more complex every day. But with our traditional way of developing the the vehicle, it’s hard. It’s it’s it’s just doesn’t scale well. That that’s the major challenge that we have in in NTCE.

But I think it’s not just us, but the whole industry is suffering from the same kind of problem. Sure. So for for the current approach, it’s heavily relying dependent on the vehicle testing. It’s a physical vehicle testing.

Which is a major concern for us because in reality and we do a lot of testing during our development phase. First thing we need to do is to access to the car and get the data from the car so that we can analyze and understand what’s going on on the car. Yeah. That is already a time consuming because if you think about our global footprint, we are based in the UK, but we have a team in Spain who does AD durability testing, for example.

And we also have a manufacturing site up in Sunderland in in the in the UK. And a lot of issues are detected there over there. And, again, in order to get the data from there is a big effort.

Oh, I see.

So but we are under pressure to deliver the car in time. So we have no choice than going getting the car. Most of the time, we have somebody who can get the data from the car and so that they can send it to us so that we and then we can analyze. But for some cases, there is nobody who can get the data, and I will have to send to somebody from my team in Cranfield up to Sunderland just to access to the data.

Just to access. And I think the other thing that I learned as we were discussing the other day is that, you know, you say, okay. Well, test vehicles, it’s not that big a deal. It’s not you know, you make cars, so you make a few extra cars.

What’s the difference? But these are preproduction vehicles. Exactly. So they’re they’re very expensive. They’re much more expensive than a traditional car because you’re not yet at the mass production economies of scale that would would take.

So then these are a real cost to you.

Exactly.

And then in case the issue is really severe, the the vehicle gets on hold waiting for somebody to arrive and get the data, hopefully hoping that he will fix the the issue quickly. Right. So it’s a huge loss for the company. And that that’s where the value is coming from, actually.

Because if you can reduce that time to access to the vehicle, you know, downtime of the car becomes much less. Right. So that’s and in the end, we can reduce the number of vehicles if that becomes reality.

And Okay.

So so resource intensive, vehicle intensive, engineer intensive, time intensive, flying people in some cases are driving driving or flying people to do that.

So

11:20 Improving data collection with Sonatus Collector AI

That’s a good setup for the problem. Correct. Let’s explore the solution and some of the things we’re doing together with Sonatus and and NTCE to do better. Let’s start with our Sonatus Collector AI product. How has that allowed you to change the way you gather data about vehicles and vehicle issues?

It changed completely the process.

Well, when somebody rings me saying that this is happening and please come and fix it, I’ll just open my laptop. I ask my engineers to open the laptop. You know? You can access to the data in about a minute or or so within an hour.

That’s a game changer. Sunderland is not so close from our our site. Right. Right.

It takes four to five hours just to get there. Right. So it’s a major change in the process. We can even access to the vehicle from from the from their their home.

Once we had a chance example had opportunity where we had to show our Collector AI to our executives, and the engineer was working from home at the time. But what he did was from his kitchen, he just opened the laptop and then remotely showed how Collector AI works to our executive, and that was amazing.

That’s fantastic. And then another thing that you’ve been showing a demonstration here that, you know, we think about data for vehicles, but, of course, there’s many, many different data sources, many, many different subsystems.

And, of course,

12:45 Dynamically adaptive data capture

as you’re testing, you’re not always testing the same things or you may be testing one thing and you find a problem in another subsystem. So another benefit I think we’re providing you is the ability to customize the data you’re capturing.

Is that beneficial as well?

Exactly. What we really want, if we can, is to upload all the car of all the data from the car to the server all the time.

But, unfortunately, we can’t do that because it’s too much. Too much. Too much. And it’s it’s more than a bandwidth of the communication.

Of course.

It’s also impacting the server side, and the cost is gonna be too much. Right. So but this customization of the Collector AI allows us to to modify it. Initially, we have just a minimum set of data uploaded to the server. But when we know that there’s some kind of issue happening, we can just remotely adjust the parameters to what we call policy.

Data collection policy. Yeah.

Exactly. And then we collect the only the necessary data for us to analyze the the issue.

And that’s really, really cool feature that we have now.

And and we’re showing a demonstration of of that capability, the ability to adjust it. We’re showing two different demo stations where you can literally adjust that data collection in seconds.

Oh, yeah.

And and I like to to joke that imagine the car’s on a test track or a test loop or something like that. If you don’t like the data you’re getting, by the time you get to the next loop, you could have changed the question. The car goes on, which is incredibly responsive. Because in in engineering, you know, we talk a lot at Sonatus about the engineering design cycle of, you know, observe, analyze, act. And the faster you can go around that cycle means you can you can re aim and re aim and re aim the the proverbial gun so you get to the target more quickly versus if it takes a week or day to do the cycle, you realize that I have to ask many, many times to get the data I need.

Yeah. Yeah. It’s perfect. It’s it’s a perfect example of agile development. Initially, today’s car, data that we upload are kind of hard coded.

We do have a feature to update it afterwards, but it’s not as fast as we want. Yeah. But with the Sonatus Collector AI, it can be done in a second, and that’s super cool.

Thank you so much for those kind words, and we we are really it. And and everyone, we’ve been showing the demo. We’ve shown it to hundreds and hundreds of people over the past several days, and the reaction has been fantastic. But then we go into the second part. So the the Collector AI is providing you better data,

which is fantastic.

15:21 Data Analysis with Sonatus AI Technician

But there’s also now of how do I analyze that data? And and the second part of your problem is combing through the data, understanding the data, interpreting the data to

see what’s wrong, and and how should you fix it. So the second product we’re working with you on this project is our Sonatus AI Technician.

Exactly. Tell us about how you’re using that and and how that’s been helpful for you.

When we had this idea of solution of Collector AI and AI Technician, we knew this is going to work because from our from our experience, when we have the data right data and provide that data to the right expert within Nissan, it doesn’t take that much time. But if we don’t have those two pieces together, that’s where we lose a lot of time. We sometimes have a struggle having access to the data, as I mentioned, but sometimes we don’t have that expert available. He was busy doing something else or it could be on a holiday or whatever.

But with this solution, AI Technician, it’s like having that expert always available sitting next to you. And we can ask him any questions, any queries, etc., to him so that he can guide you through how to narrow down the root cause. And yeah, that that’s a huge time saving for us, for any engineers working on issue. And not just the design engineer.

Right. It can it will also help help the test engineer who detects the issue. Also, it’ll help the production quality engineer who detects also detects an issue. They always have to query to the design and design engineer.

So design engineer is fully loaded, have receiving so many queries from everybody if there’s an issue. So this AI Technician will accelerate the issue analysis, but also free up the design engineer so that he can focus on the actual design job.

Yeah. Or the more complicated situations. Exactly. One of the we showed a number of different examples here, and one of the examples you’ve been showing is how you can begin by first combing through, I like to say, the haystack and just more quickly

find the needles.

17:38 Making every engineer an expert with AI

Looking at what are the diagnostic trouble codes, DTC codes that are active. What do they mean? Categorizing them in a way to get a sense of what’s more urgent, which are active versus which are historical. So that’s a kind of a first level of improvement of productivity that a human can do.

There’s nothing magical about it, but it makes it much quicker to get to these patterns. Yep. But then the second part, which is super interesting, is then you’re able to drill into some specific failure and say, why is this failure happening? And collectively together, we’re able to use your knowledge base and your database that’s then ingested into AI Technician to give you the logic of how we were able to diagnose the problem.

How how much of a helper is that for you?

It’s big, because what we implemented, realized using AI Technician is is a normal approach for automotive design engineer, automotive engineers. When we have an issue, we try to understand the possible causes, common approaches like FTA, and that is already built into the AI Technician. But unfortunately, everyone is not a superstar. He might be just a software engineer, not necessarily understand have a good understanding of the hardware.

Right. So but having this so if there’s an issue, software engineer can narrow down, can list up all the possible causes from software point of view. However, sometimes it’s missing the hardware perspective in in the FTA. Right.

But with the AI Technician, it gives you the full view of the possible causes from software, hardware to could be a operational issue of the customer, etc. So that is a is a big help for any engineers working in this in Nissan, or not only in Nissan, but I think it’s on also helps our Tier-1 supplier to narrow down the cause and get to the bottom of

the concern.

19:37 Efficiency gains in problem diagnosis

I love that. And your yourself and your presentation and also your colleague, Sarah, who’s been here with us this week, I really like how you’ve highlighted that one of the things we’ve delivered is how we tell you what’s

not the problem.

So a lot of times in diagnosing things, half of the battle is figuring out what’s not wrong so you can spend more time on the parts that are the potential problem. So we’re helping to provide, for example, an indication that, know, for example, it’s not a hardware problem or it’s not a problem with this or it’s not a problem with that, that then we can spend the rest of the time in a much smaller problem space to understand the problem.

Is that is that useful to you?

That’s very, very useful. Common approach for for anybody in Nissan as soon as they find an issue is to check the harness, for example. Right. How is the connection?

Is the harness okay? Is the fuse okay? Is the twelve volt battery okay? And sometimes just try to is the software version okay?

Right. But with the this solution, with the that kind of guidance coming coming up ina few seconds, save us you know, we don’t need to do all that.

If if we have the access to the data and we already know some of them are already ruled out from the the FTA. Right. Save huge amount of time.

That’s great.

That’s Well, I know there’s such a pressure on in the industry on time to market.

There’s a lot of the historical design cycle for vehicles has been quite long. There’s a lot of pressure. Certainly Chinese OEMs are delivering a faster cycle time. And so everyone is trying to see what can they do while at the same time not sacrificing quality.

So I think our mission in working with you was to help you to pull in your SOP start of production, help pull in your SOP cycle time while at the same time achieving the quality and

productivity goals you want.

21:20 Conclusion and Reflections

So we’re excited to be working with you.

Me too.

It’s we’re so grateful for your partnership and for for you being with us here at the show this week. It’s been wonderful to present side by side with you. And I think we both learned from the process, both from what you’ve

done and also the great questions we’ve had from the many customers and many visitors we’ve had. So just my thanks to you and thank you for joining us on the podcast today.

You’re welcome. Thanks for having me here. It was a really, really exciting week for me.

Thank you.

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