Driving Innovation by Sonatus

In this podcast episode, Engineer for e-Planning, Coding and Cybersecurity Sarah Dorey of Nissan Technical Center Europe (NTCE) explains how NTCE is using Sonatus AI Technician and Collector AI to transition from manual, on-site vehicle validation to a remote, AI-driven workflow. This digital transformation has significantly boosted efficiency by allowing global teams to collaborate in real time and reducing root-cause investigation times from two weeks to just two days.

Creators and Guests

Host
Sanjay Khatri
Sanjay Khatri is Head of Product Marketing at Sonatus, Inc., a leading provider of software solutions accelerating the shift to AI-enabled Software-Defined Vehicles (SDVs). He brings more than 20 years of experience in product management and marketing for cloud and edge software across Fortune 500 companies and early-stage startups. At Sonatus, Sanjay defines product positioning and go-to-market strategy for solutions that help automakers deliver intelligent, adaptive vehicles that evolve with changing market demands. His background includes leading strategy and execution across Connected Car, In-Vehicle Infotainment, and IoT technologies. Sanjay holds a Bachelor’s degree in Computer Engineering and an MBA, along with certifications in AI/ML, telecommunications engineering, and content strategy. He is a frequent writer and speaker on the future of automotive software, edge AI, and the rapidly transforming mobility ecosystem.

What is Driving Innovation by Sonatus?

Dig into wide-ranging technical topics about modern vehicle technology with industry leaders, hosted by Sanjay Khatri, Head of Product Marketing, Sonatus.

Welcome to another episode of
the Driving Innovation Podcast.

Modern vehicles are
amazing feats of design and

engineering, but they're
also increasingly complex to

develop, test and validate.

Today, we're diving into how one
of the world's leading automakers

is revamping its pre-production
validation process through a

data driven and AI-aided
approach to simplify that

complexity, gain efficiency
and ultimately save costs.

I'm your host, Sanjay Khatri,

and I'm thrilled to introduce
our guest, Sarah Dorey,

Senior Engineer for e-Planning,

Coding and Cybersecurity at
the Nissan Technical Center Europe.

Sarah is here to discuss how
the center is solving major

validation challenges and
accelerating its vehicle

development programs with the
help of Sonatus Collector AI

and AI Technician.

We'll explore the power of
event driven data collection,

the insights unlocked by
connecting data across

complex vehicle subsystems,
and how an iterative closed

loop workflow is fundamentally
changing how they conduct

root cause analysis.

Sarah, thanks for joining
me on this podcast.

Hi, great to meet you. It's
lovely to be here again.

Thank you. Yeah, so
let's get right into it.

Your organization is at the
center of Nissan's vehicle

development program
for, I should say,

award winning and popular
models like the Nissan LEAF and

Juke.

By the way, congratulations on Nissan
LEAF getting all sorts of awards.

What were the validation
challenges your teams were

running into that made you
look for a different approach?

Yeah, thank you.

Great question, because it's something
that's been challenging us and many

OEMs through the
development phase.

We tend to find difficulties
when it comes to root cause

investigation in all of
our standard developments.

It's nothing unusual
within the industry,

but with the distances where
our manufacturing plant

is at two R&D centers in Europe,

one in the UK and one in Spain,

gathering data can
be really difficult.

You have to be on the spot or at
least have somebody with the skills,

knowledge and capability in the
right place at the right time.

And that's not always the case.

Quite often you're
gathering data, you go back,

you analyze the data.

And even after that analysis,

you want to do some more
data collection to really dig deep

into the data, really analyze
what ECU is having what

problem with what scenario
or what CAN connection.

So more data logs are required,

which if you've already left
the site is a bit of a problem.

Also, it comes to our
development cycle and we're

adding vehicles go down
the production line,

we have problems come
up as they're produced.

The traditional model is that the
data loggers go on as the

twelve volt battery
is connected.

But we don't find out what's
on that data logger until it

reaches the end of the
line, somebody collects it,

they upload the data and then
notifies us that it's available

in the cloud so that we can
go into that data again.

So this kind of slows down
the investigation process

and makes it quite lengthy.

We want to speed up
with what we're doing.

We have such great products.

We want to be able to get them out
to our customers a lot quicker.

The products are a lot
more complex as well.

So there's a lot of development

concerns that we need
to have a look at,

which are standard concerns.

But we want to make sure that the
customer doesn't get to see these.

The software is so complex.

You've got cybersecurity
on top of that.

There's a lot to dig into and
we want to make sure that by

the time we release
that product,

that the customer is getting the best
of what our products can deliver.

So it's really been
able to speed it up and

reinforce the quality that
we offer our customers.

Yeah, so I hear you
describing something

which maybe I can characterize
it as digitalizing or

virtualizing the process
so that it's more dynamic,

it's more real time without
having to wait on assets,

wait on test vehicles, do a lot of
sort of manual transfers and so forth.

So I mean, it's reducing a lot
of the friction in that process.

So you've adopted a more sort of

digitalized and AI enabled
workflow with Collector AI and

AI Technician. What sorts
of results have you seen?

Yeah, it's really
quite promising.

We're obviously going through our
proof of concept project at the moment,

but the engineers are really
enthusiastic about the

opportunities.

As I said, traditionally we're having
to travel to go and collect data.

With the remote
deployment of policies,

we don't need to
be able to do that.

And then with the redeployment
of policies, again,

it's very quick and very easy.

We can see results
really quickly.

When we then turn around
that data into AI Technician,

then we can start to go
through the FTA (fault tree

analysis) really quickly.

We can start using
our experience and our

education to be able to ask
the questions that go into the data

that AI Technician connects
all the dots for us.

So we're not having to sift
through different SharePoints

and different files to pull all
that knowledge together or go

and speak to people across the
business because we've pooled

all of our Nissan knowledge
and using AI Technician,

we can bring it all together.

So it's really exciting that the
speed that we can be able to do this.

And we need all of our
engineers still within our

business to be able to
make these decisions,

but they can at least do
it quicker with more access to

data and real
reliable data as well.

So it's a real bonus for us.

So I hear you saying,
number one, it allows

your engineers to work on
vehicles without having

physical access to vehicles.

So you sort of remove
that dependency of

contention with resources in
terms of your test vehicles.

And then the other one is being
able to sort of dynamically do

things potentially even in a
test drive, I would assume,

so that you're not having to kind
of bring the vehicle back, refit it,

adjust the loggers
that are in the

vehicle and so forth.

So is that how you
would characterize it?

And how is
that helping sort of,

you talked about kind of a
global team with multiple

sites, presuming you have
interactions with folks in

Japan as well. How is that
all helping you collectively?

Yeah, it gives us a
lot of flexibility and agility to

react to situations.

This ability to remotely deploy

the policies means that we can
have a car going around to test

track in Barcelona, but
I can be accessing it

here my home.

I can be doing it
from the office.

Somebody from Japan could be
checking the dashboard data as well.

So this one car and one
data set with access for

all really opens up
the opportunities for us to

go quite wide across our test
fleet when we're testing in a

way that we haven't
necessarily done before.

Normally, test cars are allocated
to a particular function and a

particular test.

So it really does mean that
we have more resources for all of

the engineers to be able to
use across the globe if we're

adding this remote access.

So that's really quite exciting.

Let's talk about the analysis.

As I mentioned early
on, vehicles are

amazing these days, including
the ones that I mentioned.

But they're also very
complex, a lot of electronics,

a lot of what I would call sort
of cross domain dependencies,

features span
multiple subsystems.

And oftentimes
what I would assume

is that connecting a lot
of those dots across those

subsystems can be challenging,

especially when
you're looking at

one particular data silo.

How has AI Technician, in
conjunction with Collector AI,

helped your engineers connect
the dots across these domains?

And what sort of insights and
time savings has that unlocked?

The engineers, their
skills are exceptional,

but they can't be an expert
in absolutely every ECU and

every system across
such complex vehicles.

And as I said before,

once you add cybersecurity
on top of that,

then it really does add a new
level to what we're working on.

To bring in AI Technician that
links with the Nissan Knowledge

Data Lake, AI Technician can
bring that knowledge together.

It can make everybody an expert.

You don't have to be waiting for an
email from somebody for a few days.

We've essentially gone
for if we're root cause

investigation on a
particular technical

concern, we can see
where the opportunity is.

During our proof of
concept delivery,

we've seen that we can go from
two weeks to two days for a

root cause investigation.

And that's massive.

But because it breaks
through that complexity,

because it can join all of
those dots for our engineers,

that's really exciting.

I absolutely love how it comes
back with my Nissan knowledge

because I'm an engineer.

I don't trust anybody.

If you're going to give me
an answer, I want proof.

And you've got to go really
deep to make me believe it. And

the Technician Solution
does It

comes back to me
in my own language.

It starts quoting documents
at me that I probably wrote at

some point and it goes into
all of that depth that actually

says, this is why you
should believe me.

But it also tells me why
it discounted other things.

That feels like a conversation
that I'm having with our engineers.

And that therefore makes me
believe that it's really gone

into the depth of the knowledge
that we have at Nissan and we

have many years to be able
to call on to give me a good

quality response back so that
engineers go into that decision

making process with a real
rich knowledge backing up

their decision making, which
is where we want to be.

That's what we do. But
now it's time consuming.

With the Sonatus AI we can get
that through that so much quicker.

Interesting.

Yeah, I like how it's
it's augmenting the work

that engineers are doing
by giving them almost an

assistant on the side who can
go out and get that information

that they may not
have access to,

or they're not sort of
inherently knowledgeable about.

It sounds to me also that
there's some sort of of an

iterative process, right?

So rather than sort of a static
linear, you get some data,

you do some analysis,
you go back and test,

and it's sort of a
sequential process.

Can you describe to me some
of the sort of the closed loop

workflow aspects of how
sort of this dynamic data,

the analysis of AI Technician,

and then potentially kind of
going back and iterating on it?

Can you give me a
little bit of insight in

how prevalent something like
that is and how much of that is

actually helping you?

Yeah, and engineers are the kids
that always grew up saying, "Why".

Why, why does this happen?

So whenever a problem comes up,

a concern needs investigating,
that's what they're going to do.

You give me some data, but then
I've got more whys to go into.

We set our policies on that.

It can be quite broad,

so we can just ask it to look
for any DTCs that come up

on an ECU.

And it will flag that and it will
collect that data automatically.

That's really cool.

But what we can also do is
pick up CAN signals that we

specifically want to target.

So if I know I'm
looking for something

related to that is
on the CAN network,

then I can set that and it
can bring that back to me as well.

So I can have these
simultaneous data collections

to really cover everything
that I'm looking to do.

It'll upload all of those
data logs into AWS for me.

So then it sat up
there into the AWS

with our Nissan knowledge and
they sit there side by side.

And with AI Technician, we
can then start interrogating that.

The cool bit after that is
that we can take that FTA

knowledge, all of that "why",

and put it back into
a policy and redeploy.

So from our user interface
that here in my garden

or the guys in Spain
or Japan globally,

we can just redeploy that.

And we can keep
going on this loop.

With the closed loop system,

then we've got that
additional safety.

And Nissan knowledge isn't
sat in somebody else's cloud.

So there's no vulnerability
when it comes to that element.

So we can feel quite secure that all
of our knowledge is held together,

whilst being able to really
deep dive into our knowledge.

So it's really cool, really.

You talked a little bit about those
better alignment across global teams.

As you expand these tools to

programs like the upcoming
Juke and the LEAF Models,

what do you see as the next
step in the smart testing AI

assisted validation?

Yeah, thank you.

It's a real opportunity
now to take more of our

digitalization work and feed that
back into this whole process.

And in the proof of concept,

we can see where the
advantages are, and off we go.

We can then get those
recommendations from the [AI]

Technician that give
us other opportunities,

whether it's seeing trends
across the business where our

fleet is flagging
certain anomalies.

Or if we're just looking
to secure that data across our

whole fleet, can do one vehicle,

we can do many vehicles.

So we can then begin to look where
we can do simultaneous testing.

So it might be me
needing a data set.

It might be two or three of
my colleagues that also need a

data set from the same car.

We can set our policies
onto that one vehicle.

We don't need three
different vehicles.

So that's where we've been
able to look at that reduction

in the test vehicles.

The cost is high, they're
prototype vehicles.

So any advantage for us to be
able to do that is always going

to be a win.

Well, has been really
helpful, Sarah.

And I really want
to thank you for

sharing your precious time.

I know you're very busy,

but this has really
been helpful.

And I've learned a lot.

I thought I knew a lot about
project that we have going on,

but I've also certainly learned
a lot and you've added a lot

more color to it.

So I really want to
thank you for joining us.

Thank you. Thanks for having me.

It's always great to
chat with you guys.

That brings us to the end of
another insightful episode of the

Driving Innovation Podcast.

We've seen how Nissan Technical
Center Europe with the help of

Sonatus Collector AI and
AI Technician is fundamentally

changing pre-production
testing and validation.

By leveraging event driven data
collection and closed loop AI

assisted workflows,

Nissan Europe is accelerating
vehicle development,

reducing costs, and achieving
better global team alignment.

A huge thanks to Sarah Dorey
for sharing how this smart

testing approach is shaping the future
of models like the Juke and the LEAF,

And we look forward to bringing
you more of what's next in the

evolution of AI assisted
vehicle development and

validation in future episodes.