The Garage by Sonatus

Today in The Garage,

we're recording live on the
show floor of AutoTech Detroit.

Our guest today is
Christoph Lloydfed,

product manager from COMPREDICT.

COMPREDICT is helping lead
the way with virtual sensors.

And in the episode,

you'll learn what is a virtual
sensor and how it provides

upstream and downstream benefits
for OEMs and consumers alike.

It's almost the perfect application
of software defined vehicles.

Let's hear about
virtual sensors from COMPREDICT.

Let's go.

Welcome to The Garage.

I'm John Heinlein, Chief
Marketing Officer with Sonatus.

We're filming live at AutoTech
Detroit so you can hear some of

the background noise.

My guest today is
Christoph Loytved,

product manager from COMPREDICT.

Christophe, welcome to The
Garage. Hi, John. Thank you.

We always like to start by
getting to know our guests.

Tell us a little about
you and your background.

Sure. So, I studied
at TU Darmstadt.

I did my master's over there,

a little bit of time at
UBC as well studying.

Then I went, for a
job at COMPREDICT,

R&D engineer.

So I developed virtual
sensors in the first years.

I think it was
around five years.

And now I recently switched
to product management,

now being responsible for tire wear
and brake wear virtual sensors.

That's great. And you're
based where in Germany?

It's, in Frankfurt.
Darmstadt, actually.

That's great.

So, we we always ask our guests
to tell us a fun fact about them.

Tell us your what's
a fun fact about you?

Alright. So for me, I
love dancing, actually.

So, specifically salsa and,

a little bit of West
Coast Swing recently.

So really, this is something
I like to do wherever I go.

So actually, here in in Detroit,

I also went dancing on the
weekend. That was really nice.

It's a little bit like an
in a different language.

I don't know if you know that.

It is. I mean, it's and
it's also universal. Right?

You don't have to speak
the same language,

but you can still dance.

This is right. This
actually, this is the fun.

I can go wherever I like.
I went on Hawaii as well.

I didn't know they were dancing
there. Yeah. It's awesome.

I tell you.

So I always try to come up with
a fun fact back to our guests.

I think my fun fact would have to be my
wife and I, years ago, we got married.

And many many couples, you know,

learn a kind of a dance routine.

So we actually took classes.

We learned a dance
routine, and we did a,

did a dance in our
in our wedding.

So there's my
there's my fun fact.

Awesome. That is awesome.

So tell us about
COMPREDICT and your role.

Sure.

So COMPREDICT was
founded around,

2016. Sorry about that.

It was a spin off of the TU
Darmstadt, so mechatronics.

Back then, we are mostly concentrated
on damage estimation gearboxes.

Nowadays, we really
focus on virtual sensors.

So basically making the most
out of existing data in the

car, really, ensuring
that we have,

better quality data in the car,

replacing hardware sensors
or giving additional,

sensors information to it.

Yeah.

And making virtual sensors
a gold standard and

So you you have to start
for our listeners to help us

understand what the heck
is a virtual sensor?

This is right.

This is a strange concept
for some people. Yes. Yes.

Explain it.

We get it all the time. We
get it all the time, John.

So, really, how it works is it's a
piece of software running in the car.

It takes all the sensors
which are existing in there.

So there's a lot of
like wheel speeds, accelerations, and on.

So so real signals,
hardware based signals.

Exactly. In the car. Yeah.

And it smartly combines them
to to replace hardware or

to add additional
functionality to it.

So it's it's basically a mix
of physical models and machine

learning, which makes, like,

your your your data that you already
have more valuable than the cloud.

So deriving different data
points from data you already

have into new new ways.

Yeah. You could say that.

Really, really interesting.

Really interesting.

And so what what are some of
the applications of can you

give us some examples to
make it easy to understand?

Sure. Sure. Let me give you
two examples of that. Alright?

The first one, headlight
leveling, for example.

And headlight leveling,
you have, like,

two to four sensors in there
measuring the ride height.

And with these kind
of, like, sensors,

you can adjust calculate the
pitch and then adjust that light.

With our virtual
sensor technology,

we can get rid of those
hardware sensors measuring this

ride height and just do fully
virtual or partially virtual as

as our customers like and thereby
save costs for the company.

Okay.

What's another example?

The other example would
be tire wear, for example.

This one is not is not
replacing hardware,

but it's actually giving additional
information into the car.

So for tire wear
to to my knowledge,

there is not really
any hardware measuring,

at least not in serious cars,
serious production cars.

Yes.

And, basically,
what we're doing,

we're giving this information to
the car and thereby giving

our customers the opportunity to
know when tires need to be replaced.

That's fantastic.

And so so thinking about
the benefits to OEMs,

you touched on it a little
bit, you know, cost savings.

But what are some of the the
motivations to shift from

hardware-based sensors
to virtual sensors?

Yes.

We had talked about the the
the hardware replacement,

which I think is the
obvious case, saving costs.

But then, like, really, like,

getting also additional
information into the car,

for example, like getting back
to the tire wear algorithm.

It really helps, to understand
when a tire will be worn,

to target customers really
specifically to go back to the

OEM-branded workshops. Right?

So you can really have the
spare part business increased.

We're estimating around 180 Euros
over over lifetime for a car.

So that's actually good money.

That's interesting.

So so upfront cost savings from
sensor you don't have to put in

the vehicle, downstream
savings from,

I would say, value added
things you can do Mhmm.

That maybe benefit it probably
benefits the consumer as well

from a safety perspective.

Of course.

Benefits the, the OEMs,
potentially ongoing revenue models.

It's interesting.

As I reflect on software
defined vehicles and we talk a

lot about this from
many different aspects of the

podcast, there's so many,
additional signals being captured,

additional data points being
captured from vehicles today.

But companies, OEMs don't always
know how to take advantage of that.

And so either in some cases,

that data is just not used
or perhaps it's captured but put

in a giant pile
that is never...so

I think this it seems like
what you're doing unlocks the

opportunity to mine
that data in new ways.

Yeah. To create new value.

And I imagine the great
part about virtual sensors is you

can add them after production.

Yeah. Everything's
possible with these. Right?

There's just a multitude of
possibilities that we're having here.

So in in in production, like,

you you could save the the
hardware, but afterwards,

even if the hardware breaks,
right, post production,

you could say, like, do we
replace it with the hardware,

or do we actually put them
in the virtual sensor that

COMPREDICT is doing? So there's
multiple options there as well.

Doesn't need to be
only on production.

This really depends.

So, tell me about, your
production adoption. Yeah.

How how wide is is your
market adoption so far?

Of course.

So one thing is talking about
dreams and stuff like this.

It's always comes down
if it's really used.

So we're quite happy that
we can actually talk about some

customers that we have.

That's not always the case.

Before I would talk about this,

perhaps let's talk about one
big factor which enables us,

which is Toyota, which
invested recently into us.

So it's it's not only us
believing in this kind of

mission, but it's
actually also Toyota Motor

Growth Fund, which
invested into us.

But then, of course,
customers are more important.

And one one person or one group
we can talk about is Renault

Group.

So really just, like,

all the brands under the
Renault Group like, Alpine,

Dacia, they are all adapting
our virtual sensor technology

for tire and brake wear.

So we're estimating by 2030
around ten million vehicles.

That's fantastic.

So across the Renault Group,

adoption of your technology as the
first application for tire wear.

That's huge. That's huge.
Yes. Congratulations.

Thank you so much.

And let's see let's
see if we can, like,

put our numbers soon on our website
as well as you do, you know?

Well, thank you. Thank you
for the kind words. Yeah.

Synodos is in, depending
when you watch this,

four million vehicles today.

But by the time you watch this,

probably five or many
more millions of vehicles.

So we're we're growing rapidly.
But congratulations to you.

That's a great achievement.

Yeah. Thank you.

So if we think bigger picture,

it it occurs to me that these
virtual sensors are really a

great application for
software defined vehicles.

What are some ways that you
think that that SDVs really are

a perfect match for
virtual sensors?

Well, there's

actually a lot of dimensions
we could talk about.

Right?

One way for us is really, like,

we're making efficient
use of this all this data.

Like, we're running
our virtual sensors.

We so we have the ability to
run virtual sensors easily in

these cars, crunch the data
there, upload the data,

which is only relevant and not all
this this the big data amounts.

Actually, the
integration we own...

That's a really
interesting point.

So so think about it in terms
of instead of having to send

all the data to the
cloud for processing,

the virtual sensor in the
vehicle can derive the data and

send the derived data,

which is probably far
smaller...Of course...

Than the, than the
the source data.

This this is how it
should be. Right?

Like, we should only send relevant data
and not all all the data to the cloud.

And, as of now, for
some information,

you have not simply
not the possibility

to send it from the car.

But with our virtual sensors,

we can create it in the car
and then send to the cloud,

like the tire wear, for example.

There is no information
on tire wear.

So that's a huge potential
cost savings for data management and

and data manipulation.

Yes. For sure.

That's great. You were
mentioning integration. Sorry.

Yes.

Thank you, integration is obviously
also a very important point for us as

we're trying to build
really scalable software,

which runs in many cars.

Right?

So for us, we don't wanna go
to through all these, like,

points of, like,

integrating in this car and
with this kind of software

interface and this kind
of software interface.

We really want to have a
standardized way to access this

kind of data, to
put our software.

Optimally for us, we just plug the data
to the virtual sensor, and it works.

So we believe that in virtual
with the SDV kind of developments,

there will be a clear way
of implementing our third

party software into OEMs cars
without also the hassle of OEMs

not knowing how to do that the
best because it's standardized.

Right? So this is
really big for us.

Yeah.

And that really speaks to
the broader question of SDV.

You know, there's been a
feeling over the past few years

that, SDV was a buzzword,
but people were wondering,

was it real, or is it
just an interesting idea?

What I hear you
saying is that this,

this and and things like it
are real applications that can make

SDV really valuable in in
creating new applications.

Yeah. Yeah. I agree.

I think we are one of those use
those of one of those many use

cases which can make
it really helpful,

which can make make most of the
SDV that we're we're discussing.

And it's really an
application case,

not just a thought about what
we could do theoretically,

but actually
something we can do.

And we know already it works.

That's fantastic.

So, Sonatus, one of
our claims to fame is,

high resolution data capture
among many other things.

So I it feels like a
great synergy with you.

We we're really excited
and look forward to,

continuing to work
with you in the future.

We're thrilled for your,

your great success with Renault
and look forward to more

customer adoption
for you coming up.

Thank you so much. Yeah.

Yeah. Hey. Thank you for
joining us on the podcast.

Thank you. Thank you, John.

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