THERESA WERTH: Thank you
all for joining us today.
I know we had to
reschedule this talk,
so I really appreciate
you connecting.
And we're looking forward
to hearing from Ed today.
So Ed Siemens-- sorry,
Ed is from Siemens--
Ed Bernardon.
He is joining us today
to talk about the work
that he has been doing
in collaboration with FIA
and World Rally.
And taking their autonomous
vehicle technology
and hopefully, using it
to make our cities safer
as we move towards
autonomous vehicle technology
being more a part of
our everyday life.
So without further
ado, I'm going
to go ahead and ask Ed
to share your screen.
If you have questions
throughout the talk,
please enter them into the Q&A,
and we will answer questions
this at the end of the talk.
Thank you.
ED BERNARDON: All right.
Thank you, Theresa, for
the great introduction.
And good morning, afternoon,
or evening to everyone.
And today, I'm
going to talk to you
about a practical application
of autonomous vehicle technology
in racing.
It's initially intended to
keep rally spectators safer,
but it has an ultimate goal
to make safer urban mobility.
And Siemens has teamed with
Bentley Systems as our partner,
as well as the FIA,
which is best known
as the sanctioning body for
Formula One rally of sports car
racing.
And in the first
phase of this project
that I'm going to talk
to you about today,
we focused on simulation.
And the simulation starts
at the sensor level
through to the vehicle
level, and we've actually
created 3D models--
very accurate
3D models of the environment
in which these cars, run.
And we've combined
that all together,
and we have what we call digital
twins of the actual rally
cars themselves, as well
as all the components
and the actual trees,
the road, and everything
that they run in when they
run these rally events.
So the goal is to
use the challenging
environment of racing to
prove initially that AV--
autonomous vehicle
and connected vehicle
technology can be used
to keep spectators safe,
and then apply what we learn
there in technology that we
want to apply in smart
cities and urban environments
to keep pedestrians safer.
Now rally is a big sport
throughout the world.
It's a little bit different
than the type of racing
that usually occurs, and it's
not as familiar in the United
States.
So we're going to start
off, for those you that
aren't familiar with
rally, with a short video
so you can get a little bit of
a feel of how rally works here.
[VIDEO PLAYBACK]
So one of the things
that we noticed here
is, first of all, when the
rally cars actually race,
they're racing against
time so they're not
racing each other cars.
And you can see here that they
can become quite unstable.
And it's lots of jumps.
And the car, you can
see going in all sorts
of different angles.
And they race on the pavement.
They race on dirt.
And I think one of the very
interesting things about rally
is that the spectators
can actually stand
almost right on the track.
And that's actually
what one of the problems
is in their search for that
perfect Instagram photo--
as you can see here--
they can actually put
themselves in danger.
And if you look at the number
of deaths that occur in racing,
about half of them occur
in rally, and many of those
are actually spectators.
So the idea here is, could
we apply sensing technology--
radars, LIDARs,
cameras, whatever
might be-- both in the car
and in stationary units
to ultimately
identify spectators
that are in dangerous areas?
And then use that information,
send it back to race control
so the people at race control
then can get somebody out
there, and get those people
out of dangerous situations.
So that's what the
project's all about.
Like I said, the
initial phase that I'll
talk to you about
today is how we
use simulation to
ultimately configure
the sensor systems, both
the stationary ones,
as well as the ones in the
car for optimal detection
of these spectators.
Now, I'm going to take a
step back first and give you
a little bit of my
background in how
I got to where I
am today at Siemens
and on this particular project.
And I actually grew
up in Indianapolis.
We like to think of that as
the world capital of racing.
You've probably heard
of the Indianapolis 500.
And like most people
in Indianapolis,
I was a racing fan.
And while I was growing up,
I had a dream, of course,
of being a racecar driver.
And that really didn't work out.
However, when I was at
MIT for my graduate work
in mechanical
engineering school,
we did manage to get MIT to help
us form the MIT racing team.
And as always, MIT
has a knack to help
you achieve your dreams.
It's not quite winning
the Indianapolis 500,
but nonetheless, here,
this is the MIT racing
team or club,
which was stationed
in the Sloan Automotive Lab.
So that was actually,
my first taste of racing
came through my
experience at MIT.
But as they say
in racing, if you
want to make a million
dollars, you start with two.
So it was great as a hobby,
but as far as my career went,
I began working for General
Motors for three years
as an applications
engineer for heavy duty
transmissions in mining trucks.
And then after my
getting a master's
in mechanical engineering at
MIT, I worked at Draper Labs
with a group that was building
robots to make clothes.
And the one that
you see here, it's
a folding robot
that we worked on,
and it's actually
aligning the seams
for a sleeve on a man's suit.
And in this work, we
gained a lot of knowledge
on how to manipulate flexible
materials with robotics.
And then we applied
that knowledge
to developing machinery
for aerospace and also,
to some extent, the
automotive industry
to manufacture composite parts.
So there's a lot of
similarities in the fabrics.
And we were actually
building real hardware.
They went into real factories.
And we realized that
it would be great
if there was a simulation
that helped us figure out
how these composite
materials deform
as they go from the flat
row onto complex shapes.
And we created a prototype
when we were at Draper Labs,
but ultimately, left Draper,
and started a company called
Composite Design
Technology-- eventually,
it became known as VISTAGY--
where we built
CAD-based software
that would actually
predict the deformation
of composite
materials as they were
shaped into three dimensions.
And most of our
customers initially
were in the aerospace
industry, eventually
racing teams, high
performance automotive.
And we grew the company
from four people
to about 80 people or
so, and then Siemens
acquired us in 2011.
So I've been a Siemens
now, for almost nine years.
And it is a little bit
of a shock at first,
of course, in going from an
80-man company to one that
has over 300,000 people.
But there are the big company
processes and procedures
that you have to follow, but
there's also great advantages.
And one of the good
things about Siemens
is they do allow you to
reach out and innovate.
And as part of the specialized
engineering software group,
I started working with
a different division
in the mobility area.
And we're combining some of the
simulation tools that we have
in our group-- digital
industry software--
with what they have in the
intelligence systems group,
and that's really what led to
the project that we have here,
with the FIA.
And for those you that may not
be as familiar with Siemens,
there's a variety of things
that they do from MRI machines
to trains, gas
turbines, wind turbines.
I'm in the industrial software
group, and on the bottom
here, you can see some
of the different things
that we do within that group.
Everything from lightweighting
for cars to autonomous driving
system, electronics,
and connected vehicle
technologies from our
intelligent traffic systems
group, traffic
simulation, and safety.
So that gives you a
little bit of an idea
of what Siemens does.
And as I mentioned
at the start here,
this was a project that was
done together with the FIA.
And the FIA is well-known as
the sanctioning body for Formula
One, as well as rally
sports car racing.
But there's another
side to the FIA,
and that's the Mobility side.
So the FIA Mobility, you
could almost think of them
as the AAA club of the world.
Although, the AAA is
in the United States,
they have motoring clubs
throughout the rest
of the world--
a total of 246 clubs
in all, 165 of which
are dedicated to Mobility.
And as part of this,
they have a mission
ultimately to provide safe
and affordable transportation
all over the world.
Their president is Jean Todt.
I don't know if you're
familiar with him,
but he led the Ferrari Formula
One team for many years
to over 14 world
titles and later became
the CEO of Ferrari.
And in 2015, the UN Secretary
General Ban Ki-Moon actually
appointed him as the first-ever
special envoy for road safety.
And this next video shows
you some of the things
that they try and do.
This is actually an FIA video.
But ultimately is
looking to help countries
from the smallest villages
to the largest cities
to figure out how
to apply technology
or whatever it might be to
help make urban mobility safer.
So that gives a
little bit of an idea
of the background of the FIA.
And turning to rally here,
when we started this project,
one of the first
things we needed to do
was to try and determine, OK,
we're keeping rally safer,
but what exactly does that mean?
And what we did is we
spent a couple of days
at a rally event,
and they gave us
free rein to move around a bit.
And this shows you some of the
things that typically happen.
Now, people and
photographers like
to find the places where the
car is the most unstable,
where you're going to get
the most dramatic shot.
And as you can see here, they
go across these yellow lines
and they actually
hide in the weeds.
And then when the car
comes by, they pop up.
So that except for
that one moment
when the car is coming by, these
photographers and other people,
they can't be detected.
So the marshals can't get
them to places that are safe.
There's other areas here where--
for instance-- you can
see, here is an actual line
that says do not cross
this boundary here.
And you can see, this is
actually a heavily populated
spectator area.
And in my two days
there, I actually went in
and I said well, I'm
going to cross this line,
and see how long I can
stay beyond the rope here.
And you can see me here,
hiding in the trees.
And those are the
types of spectators
here that we're actually
trying to detect.
We were very fortunate
on this project
to have two rally
champions working with us.
Robert Reid was a champion
in the early 2000s,
and Michele Mouton was
probably, one of the greatest
of rally drivers of all time.
And what we tried to do
is quantify more directly,
where are these unsafe areas?
Where should we focus on
trying to detect spectators?
And it turns out that
spectators initially
gravitate to the most
dangerous parts of any course.
And you can see here, section
one shows [? you ?] turn.
So they like to be
on the exits of turns
or on the entrance,
where the car tends
to be a little bit unstable.
Then there's those big
wide-sweeping turns where
the cars kicking up
all sorts of dirt--
that's another favorite.
But probably, one of
the most popular--
not surprisingly-- is where
there's jumps and the cars
are airborne.
And a lot of the times
when they're airborne,
they can become a
little bit unstable,
and those are especially
dangerous areas.
And then finally, one that
was a little bit of a surprise
was access roads.
So these courses can be 10,
20, even 30 kilometers long.
And wherever there's access
roads, people can come on in.
They don't even need a ticket.
They can come in and congregate
in some of these areas.
So we tried to focus in on these
particular types of features
when we tried to design
and configure the system.
So step number one
was to actually create
a very accurate 3D model
of the environment.
And the way we did this is
there's two parts to it.
First part is we instrumented
a car with differential GPS
so we could know exactly where
it was on the rally course.
And then Bentley Systems--
our partner on this project--
came in, and through the use
of drones and LIDAR, actually
scanned the track.
So here, you can see the LIDAR
cars as it's going through.
We also-- like I said-- we
instrumented an actual rally
car with differential GPS so
we'd know exactly where it
was positioned on the track.
And from this, we
then created a very--
like I said-- accurate 3D model
of the actual course itself.
So here you can see
we're zooming through it.
The accuracy here is plus or
minus a couple of centimeters.
And then, ultimately, this
will be the foundation
for our simulation.
So now we have--
and as you can see here, except
for the color of the trees,
every tree, every rock,
every parked car--
whatever it might be that
was on the course that day--
has now become part
of the environment
that we'll use in simulation.
In addition to
that, we utilized--
Siemens has a CAD
system called NX,
so we utilized that
system to, first of all,
have a physically accurate
model in the simulation.
But also, more
importantly, to determine
where to place our
sensors to make sure they
had a clear field of
view for the cameras.
Make sure the radar
was placed such
that it would work properly
and not behind materials
that might inhibit the signal.
So from that, we
then were able to--
we have a model of the car.
We have a model of
the environment.
And the next step now is to take
what we call the scenarios--
the unsafe scenarios that
the FIA had described to us--
and develop these
within the simulation.
So what you can see on
here is the actual scan
that was done by Bentley
of the rally course.
And what we're
going to do now is
in the next step was to
work together with the FIA
to determine where we
wanted to place spectators.
What behavior we
wanted them to have
relative to the different
positions on the track
and the movement of the car.
And for this, we used the piece
of software called PreScan.
And what PreScan
does is you can go in
and you can designate
areas of movement.
You can see here, the green
dots are actual spectators
themselves.
So you go in, you
place your spectators.
You place your arrows that
determine what their motion
and movement is going to be.
And also, as I mentioned,
you can do things here--
like we're showing--
place your detection
systems, which
need to be placed such
that they can still
[? view ?] spectators.
All of this can be done as
you develop your scenarios.
And this is what it
looks like once you've
designated what you want
the spectators to do,
then they actually become--
obviously-- 3D.
And then you can run
your simulations.
So we'll let you see
how that looks like.
We're starting off here,
this is an actual video.
This is an in-car camera.
And here is the simulation.
So we're looking over
the hood of the car.
And so on the left, you have
what we call the reality model
with the vehicle in it.
You can see our
spectators that we've
populated based on the
specifications that we
got from the FIA.
And on the right there
was the actual vehicle.
So it's very, very close,
very, very similar to
what's actually happening.
And once you have
this set up and you
have the motion, the
placement of your spectators,
now you can start to design
and conceptualize your system,
and try to determine what
the components should be.
So what are the components
of the safety system itself?
So what you're seeing
here is the way
it actually works right now.
There's, of course,
a race control.
Their job is to make sure that
the drivers, the spectators,
everybody is safe.
If there's a problem
on the track--
an animal crossing the track
or people in unsafe areas--
the marshals, which are
the people that are out
on the rally stage, will inform
race control that hey, hold up.
Don't send anybody down
till we get things clear.
So that's the way they work.
Now if you have a 10 or 20
kilometer long rally stage,
it's very hard to have enough
marshals to put them everywhere
they're going to be.
And that's where
this system comes in.
So there's really,
two parts that we
worked on that are part of
a bigger overall system.
The first one is what we call
the stationary system, which
is based on a Siemens
roadside unit that
has a set of sensors with it.
Can be cameras, LIDAR,
radar, whatever it is.
And if you think about the way
one of these races is set up,
there are areas that
are very, very popular.
There they put a
lot of marshals,
and no additional
technology is needed.
On the periphery of those
very popular areas that
are well-marshaled, the ratio
of spectators to marshals
goes way down.
And that's where the stationary
systems could be used.
But then, you have these
long, long stretches
that could be many
kilometers, and that's
where your photographers
and other people
are using the access roads
or hiding in the weeds,
and popping up right when the
car goes by or whatever it is.
And to detect those
spectators, that's
where the in-car
sensors come in.
Now the goal here is
not to control the car,
but actually to send
information back to race control
so that they can activate
the marshals properly.
And the sensor systems
within the car itself
obviously relies
on sensor fusion.
You'll see here we're combining
different types of sensors
to work best in all conditions.
But it's really, not only
fusion at the vehicle
level, but an overall
fusion of the vehicle
sensors, the stationary
sensors, and other capabilities
that the FIA is putting to
work here, such as drones
and other types of
monitoring of unsafe areas.
At some point, it may make
sense to actually provide
a warning to the driver.
It was not part of this
project, as our focus here
was mainly to make sure we
were able to do detection.
So you have your
environment setup.
You've got your actors
doing what they need to do.
The car's in the simulation.
And one of the first
things we wanted to do
is say, well,
where would we want
to put these stationary systems?
You have to be a little
bit careful here.
Because you can't put them
everywhere because then
it would become
very, very costly.
So we tried to look
at areas where they're
blind spots for drivers,
say, where, at the same time,
that blind spot's
also associated
with a very popular area
for spectators to be.
And with the simulation,
we could actually
look at this in 3D.
So if you watched a
simulation, the first run
through here, as the person
starts to across the road,
you can see here
that it actually
is quite difficult for
the driver of the car
to see that person.
So this is how we
started to identify
locations that made sense
for the stationary systems.
On the in-car side, one
of the first challenges,
as well, these
vehicles are going
to be going quite quickly.
And our first check was
to take in-car footage
and just run pedestrian
detection algorithms
that we had used on commercial
autonomous vehicles.
And what we found was if
it was a bright sunny day,
it actually worked
quite well, even
at speeds of over 200
kilometers per hour.
This stage here is
actually in Argentina.
It's probably one of the
most populated races.
They have a lot of
rally fans in Argentina.
And you can see the detection
algorithm is actually
doing quite well.
Now rally will run in all
conditions, so rain, fog,
night, even on snow.
And so it was very
important that we
tried to determine how well
the system would perform
in these different conditions.
And we had access from the WRC--
the World Rally Cup--
to their in-car
footage, and we found
all sorts of examples
of rain and mud
getting thrown up on the
windshield-- whatever
it might be.
And we found that there was
significant deterioration
in the ability to accurately
detect spectators.
And other things, too, like
an initially having a group
of spectators-- as you can
see here in the red block--
all of them with umbrellas
group tightly together
was something that required
a little bit of training
for us to overcome.
So it was obvious
then that we had
to add something to the cameras,
and we couldn't rely just
on images alone.
And this table here shows you
a little bit of our thought
process as we are
trying to figure out,
what's the right
combination of sensors?
So as you can see here, as soon
as conditions deteriorate--
rain, fog snow, glare,
whatever it might be--
a camera alone is not good.
Bringing in radar actually
improved things quite a bit.
Certainly would be better
to add LIDAR, as well.
It adds cost.
And also, one of the
great things about radar--
which was really, really
important in this situation--
is its ability to survive
in harsh conditions.
And so, we really looked
very, very closely
at the combination
of camera and radar
to see if it would
be sufficient.
So once we had our digital
twin-- shall we say--
of the environment
and the vehicle,
we could then take and develop
scenarios for different weather
conditions in rain, night, fog--
whatever it might be.
So here's an example.
This, again, shows you an output
from our PreScan software.
You have a person
here that's just
about to cross the
road, just jumping out
from behind this car.
And you can see here
how in fog, and in snow,
combination of snow
and fog, the radar--
the red dot-- represents
the radar detection
is able to see it.
Although, a camera would have
some difficulty, especially
with the snow and fog.
Additionally here, this is
actually from the reality model
that we created.
Here's a radar detection
on the top there.
You can see it
detects a pedestrian
long before the headlights
actually see that pedestrian.
And this brush that's
on the bottom--
that is an actual tree
that was on the course
when we scanned it.
So we utilized that in
our simulation here.
And radar, although it
can't see through a tree,
it can't see through
brush quite well.
So it was able to
detect a spectator that
was beyond this brush.
And if you recall at the
beginning, when I showed you
that I jumped across
the rope, that's
a perfect example of
the types of things
you'd be able to do
with radar, which
would be a little bit more
difficult when you try and do
that with images alone.
So this shows you
the simulation,
both in the night
conditions on the left.
And on the right
here, if you watch
the person in the
center of the screen,
they're moving actually
in and out of the fog.
The green represents
the image detection.
The red represents
detection by the radar.
And you can see here, as that
person moves into the fog,
the radar continues
to detect them.
On the bottom half of the screen
here, you can see it zoomed in,
where you can actually
see the simulation,
the spectator in it, and
the detection by the radar.
So just to finish
up, based on this,
we then determined what the
proper placement of the radar
would be on the vehicle,
number of radars and sensors
that are required.
And from this then, we're now
prepared to move into the next
stage of the project-- which
we are in the planning stages
right now--
which is to actually
instrument an actual rally car,
and verify what we
saw in the simulation
to determine the
proper configuration.
But the ultimate goal is really
to take what we've learned here
and apply it in the
urban environment.
And on the right there,
you can see our rally car
in our simulation.
And on the left is
also a simulation
based on a digital
twin of a city
combined with a digital
twin of a vehicle.
And similarly there,
very accurate model
to centimeters of the
actual city itself,
and with the placement
of the vehicle,
the sensors, and the
pedestrians in it.
And again, that's
the ultimate goal
for what we're
trying to do here.
And that's about it.
And thank you very much.
THERESA WERTH: Sorry, my
mic didn't want to turn on.
Thank you so much, Ed.
What an exciting project.
And thank you for bringing this
to the MechE Alliance Seminars.
I'd like to open for questions.
So to all of our
participants, if you
have questions for
Ed on this topic,
please use the Q&A or chat
to submit your questions.
I guess I have a quick
question to maybe just
to get us started.
Just from your very
brief closing there,
it seems like the
in-vehicle technology
you're considering using
in our urban environment,
is there any way of
applying the pillar sensors
that you were using, as well?
Have you considered that at all?
I was just thinking
about major events, when
we start having them
again, that kind of thing--
might be interesting
applications there.
Is that something you
guys have considered?
ED BERNARDON: Yes.
In fact, the typical
applications of those
is on street corners, being
able to detect pedestrians that
are crossing the street and
then, with connected vehicles,
sending that information
to an autonomous car
or even a traditional car as
it approaches that intersection
to let them know that
they should slow down.
You could think of
it also as being
able to see around a corner
right as a car's [INAUDIBLE]
a right turn.
You could also
apply this, like you
said, at events, potentially.
One of the other applications
we're thinking about here
is a race application
as well, but to use that
on closed racing tracks
where [INAUDIBLE] detect
the position of
the, as they say,
corner workers or the
marshals to make sure
they stay safe as the race
cars continue to go around.
THERESA WERTH: That
makes a lot of sense.
I'm thinking, too, it
was impressive thinking
about how the in-vehicle
sensors were sensing people that
were not visible
to the driver yet,
and I could see that
could be even implemented
in traditional vehicles.
We're seeing a lot of
the technologies that
are being developed for
autonomous vehicles starting
out in a traditional vehicle,
so that could be a really
interesting way to give
drivers information
that they don't have.
Yeah.
Our participants are being
very shy about questions today.
So just reiterating, if you
have any questions for Ed
before we close
the session today,
please type your questions
in the Q&A or into the chat.
You can also raise your hand.
If there are not any
questions, then we'll
go ahead and close shortly.
OK, Audrey is thinking
of her perfect question,
so we'll hang tight.
Thanks, Audrey.
ED BERNARDON: Just a
comment on what you were--
THERESA WERTH: Oh, please, yeah.
ED BERNARDON: The ability
to see around a corner
or [INAUDIBLE] be really takes
control of autonomous cars
to the next level.
[INAUDIBLE] you can to make the
autonomous car work on its own.
But if you can
leverage other systems,
like vehicle
technology or cameras
that are located elsewhere,
it makes it a little bit
easy to solve some of the
more difficult corner cases.
And in this case, as you saw
there, it is in-car sensors.
We also have the stationary
sensors on a pole.
But we also have the drones
that are going to be out there,
and figuring out how to best
use all that information to keep
people safe is key
to figuring out
how to use all the
information you have
in an urban environment,
including things
about overall movement of
traffic and control of traffic
lights and whatever
else it might be.
THERESA WERTH: Yeah.
Yeah, I agree.
Although part of me
is thinking, well,
with those
photographers that just
put their cameras on
the drone, then maybe
we would be a little safer.
[LAUGHS]
ED BERNARDON: [LAUGHS]
THERESA WERTH: OK, we have
a couple questions here.
So was thermal imaging
a possible solution
to detect viewers?
ED BERNARDON: Yes, absolutely.
And in fact, that's
still in consideration.
But that's a
possibility as well,
especially at close range, so
that could be used as well.
Again, [INAUDIBLE]
a sensor you could
add to the suite for a sensor
fusion approach to this one.
THERESA WERTH: Got you.
So another question-- could
the data from the sensors
be integrated into
a heads-up display
to identify objects and
people in poor visibilities
in both race and passenger cars?
ED BERNARDON: That's
a great question.
And in fact, one of
the things that we'd
like to try and consider
in the next phase
is, even for race control--
is let's say that you
detect that there's
a pedestrian or a spectator
in a bad location.
It would be great
for race control
to actually see what the
sensors are actually seeing,
have a reality model, a
three-dimensional model
of that section of
the track as well
as their location on
the map-- like you
might see in Google Map.
And that gives you a much
richer environment for them
to know how to, ultimately,
then send the marshals there
to find the spectators that
are in dangerous areas.
And ultimately, that
could also be applied
within the vehicles themselves.
There's no [INAUDIBLE]
you couldn't do that.
THERESA WERTH: Yeah.
Yeah, thank you.
So next question is actually
about working with FIA.
How has that been for you?
Have you found that they're
open to incorporating
new technologies from research?
ED BERNARDON: Absolutely.
In fact, they have teams that
focus on those very things.
So we're with the safety
team, and they're always
open to new procedures and,
obviously, new technology
to keep people safer.
I will say this about
working with the FIA--
and we have projects with
them both on the racing side
and the mobility side.
They do have that racing
mentality, and [INAUDIBLE]
actually quite good in that it's
very, very efficient mentality.
You have to work quickly.
You have to know how to
focus on what's right
and get to that point
as quickly as you can.
And they are very,
very cooperative.
So there are technical
people on their side.
They have a full
engineering staff
that we work with quite closely.
And as [INAUDIBLE] at the
beginning, figuring out,
what is the real specification?
What are you [INAUDIBLE]
trying to do?
In that sense, giving us access
to former champions and safety
officials--
that was a key part to
making sure this project did
get focused so we could
move at racing speeds
through the project.
THERESA WERTH: Great.
Thank you.
I have a question that maybe--
it's not a technical question.
So in Rally, they typically
run one car at a time.
Is that correct?
ED BERNARDON: That's correct.
THERESA WERTH: OK.
Yeah, because I was
wondering if they ever
get into a situation
with damaged
vehicles or that kind of thing.
But it sounds like they would
just clear that and then run
the next car, so got you.
ED BERNARDON: Exactly.
Right, yes.
If there is a car that's
had a mishap or crash
or whatever it might be,
they'll hold up the next one
until [INAUDIBLE] is clear.
THERESA WERTH: So they have
a good system for that.
They don't need your
technology, per se.
ED BERNARDON: You
can have situations
where people are on the track.
And what ends up happening there
is, when people that are not
monitored well get
onto the track,
they have to stop
the whole thing.
And of course, you're
there to watch these cars.
So detecting them
certainly keeps people safe
but also adds to the
entertainment value
because you can keep
the event moving.
THERESA WERTH: Yeah, that
makes a lot of sense.
We have another question here.
Is any of the data that is being
sent to the car also being sent
to the driver or co-driver?
ED BERNARDON: Well, as I showed
on that one slide, right now,
no.
The first step is to make
sure we can do the detection
and get the systems working.
And ultimately, that's
certainly something
that could be considered.
Now, the one thing to
remember is these drivers
are concentrating on what
they're doing, especially
in these dangerous areas.
They're on the
brink of disaster,
but that's what
they're paid to do.
And so you have to be really,
really careful bothering them
when they're in that state with,
let's say, a false positive.
You don't want to
create accidents, right?
Step one is, let's make sure
the sensing system works.
And then potentially
someday, that
may be where it goes
but certainly not
in the foreseeable future,
especially not in Rally
where the spectators are
so close to the road.
THERESA WERTH: Yeah, definitely.
And also, it's competition,
and so that's probably
why they have marshals
to deal with the safety
because they want to win.
[LAUGHS]
ED BERNARDON: That's right.
They want to focus on winning.
They'll follow the
rules most of the time.
THERESA WERTH: Yeah.
[LAUGHS]
ED BERNARDON: And leave
the safety to the marshals.
THERESA WERTH: There you go.
Very good.
It takes a village.
ED BERNARDON: That's right.
THERESA WERTH: A couple of
questions here-- so just
generally, how many of these
rallies have you attended
and where?
Just to build on that, has that
played into your design at all?
ED BERNARDON: Well, as
I mentioned earlier,
the first thing that we did
when we were getting started
here was to actually
go to an event.
Like I said, we met
drivers, former champions
at those events, and pretty
much allowed us to roam through.
We had, of course,
guided tours, and they
would point to us about
spectators doing what
they weren't supposed to do.
To actually ride in a shakedown
run with Michele Mouton,
who's the [INAUDIBLE] champion--
and she would point to
people and yell at them
when they were in the wrong
places, and she'd turn to me
and say, hey, you see that?
They're gone now.
In 10 minutes,
they'll be right back.
She'd go, your system
has to find those people.
So yes.
And I think that, in developing
any type of a software
or hardware solution-- and
certainly through my career
and the people that
I've worked with,
even going back to Draper
Laboratory when we were
building those machines for
[INAUDIBLE] manufacturing
composites or
manufacturing apparel--
again, one of the first
things we would do back then
was to actually
go to the factory
and lay up composites
ourselves so that we truly
understand the problems.
THERESA WERTH: Great, thank you.
Well, it is 12:45, so I'll
say, last question, OK?
So the question is, how many
marshals are there, typically,
per kilometer of track?
And what would be
the typical delay
between the centralized
controller learning
about an issue and
the marshal arriving?
ED BERNARDON: Oh,
what a great question.
THERESA WERTH: I know.
Audrey's killing it today.
[LAUGHS]
ED BERNARDON: Yes.
That's a great question.
Well, like I mentioned
earlier, there are areas,
well-marshalled areas,
very, very popular--
you have hundreds of
people gathering--
obviously, no
technology needed there.
In the areas where,
adjacent to those,
the spectator and marshal
ratio goes a little bit higher,
possibly that's
where we might deploy
some of these stationary.
There you could have a
very quick turnaround
because the marshals are there.
They're just not looking
in the right [INAUDIBLE]..
Now, the last one, which is when
the car is sensing these people
along is 20-kilometer route
and they're popping up
in the middle of nowhere--
well, you detect them.
They're 5 kilometers
down the road.
There isn't a marshal that's
nearby [INAUDIBLE] drive
over there and get
them out of there.
Right now that would
be-- it depends where
you find them, how far away.
But the signal would get back,
obviously, almost instantly,
back to the race control.
Reacting is going to be a
little bit slower, depending
on how far away they are
and what the conditions are
where they're located.
THERESA WERTH: Gotcha.
OK, well, thank you so much.
I've learned a lot, and
it's been a lot of fun.
Thank you, Ed, for
taking the time
to be here for the
MechE Alliance Seminar
and for everyone for joining.
I just want to let you know
about our upcoming seminar
before we close.
So we've been trying to hold
our seminars once a week.
But next week we'll
probably be taking a break,
and we'll be regrouping
on Wednesday, July 1
to hear from another
one of our MechE alumni,
Dr. Albert Moussa, who has built
his career on improving safety
around both fires
and explosions,
so it should prove
to be quite exciting.
So I hope you'll join us.
When you log off, you'll
actually receive a pop up
to join our mailing
list if you'd
like to be notified of
the upcoming seminars.
Thank you again, Ed.
And thank you, everyone.
Have a wonderful afternoon,
or evening, or day,
depending on where
you're joining us from.
See you next time.
ED BERNARDON: Bye.
Thank you very much.
THERESA WERTH: Bye.
