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So hello, everybody.
Welcome to the exciting stage.
First talk on day three.
We are really happy to have Alastair and
ours here.
All right.
They're from the French Cocoa Group,
which is an interdisciplinary group
that is researching stuff and
trying to, on one
side, work on their own, but also get
together, discuss, debunk ideas
and help each other with their projects.
Defend Cocoa Group was founded in
2013 by ours, if
I remember
correctly.
And yeah, they're
researching a wide range of interesting
and relevant topics,
and they are formed up to now
non funded and completely autonomous.
Which brings us also to the subject
of our talk, because
those two are going to talk about
self-driving cars.
And yeah, I'm really
interested in what you have to tell and
to stay tuned. Yes.
Thank you.
I just needed to unmute myself.
Thank you so much for this kind
introduction.
And before we
get anything
wrong out there, it was a bit of
a bit of a misunderstanding.
This is not part of and Coco.
Maybe I can.
Maybe I can convince him to
be part of a coca one time.
That's fine. No, but he's taking part.
That's fine.
So we're going to talk about let us go.
In Lydia's case, we're going to talk
about the case 100 billion
dollar delusions with self-driving cars,
and we are talking
about it as a delusion and
hoping to create an interesting talk
with all touching all kinds of topics.
But we're going to end up with a question
of whether the car in itself should
be abolished and of course, what
interesting mobility concepts would
follow if we try
to do a encompassing
futuristic mobility that's good for
everybody.
Thank you.
Let's start with you.
I'm sure you all know here he is, looking
extremely serious.
Is a very serious guy.
As you know, this is him in 2019.
I think we'll be feature complete and
self-driving this year, meaning the car
will be able to find
you in a parking lot, pick you up, take
it all the way to your destination
without any intervention.
And just to make sure you know how
serious he is, he says.
I am certain of that.
That is not a question mark.
So let's see how they're getting on.
They actually released Autopilot a year
later.
A bit later than they planned.
And it's now in beta.
Here we go.
You can let it go out.
Oh, oh, oh, jeez.
Oh my God.
Yeah.
Oh, give us a good example, a good
example of this. Still, Peter
does have control on terms who industry
just left him, just stared
directly into it
into the back of this parked car, and it
wasn't going to break.
So it's still detecting those rings on
the road as a
cheese.
Yeah. Oh my gosh.
Well, this is why we don't have people
with us normally.
OK, so, so there might be
a few problems there.
And this is really what our talk is
about.
Over the last few years have been
hundreds of billions of dollars spent in
this field.
And we got that from a survey in 2017
where he said 80 billion and we know that
there was quite a few of the billions
more than that have been spent in the
last few years where it's really peaked.
And that includes startups, including
the big tech companies, as you know, in
automotive vehicles, plus a whole network
of other suppliers and consultants.
And we call this for the sake of
convenience, the technology mobility
complex.
And they're all convinced and trying to
convince us that self-driving
cars are just around the corner.
So how are they getting along?
Well, we've seen Tesla,
and certainly they have something to
market. It is in beta or $7000
or somewhere around that, and they've
been three fatal crashes so far.
It's very limited compared to what
they said it would be.
You've got Audi, for example.
We've also tried to bring something to
market.
This is a song that has jam
pilots.
They convince regulators in
Germany that it was perfectly safe
to read your emails whilst driving
with with jam pilot on.
Unfortunately, they didn't quite convince
any other regulator in any other country.
And as a result, it's been withdrawn from
the market this year, basically because
if there was an accident, it would
effectively be out his fault.
Then you have, of course, the tech
companies, the big tech companies.
Uber has been very prominent in this
field looking for mobility
as a service. That's a concept will
come back to.
They started in 2015.
They've invested over a billion dollars,
but unfortunately, Uber has had some
issues too.
And we'll talk more about that later on.
But there was a fatal crash in 2018.
In November this year, just a few weeks
ago, they announced they were giving up
altogether.
They sold off and, we should say,
paid to give away
their self-driving project to
another company.
Amazon got into the field with
this startup, Zoox
and very recently, a little late
to the game.
But ASICs has gone for the all
completely autonomous and its
new build design.
This was released a few weeks ago.
However, they were a little bit coy
about when this will actually be working
on the streets. Or they would say, is
it's definitely not going to be 2021.
Our view is it's probably going to be a
bit later than that.
And then you have the old automakers also
involved. And what most prominence among
those is General Motors, who bought up
Cruze.
They've been investing in this, and since
2013, they claim
to have a commercial taxi versus a robo
taxi service available
by the end of 2019.
Well, that hasn't quite materialized.
If you go to San Francisco, you certainly
see the cruise cars all around.
They've recently announced and very,
very happy about this.
They recently announced they have
driverless vehicles for the first
time on five streets in San Francisco.
They will only be working in low traffic
and at night, and there's will be one
person in the car with an emergency stop
button. So a little far away
from a commercial service.
And then, of course, you had the big
player always, which is Waymo,
which is, of course, owned by Google.
They've been around since 2009
with a number of different iterations.
You can see the first version they have
here, the Firefly, but someone
must have been very rude about that to
somebody at Google because these are the
latest versions you've got here, which
are obviously really, really mean
Jaguars.
Anyway, they've been going in Phenix
for a long time since 2017.
They announced in 2018
that they'd have a fully driverless taxi
service. Then they announced it again in
2019.
It's taking a little bit longer.
They finally announced it again in 2020,
and they actually kind of do have
driverless taxis in Phenix,
but it's limited to a 50 square mile
area.
They actually have super sized remotely.
So somebody can intervene
from afar, a safety
driver effectively remotely dialing
in. And they're also
they need perfect weather.
So again, very limited
and is always in where you see
the successes in this area, it's always
in these highly controlled environments.
Phenix is an ideal place
with perfect weather, easy streets, no
hills.
And then you've got other projects like
this the Ford Argo.
Again, a lot of investment two
billion from Ford, even further
investment from Volkswagen, and
they're developing delivering fruit and
veg in Miami, which is very nice again in
this very small area.
But that requires two safety drivers.
And then he get this, which is a very
successful project.
The optimists right project
that's in a in a retirement
community, and those are the kinds of
places where self-driving cars
seem to be working.
Not so much on busy urban
streets.
And you could go further than that.
And this is a quote from Dr. Jill Pratt,
head of Toyota Research.
She says I would challenge anyone in the
automated driving field to give
a rational basis for when Level
five will be available,
and we'll talk a bit about Level five,
but that means a fully autonomous
vehicle, which
as we would expect.
And you know, here's
John Krafcik, the CEO
of Waymo.
In a candid moment a couple of years ago,
he conceded, You know what?
Self-driving car technology is actually
really, really hard.
Who could have possibly thought that?
And I'm going to hand over to auris to
explain a little bit more about why
that is.
So we're going to talk about
first. Why do we call it a self-driving
car? If I may.
And so to answer
it, we need to look at language and
technology. And in general, it is
interesting to note that
words gain meaning
through the use they
use. And they can they can.
If you want to say that, lose meaning,
but they can truly change meaning
and get ambiguous by the
wide acceptance of more than one
meaning in society
and autonomous has, by now, not
only one anymore.
If we want to say the least.
So if you look at a
definition of an autonomous car.
Hang on, hang on, stay with me.
If we look at the definition of
an autonomous car, you get the obligatory
definition of a vehicle
capable of sensing its environment
and operating without a human
environment. A human passenger is not
required to take control of the
vehicle at any time, nor is
a human passenger required to be present
in the vehicle at all.
And an autonomous car can go anywhere
the traditional car goes and does
everything that an experienced human
driver does.
Now that is a high aim,
would you? Yeah.
So we land in impractical ambiguities
with this, not only because the
traditional definition of autonomy
is something completely different.
I'd like to share with you just a very
short version of
what we read when we type it into the
Stanford Encyclopedia of
Philosophy, which is a nice
source and it gives you individual
autonomy is an idea that is generally
understood and referred to to the
capacity to one's own person,
to one's life,
according to reasons and motives taken
as one's own and not
as the product of manipulative or distort
distorting external forces.
Now, this is not a new point.
This point about autonomy has been made
quite a number of times, especially in
terms of autonomous cars bus, but
it is important to mention nonetheless.
And we will see
how difficult and how ambiguous
it is when we go to the automation levels
that are utterly defined via automation.
Sorry. The automation levels that are
utterly defined by our autonomy
and autonomy is supposed to be the more
complex concept.
And this is supposed to, you know,
seems a bit the other way around.
So the society
of the automotive engineers.
Oh, by the way, insurers have
have identified autonomous
ambiguity as a
potential
reason for an increase in
crashes due to confusion.
So that's not. It's not only
a point of philosophical
extent.
So the society of Ultimate, yeah, that's
it. Thank you. The Society of
Automotive Engineers, the SAIC currently
finds six levels starting by zero,
which is why we ended five, obviously.
And.
These levels go from fully
manual to fully autonomous,
and these levels have been adopted by the
U.S. Department of Transportation.
Now there is a standard out there that
I'm going to going to come to and look
at the wording that they use to
be sure what we're talking about.
But in general, it's it's it's safe
to say level one assumes
that a system can assist the driver with
one driving task just one.
And so it fits into
this category, which
is the
adaptive cruise control HCC
and level two systems such as
Pilot Assist can assist with, for
example, two tasks, and Level two is
the highest level of automation currently
available that will lead to
a discussion on strategy and development
that that the one is trying to erase
the driver and the one is trying to kind
of see through the driver and learn from
the driver.
The first one is trying to skip level
three, and the second one is kind of
putting great emphasis on
it. But to understand where
that lands with all the assistance
systems and the the wording, we need
to map it somehow, which is why I looked
at the taxonomy and the definitions
for related terms
to driving automation and systems
for on road motor vehicles, which
just the long ass name for basically
the standard J.
36, 20
18 06, which is
the standard, which is where it's all
defined.
And this standard gives you
roughly this explanation.
And me as a philosopher, I need
some, some proper words.
And I do.
As I said, I am utterly aware of the fact
that we could use in different contexts
words differently due to different
reasons. But we should also make sure
that we do understand each other
and the context in which we use those
words. And with an
and we should be clear on at
least our own extent of meaning
when we use the words and
and then especially with
if it's used by others, sorry, I've got a
little,
uh, scrolling problem here.
OK, so this document that
I was talking about refers to three
primary actors and driving the human
user, the driving automation system and
interestingly, other vehicle systems
and components and these other
vehicle systems and components
or the vehicle in general terms.
Sorry, I'm having serious trouble
scrolling here.
So it boils down to
processing model modules and operating
codes that overlap in the automation
system and the subsystems that are
supposed to somewhat actor, primarily
actor wise, can we go back?
One primarily actor wise
are supposed to be distinguished.
You know, I'm going to be through with
this in a second. But just so you know,
these automated automation levels
are defined by the role of those primary
actors and how they act in
traffic.
So they're trying to match the automation
levels onto the dynamic driving task
performance and the DDT.
So dynamic driving task DDT fallbacks,
which is usually the driver, especially
in the systems that we're talking about
nowadays.
But this is supposed to be done by the
system completely, which we're going to
talk about a little bit later.
So, for example,
a driver is oh yeah, and
it's necessary to see that it's about
the the way that the system
is designed, not necessarily
the actual performance of a given primary
actor. For example, a driver, for
example, who fails to monitor
the roadway during engagement
of a level one adaptive cruise control
system still has the role of
a driver, even though he or she is
neglecting it, which is basically the
most easy example that you can pick and
all the others bring you into actual
trouble. This one seems clear, but the
others really don't.
OK, so we're talking
problematic in decision making
and
and predicting and responsibilities.
So these levels apply
to the driving automation.
And you can see what I've just been
talking about
here. It's a little bit moved,
but I've tried to repeat the the
definitions up there.
You can see that it matches roughly to
the scale, you have the systems, you have
the human driver and you have the other
system components which end up being some
driving modes.
But some driving modes doesn't
give you too much information, obviously.
So while
we're trying to
get informed
about the extent of assistance systems
and how responsible they are,
we get that and end up being really
confused and that's a bit of a pity.
And we can see this.
I think we can we can move on
to that. We are already so.
So some of those subsystems,
even in the definition, are explicitly
excluded from the taxonomy
that are supposed to describe the
automation. So we have some automated
subsystems explicitly excluded from the
automation taxonomy.
And to understand what that means is
only possible if we look at what the heck
we're talking about. So what are these
ADAS systems that we, we
we have to understand language wise
and these features and basically
boil down to perception.
So we have this is that every, every
autonomous car roughly
has if and you can
always argue with the wording and you
could always argue with the nuances, but
roughly you have a perceptual system and
a decision making system and then you
have actual orders of those decisions.
But they consist
roughly of depending on whether
you use a Tesla or not, because Musk
doesn't believe in later.
But um.
And for a good reason that I'll come back
to later.
But the idea is you have surround
views around view cross-traffic alert,
park assist, emergency braking,
traffic sign recognition, lane departure
warning, adaptive cruise control,
collision avoidance, rear collision
warning, surround view, park assist and
all these kind of things.
And they amount to different
functions with different extents of
automation, right?
Lane keeping has more automation
than the, well,
I'm just not I'm not even going to going
to go there because that's that's
what is so difficult about it.
So what's interesting and
and necessary to understand in general is
that we have perception a perception is
made up of computer vision and sensor
fusion, and
it's all about understanding the
environment.
Computer vision uses cameras, and it
allows to identify cars and pedestrian
and roads, and sensor fusion uses and
merges data from the sensors,
such as a radar or a slider,
or the complement data from the cameras
or infrared when it's close to the car
or all kinds of things, depending on the
project we're talking about.
But decision making
is on the prediction and decision side,
and it's not as perception
doesn't seem to be yet, even though
impressive. It's not
developed sufficiently to just roll it
out as they're claiming, and that's what
I'm trying to get here.
And while showing that
it is, it is interesting to look
at those things now.
All these things, we can
come back to enter the discussion, I've
got to move on.
Yeah, yeah.
So.
Oh, now we're going to talk about
an automated,
sorry assistant, automated
driver assistance systems, that's what it
is. And and the
interaction with the driver.
And there are two
slides on this because and this goes back
to what I mentioned in terms of the
in strategy.
The first.
Let's say bulk of ADAS
systems are supposed to take away the
wheel to take away the need for you to
take the wheel and the second bulk
or what they are now recommending
is basically making
you see through in your decisions
so that the system can land for you from
you. Because what drivers
can do
is still much better than
what cars can do.
And since it didn't really
work out to skip free to skip level
three, they're now trying to come back at
it. So while
the downside of ADAS systems
that doesn't seem so problematic, you
know it's all confusing, which
interestingly actually has practical
consequences. People don't know what
they're doing.
And thusly, they're creating accidents.
But also it's misleading
in terms of wording. I had that to a
point because I think they can take a nap
while driving, which I found very
interesting. And now if we
if we look at we can, we can move.
If we look at the next one, you can see
that there are recommendations
recommended escalating tension
reminders for level two automation
and level two automation again.
We we started from zero.
We want to go to five and we have level
two right now, and
it's already ending
with the car taking over from
you and locking you out.
So so what I'm trying to say
is first, they tried
to erase the driver, and now
they're there. They're doing everything
to make the driver back up the system
that they developed.
And and to do that,
the the consequences are the costs
that come with it might just be not that
desirable depending on what you're
looking out for or have a problem
with. And of course, I'm hinting
to privacy later on.
But but even
if we leave out that very massive,
unbelievably massive topic.
Oh, and I didn't even go into,
I think, observation
and control in terms
of the ambiguity.
Um, even if
we don't think about that privacy
issue, that turn and development shows
that the hype is is leading
to developments.
And I mean, massively and greatly pushed
developments that might not just be
that well aimed.
So.
If if the first
galloping in this direction leads in
galloping in the opposite direction
and still doing it with a lot of
enthusiasm, it
might just be a thing to notice.
And and it's not a it's
not a grumpy point of
pro progress, but you know, and
the better and in a good way.
Oh, OK, where are we?
Failures in perception I've been
mentioning, and I don't think I have the
time to really explain what it boils
down to. But here is
what part from the driver
not paying attention.
One of the reasons why this
is problematic is because
because of the driver's confusion, but
also because it's really unclear
what the two automated systems
can do.
And I've been
reading up on it, which led away
on 73 mapping, 2073
mapping.
Hang on.
Go on. Hang on.
OK, that's good.
OK, so.
So localization, we're talking about
localization right now, this is the my
pre mapping slide, which which hints
to the fact that you need specialized
skills code for pre mapping, and
that seems to be a rather difficult
issue.
More importantly, though, localizing.
Which is making the jeeps
signal or which is complementing
the signal with other technologies,
so you really know where you are and not
only in the range of 10 meters basically
is.
This is a lighter, specific
problem, because it's about keeping the
maps
current and
all that and.
Well, anyway, the pre mapping.
Yeah, so the the issue is that if
the map changes and you can see that up
there really frequently as
it does, that if it changes too much, you
can actually lose your localization.
And and that is needed true
for the car to know where it is and so
on.
And that in the end, this advanced
technology presents a drawback to the
self-driving cars.
And I mean, moving on the
weather thing we can just skip because we
knew that already.
Interestingly, there are developments
that can see in the dark, but how
fast they can ride and whether you
want to sit in them in all situations is
a completely different question.
And now we come to, as opposed
to the problems with recognizing
objects and classifying and classifying
them properly, which is perception.
We talk about prediction.
And again, even though impressive,
it's a it's an open problem.
If you can predict the future accurately,
then planning and how to react to those
situations is easy to solve.
That sounds like an eight equals a
sentence, but
being able to predict the future actions
of recognized objects in autonomous
car computing is an open problem, and
that is Dr Eustace
from Toyota, who is
the SVP for automated driving and
AM.
The issue about
the issue about this is that
you have very specific problems that the
car needs to solve.
Just a second.
And that is the semantic
recognition of something not only the
understanding of the surrounding, not
only the the perception
of the surroundings.
So if you accept these surroundings, you
can see people on the side
of the road. But if you understand the
surroundings, you understand the
difference between teenagers who might be
erratic and run onto the street to use
the obligatory example,
or an older lady and a younger child
who very
conscientiously wait for the lights to
turn. And that's the difference that
humans are much better in
recognizing than cars.
And if we look at the ethical problems.
So we have perception.
Once you have precept it correctly, if
you want to distinguish it like that, you
have to predict correctly what those
objects are going to do, which is a whole
'nother question. And then once you've
done that, you still got some ethical
problems, which are usually
explained by the trolley problem now
due to a couple of reasons which will
become clear with a next slide.
I've put this in only as a joke, because
that probably trolley problem, as it
turns out, doesn't give us too much
information for the development of
autonomous cars, neither on the
programing nor on the on the ethical
side. Although it focuses our attention
and thusly, it's not to be missed
as a point or a topic
focuses our attention on
country and questions of responsibility
and autonomy or utilitarian
questions. For example, like in mills,
you're the term determinism
which need to be thought through if we
want to be able to structure society
properly.
We can't just leave ethics out, and that
seems obvious.
But I'm just going to make that point
once more.
And and here you can see driver
versus pedestrian and cyclist is another
version of this.
And this just roughly says what I
just told you. There has been a lot of
hype around the trolley problem.
But but in the end,
the information that we get out of it is
rather restricted as opposed to
situations that could actually happen to
you as a driver.
Which brings us to fatal crashes due
to perception failure.
And I'm just going to go
quickly because we've made that point a
couple of times now.
There is the
uh,
I think, to
well, basically throw on
way over the perception issue.
Oh, listen, and I'll come to this bit.
And I think there is one more slide about
security, but I'm happy to give over
right now
as we're talking here.
Next slide is around.
And this is really interesting.
We talked about the fatal crash with Uber
in 2018, and what was interesting here
was when it was investigated by American
authorities.
They found what they called a cascade
of design failures all the way through
the process.
The car itself had six seconds
to determine what was in front of it,
what objects. It was alternating between
different things. Every time it
alternated between thinking it was a bike
to an object, a person, it lost
the memory of the movement of the person,
so it couldn't actually adjust
its position, according to the situation
it found itself in.
And then when it got close enough, there
was an action suppression system that
kicked in to prevent sudden movements,
which prevented it from handing over to
the driver.
And what's interesting about this is
the level of failure to the
the authorities found there with
Uber and also
the safety failures the regime which
oversaw the safety drivers.
They didn't drug test, they didn't.
There was no oversight.
And yet when it came down to it
and they in the end
ended up charging the driver and not
actually sanctioning the Uber in any way
and coming back to this point, arrest was
making about the safety issues.
Who's actually going to be responsible
for a fatal crash, however safe
these cars are, they are always
going to create some kinds of fatalities
is inevitable with the scale of
automotive transport.
But who's going to actually be
accountable for it?
And this is not
a good precedent.
So we talked about cybersecurity.
Should I just quickly case
I can do that one just very quickly,
just because this is a very specific one.
And if you see
the headline, it says
a study on a can you can you put
in the headline interview?
Yes. Thank you.
Automotive industry cybersecurity
practices from measured
or assessed in an independent
study by the Commission by the FAA
International. It's enough there's now
synopsis sells software
for autonomous driving so we can see
where that is coming from, but those guys
are trying to get it into
boxes that we can work with.
So
those are the key results
from this study.
And the study is not only on
a connected or autonomous
cars, but on new cybersecurity
and auto mobile in the models and global
industry. And this is just
this. This point is just a bigger
explanation or a longer explanation of
this one. And the three key points
are software security is not keeping pace
with technology in the auto industry.
Software and the auto supply automotive
supply chain presents a major
risk, and that's an issue that will lead
us back to proprietary versus open
software issues, among
other things, because the software comes
from third party suppliers and
sometimes you know, the
OEMs have to superimpose
things to them to make it more secure.
And these guys, we can go into this under
discussion and and connected
vehicles have a unique security issues.
I mean, we could all have guessed that
one. But that's just what
I wanted you to throw out there because
I found they did.
They do have some interesting
questionnaires with people from the
industry and
and from science as well.
Okay. And then when we're talking about
these cybersecurity issues, we can
also we need to talk about the data and
privacy issues.
Tesla, currently on the road today, is
equipped with hardware for
autonomous driving, and that means
it has eight eight 360
degree high definition cameras
all recording. It constantly has 12
ultrasonic sensors.
It has
GPS. It has an inertial gauge,
even actually monitors
the pedals and the steering.
All of that information is being shared
with its sensors with Tesla,
and it can be recording even when
the car is stationary and actually office
recording all the time.
So it's not just recording the people in
the car, it's recording all of its
surrounding area and all of the people
there as well.
Research suggests that there's something
like potentially a fully
autonomous vehicle could actually be
sharing 40 terabytes of data every
eight hours.
And then we have. Cyber security issue.
What if we have malware, for example, in
a car? It's one thing if you have it in a
computer at home, when that computer
is in two tons of metal, going at 120
kilometers an hour, that's a bit of a
problem. And it's not just malware for
the car.
A lot of researchers are concerned
about passive hacking that is
doing things contaminating
the environment with different
information. For example,
with road signs which really screw up
what the perception of a
self-driving car system would be.
This all could be things with fatal
consequences.
So there are massive issues that
I would like to add that we are
aware of the fact that recording and
monitoring is not the same thing and
monitoring something and recording
it doesn't have to go
together. It usually does, though,
because for a couple of reasons,
mostly because if you wouldn't need the
data afterwards, why would you monitor
reinforced first place?
And those are things to think about.
Sure.
So let's see.
Yeah. So having seen all
of the complexities and difficulties and
challenges we may want to revisit,
why exactly do we need self-driving
cars anyway?
And one of the obvious answers
we have to that,
which is the frequently said it's going
to someday be an immense boost to
our economy.
It's going to have. One report recently
said it's going to add seven trillion
dollars to the world economy.
That's twice the size of the economy
of Germany.
And how is it going to do that?
Well, when you look into the report, you
can see where they're going and second,
saying that it's going to be three point
seven trillion dollars spent
on mobility as a service.
In other words, taxes, that's
an awful lot of money.
That's three that's more money than
the entire automotive industry generates
currently, which is about three trillion
dollars.
So that's a lot of money we're spending
on taxes.
Is that going to make us richer is as an
economy, as a society.
It's hard to see that, really.
And likewise, they looking at freight and
transport and three trillion dollars
spent on autonomous vehicles, well,
that could be more efficient.
But of course, we have a huge number
of of of
a huge workforce employed in
the train frame transport industry.
There was something like five million
drivers professionally
in Europe alone.
What happens to them?
Is this really a great idea for our
economy? And fundamentally,
is it actually going to make us that much
richer if when we're in a car instead
of driving it, we're looking at our
emails? It's hard to see that
safety is the other big
argument that's often made
his. This is taken from Waymo's website.
It says one point thirty five million
deaths every year, 94
percent. This is a statistic you often
hear when you look at things around
self-driving cars. 94 percent of
accidents are caused by human
error, the implication being that
somehow autonomous vehicles will
actually address all of those.
But a lot of research is a question that
and said, well, actually only a third
of those accidents are going to be
avoided by autonomous vehicles, even
when there are humans involved.
There's nothing an autonomous vehicle can
do about a pedestrian in the street,
for example, to a to avoid the crash.
So the idea of safety is a big,
big question. It's an assumption, but
there's no real data to support
that. What we know is that autonomous
vehicles can be reasonably safe
in controlled environments,
but that's not the same as a normal city.
And then we're given this kind of vision.
This is Berlin.
This is a lovely Berlin a few years in
the future, as created by Daimler,
and this is from a report from Synopsis,
another company in this whole
self-driving car industry about how
it's going to reduce congestion.
This is a very popular argument.
Also, how is going to cut transportation
costs by 40 percent?
Hard to see how it's going to do that
with the cost going into the research
and how it's going to improve our
walkability and livability.
This congestion issue keeps coming up.
And the idea, presumably is that if
you have a whole fleet
of autonomous vehicles, they can just
drive bumper to bumper at 70 kilometers
an hour and be hugely efficient.
But it doesn't really work like that for
once.
For a start, you've got autonomous
vehicles, almost certainly for the next
few decades, even if they are exist
working with normal traffic.
How is that going to be more efficient?
And evidence suggests to actually
autonomous vehicles could increase
traffic congestion as people start
using them for completely frivolous
journeys if they're not actually in them?
And so the issue around congestion
is is very questionable indeed.
And indeed, traffic planners also point
to public transport, and
highways today can carry a
maximum of 2000 people
cars per hour.
If you're very optimistic about
autonomous vehicles, you could possibly
quadruple that, but that's really
stretching it, but a
good public transport system
will transport 50000 people
per hour, and there's no way
as this this urban planner says
no technology can overcome that basic
geometry.
Maybe, maybe just as a side note,
if automation could eliminate all
involved driver related factors,
then that would that would help a lot.
It's a big if and
and there is actually numbers out there
that show that even with the increasing
automation level, you don't get that much
out of it. It's like there are numbers.
I'll have to check it out.
But the point is that those
assistant functions work well, either
on higher speed roadways that are so
framed that it works anyway.
And that's not where you get usually the
crashes.
That's not where you usually get the
crashes, or it's
it's even if
that those ones were excluded.
It would still just mean like 15,
only 17 percent fewer deaths at nine
fewer injuries, nine percent fewer
injuries or something like that.
So I mean, we have to look at the numbers
properly, but this is this is an
interesting thing to note.
So one of the other challenges
here, as we said, is it's so difficult
for cars to perceive their environment
and inevitably because of the industry
and the scale of it. You're seeing the
alternative and this is from Andrew Ng,
who's one of the most prominent A.I.
researchers in the world today.
He's he wrote an article saying
self-driving cars won't work until we
change our roads and attitudes.
It's up to us to adapt to them.
And this is going to be increasingly
loud in the years to come.
As one expert transport expert
put it, the open spaces that cities
like to encourage would end
as the barricades go up.
That movement would need to be enforced
with seamless, poor style
authoritarianism.
Maybe that's why we're seeing also
a huge amount of of hostile
action and human beings being really
quite cruel to robots in
self-driving cars.
This is from Arizona.
There have been other cases as well,
but there are other issues we need to
think about and that is if you want
to look at these scenarios,
something like this VIDEO Coming up
is one of the places where you need to
worry about self-driving cars going
a.
Thank think.
They got to
this point.
He
will be.
So as you might have expected,
that is from the recent
fires in California, something
you would think might be present in the
minds of a lot of people in the
self-driving car industry, as they're all
based around Silicon Valley and probably
encountered problems with fires over the
last few years.
The real issue here, then, when we're
talking about driving is not who
drives a car, but the fact that we have
any cars at all.
And so.
And the fact that we have 1.4
billion cars currently on the planet.
Anything we do with cars is
going to be unsustainable no matter
how we change the
technology that drives them.
And of course, what we assume are
what the self-driving car assumes
is somehow or other.
It's going to be fine because they're all
going to be electric.
Well, this is a lithium plant in Bolivia,
admittedly, and it's actually quite
pricey from here.
But you've got to remember that each of
these pools of evaporation
has toxic waste in them, and
lithium extraction is like any other
extractive industry.
It is appallingly destructive
to our environment and the places
where it happens. It has a huge cost.
When you look at lithium,
there is a vast amount that we require
currently there.
If there are 1.4 billion cars in
the world and we changed them
to lithium, that's 12 kilograms of
lithium per car.
That's the normal amount at the moment
for, say, a Tesla.
That's sixteen point eight million tons
of lithium.
And yet we have 80 million
tons of so-called resources
known quantities, but only 17
million tons of reserves those that we
can actually extract.
In other words, all the lithium we know
we can extract, we would have to use for
self-driving cars.
That means there's nothing left for
mobile phones. They'll have to go
clockwork, and that's 10
times the production of lithium
that we that we actually produce
today.
And of course, lithium is not the only
element we need to look at cobalt, as
well as a kilo of cobalt in a lithium
battery.
And that comes primarily from the direct
from Congo, which is at the center
of some of the world's world's worst
child slavery situations.
So again, you've got this real
problem of locking us further and further
into an extractive industry, which is
fundamentally unsustainable.
And even when you look at the carbon,
it's not so clear that a lithium
powered electric vehicle is somehow
going to be more sustainable than a
carbon atom in a normal combustion
engine.
Researchers in Germany have found, for
example, that in actual fact,
over the life cycle of a car that is for
the manufacture as well
as the consumption of energy, while it's
works in service to the
actual disposal of the car, the carbon
impact of a of an electric vehicle
is probably just as much in Germany
because of the dependance here on fossil
fuels on coal power.
Other research has been more optimistic
about that, and it's found that, for
example, if you do the whole hype, if you
look at the whole life cycle,
a standard average petrol
car is going to produce something
like twenty two hundred and fifty grams
per kilometer, whereas a
a Nissan Leaf, one of the lightest
forms of electric car available today, is
one hundred and forty two grams
per kilometer. So a lot less,
admittedly, but it's still significant.
And of course, this is one of the
lightest cars, but
electric vehicles are having another
issue.
And so autonomous vehicles are
having another issue, and that
is on impacts on public policy today.
And here we have a situation in Camino,
in California and in
Camino in California.
They try to introduce bus lanes,
and those bus lanes
were overrun by people saying that
they're going to be antiquated and
they're going to. We need to wait for
self-driving cars.
That was in 2014.
They're still waiting for them.
They still don't have any bus lanes.
The same thing happened in Detroit
and and in Detroit, they
had a referendum
which was overruled.
And here you have and this is at the
heart of it, really.
This is from one of the leading venture
capital companies.
Don't build a light railway system.
Please, please, please don't, says
this person from Andreessen Horowitz.
We don't understand the economics of
self-driving cars because we haven't
experienced them yet.
Let's see how it plays out.
And here you can see even send a
picture here.
Talking about this is in the last few
weeks how Google
is helping with climate change,
how it's using A.I.
to address carbon impact.
Nothing here about the fact that plowing
tens of billions of dollars into a
technology which is going to take
us towards climate change.
So the reality is we got.
One option for safe
cities, and that is to take cars out
to them altogether.
And what we need to consider is
why we are going in the self-driving car
route in the first instance.
Why are we not?
It's like other technologies,
which the tech company
to tend to
tend to push on us, like going to Mars,
like cryogenics.
These are things that belong in a
teenager's bedroom.
And so with 100 billion dollars,
we could do a hell of a lot more.
We could build cycle superhighways.
We could spend 10 years of free
public transport.
So this is the real future of
the car. Autonomous or not autonomous?
This is the way cars can contribute
to a sustainable future.
Thank you.
Given rights, do you have time
for a few questions because there
are a couple of them?
So the first one is is it more a
liability issue or a technical
issue that no alta nos vehicles
on the street yet
that no autonomous vehicle on the street?
Well, first of all, we have
we have we don't. Well, it's
technologically, if I may.
Yeah. But I mean, not not
yet possible due to the
fact that it's always restricted to the
geo fenced areas where the maps are
pretty mapped prebuilt
or where the weather can't
harm you. So truly autonomous, truly
autonomous. Or let's call it,
self-driving cars are not out
there for the technological reason.
But we can be very happy about that
because the liability, even if they're
trying to grasp at it now, is
not at all done and it doesn't to doesn't
look like they're going to develop it in
a way that we humans can just lean back.
We rest of our humans.
So that not necessarily depend where the
millions go right away
and the liability problems are so
intractable, it's hard to see where the
solutions really lie to under our current
regulation systems.
So both, thank you.
Next question is, are regulators
or insurers worried about the danger
inherent in human passengers who
aren't paying attention only needing
to take control in extreme conditions
seems far more dangerous than
it requiring constant attention?
Nice one
for you. Yeah, sure.
I mean, I mean, this is absolutely
correct. I mean, when you look, this is a
real problem with level three.
We went through to different levels.
Level three autonomy, which
seems technically most achievable.
That's what Tesla is aiming for.
The real problem there is that
it's very hard to get
drivers to pay attention if
they're not actually driving.
And research has shown time and time
again as a result,
the reaction times when something does go
wrong and that much slower.
And this is a massive, massive problem,
and I think it was highlighted in the
scale that you showed.
But this is a big reason why,
for example, Waymo, other
self-driving cars are going straight to
level five.
They actually find level five
to be an easier technical challenge than
trying to address this human interaction
problem we did before.
They couldn't, it couldn't erase the
driver. And this question has another
beautiful connotation.
It goes into the direction of what they
are. What are they interested in?
Are they interested in saving the call
the human or roughly something like that?
If I heard that correctly, and it's
that's a nice one, because that's exactly
what what this is so interesting about in
this turnaround that they made.
First of all, they're trying to skip
three and then they're trying to make
the drivers see through so they can
make three and then
four and five.
And it's so weird because, as I said,
it's never the everyday or I hope I've
said, it's never the everyday situations
that are a problem with the autonomous
driving. It's always the interesting
out of order situations that then
the system is supposed to learn from the
driver in these situations where the
system wouldn't have ever decided like
that, but the driver did.
And that is just
a hundred and sixty degrees around
and then another 180 more
to go into the into the other direction
that you've been been going in for.
First, you want to erase the error
source human and then you need
the human not to do the worst
errors ever, which is just
a complete turnaround.
OK. There are quite a lot
of questions here, and I think you're
right. I mean, we would love that.
I'm afraid we won't have the time
to ask them, or I'll take
one. But before
there's already the question, where can
we discuss this further?
Well, tonight I would recommend meeting
the pets because it's
I think Coco
just and this is why I'm so glad that you
mentioned it beforehand is basically
a spending possibility
to do colloquium and conferences
on all the kinds of topics that you're
interested in. And I'm going to do that
talk in our different version
again at Coco, and I would be delighted
if you guys came and and brought
in your expertize, and I'm sure Alison is
going to be there.
And apart from that
email, and
yeah, I mean, meet us follow up
on chat afterwards.
Yeah, yeah.
OK, then. Thanks.
Thank you for me. And also, there are
a lot of things and that's the best
talk we heard in the chatter here in the
Petzel and a lot of questions.
So I give you the link afterwards and
you can chat it out with them
over there. So thank you for the talk.
Thank you.
Thank you so much.
You.