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Language: en
so today we have Chris Gertie's with us
he's a professor at Stanford University
where he studies how to build autonomous
cars that perform at or beyond human
levels both on the racetrack and on
public roads so that includes a race car
that goes 120 miles an hour autonomously
on the racetrack this is awesome he
spent most of 2016 as the chief
innovation officer at the United States
Department of Transportation and was
part of the team that developed a
federal automated vehicle policy so he
deeply cares about the role that
artificial intelligence plays in our
society both from the technology side
and the policy perspective so he is now
I guess you could say a policy wonk
world renowned engineer and I think Oh
was a car guy yes
so he told me that he did a Q&A session
with a group of three graders through
great third graders last week and he
answered all of their heart hitting
questions so I encourage you guys to
continue on that thread and ask Chris
questions after his talk so please give
a warm welcome to Chris
great Lex thanks for that great
introduction and thanks for having me
here to talk to everybody today so this
is this is sort of my first week back in
a civilian role I wrapped up at USDOT
last week so I'm gonna no longer
speaking and officially representing the
department although some of the slides
are very similar to things that I used
to speak and represent the department so
I think as of Friday this was still
fairly current but I am sort of talking
in my own capacity here so I wanted to
talk about both the technology side and
the policy side of automated vehicles
and in particular how some of the
techniques that you're learning in this
class around deep learning and neural
networks really place some challenges on
regulators and policymakers attempting
to ensure vehicle safety so just a bit
about some of the the cars in my
background I am a car guy and I've
gotten a chance to work on a lot of cool
ones I actually have been working in
automated vehicles since 1992 in the
Lincoln Town Cars in the upper corner
are part of an automated highway project
I worked on as a PhD student at Berkeley
I then went to freight lidar heavy
trucks in daimler-benz and worked with
suspensions on heavy trucks before
coming to Stanford and doing things like
building p1 in the upper right corner
there that's an entirely student built
electric steer by wire drive by wire
vehicle
we've also instrumented vintage racecars
electrified a DeLorean which I'll show a
little bit later and worked as Lex
mentioned with Shelley which is our
self-driving Audi TT which is an
automated race car in addition to the
Stanford work I was a co-founder of
peloton technology which is a truck
platooning firm looking at bringing
platooning technology so vehicle to
vehicle communication which allows for
shorter following distance out on the
highway so these are some of the things
i've had a chance to work with to give
you a little bit of a sense this is
shelley going around the racetrack at
Thunderhill she can actually go up to
about 120 miles an hour or so on that
track it's really just limited by the
length of the straight it's kind of fun
to watch from the outside a little
disconcerting occasionally as you see
there's nobody in the car although from
inside it actually looks all
pretty chill so Shelly we've been
working with her for a while out on the
track
she's able to get performance now which
exceeds the capability of anybody on the
development team
I'll even many of us are amateur racers
in fact actually most of my PhD students
have their novice racing license we make
sure that they get that license before
going out on the track and testing so
Shelly could be in anybody in the
research group she actually can beat the
president of the track david Vaadin now
and we've had the opportunity to work
recently with Junior Hildebrandt the
IndyCar driver who finished six this
last year in the Indy 500 he's faster
but but he's actually only about a
second or so faster on a minute and 25
second lap so we're approaching his
performance and he's actually helping us
get there now the interesting thing
about this is that we've approached this
problem really from one of physics force
equals mass times acceleration so the
car is really out there calculating what
it needs to do to break down into the
next corner
how much grip that it thinks it has and
so forth as it's going around the track
it's not actually a learning approach at
its core although we've added on top a
number of algorithms for learning
because it turns out that the difference
between the cars performance and the
human performance really getting that
last little bit of capability out of the
tires
humans drive instinctively in a way the
best of humans at any rate drive
instinctively in a way which is
constantly pushing to the limits of the
cars capability and so if you sort of
prejudge what those limits are you're
not going to be quite as fast and so
that's one of the things we've actually
been working with learning algorithms on
is to try to figure out well how much
friction do I have in this particular
corner and how is that changing as the
tires warm up and as a track warms up
from the course of the morning till the
afternoon these are the things that we
need to be fast on the racetrack but
they're also the things that you need to
take into account to be safe in the real
world because what we're trying to do
with this project is understand how the
car can drive at the maximum capability
of the limits of the friction between
the tire and the road now racecar
drivers do that to be fast as they say
in racing if you want to finish first
you have to finish so it's important
that they actually be fast but also
accident free so we're trying to learn
the same things so that on the road when
you may have unknown conditions ahead of
you the car can make the safest maneuver
that's using all the friction in between
the tire in the road to avoid ultimately
any accident that the car would be
physically capable of avoiding that's
our goal with that so we've had a lot of
fun with Shelley we've gotten to drive
the car up Pikes Peak in the Bonneville
Salt Flats actually Shelley appeared in
an Audi commercial with Zach Quinto and
Leonard Nimoy and so at the end of the
commercial they both look at each other
and declare it fascinating so if you're
as big of a science fiction fan as I am
you realize that once your work has been
declared fascinating by two Spock's
there's nowhere to go so I had to take a
stint and try something different in
government and so I spent the last year
as the first chief innovation officer at
the US Department of Transportation
which I think honestly was the coolest
gig in the federal government because I
really didn't have any assigned
day-to-day responsibilities but I got to
kind of dive in and help with all manner
of really cool projects including the
development of the first federal
automated vehicle policy so it's a
really great opportunity to sort of see
things from a different perspective and
so what I wanted to do was you know kind
of coming into this from an engineer
give you a perspective of what is it
like from somebody looking at the
regulatory side on vehicle safety and
how are they thinking about the
technologies you're developing and where
does that actually leave some
opportunities for engineers to make some
big contributions to society so let's
start with with what vehicle safety is
like today so today we have a system of
federal motor vehicle safety standards
so these are rules they're minimum
performance requirements and each of
them must have associated with it an
objective test so you can tell does the
vehicle meet this requirement or does it
not meet this requirement now
interestingly there is no federal agency
that is testing vehicles before they are
sold
we rely in this country on a system of
manufacturers self certification so the
government puts these rules out there
and manufacturers go we got this we can
meet this and then they sell
certify and put the vehicles out on the
market the National Highway Traffic
Safety Administration can then purchase
vehicles and test them and make sure
that they comply but we rely on
manufacturers self-certification this is
a different system than in most of the
rest of the world which actually has pre
market certification where before you
can sell it the government agency has to
say yes we've checked it and it meets
all the requirements Aviation in this
country for instance has that aircraft
require certification before they can be
sold cars do not now where did that
system come from so a little quick
history lesson in 1965 Ralph Nader
released a book entitled unsafe at any
speed and this is often thought of as a
book about the Corvair it's it's not the
Corvair featured prominently in there as
an example of a design that Nader
considered to be unsafe what was very
interesting about this this book was
that he was actually advocating for
things like airbags and anti-lock brakes
back in 1965 these technologies didn't
come along until much later his argument
was that the auto industry had failed it
wasn't a failure of engineering but it
was a failure of imagination and if
you're interested in vehicle safety I
would really recommend you read this
book because it's fascinating they have
quotes from people in the 1960s
basically saying that we believe that
any collision more than about forty or
forty-five miles an hour is not
survivable therefore there's no reason
for seatbelts there's no reason for
collapsible steering wheels in fact
there's a quote from somebody who made
great advances in Road Safety saying I
can't conceive of what help a seatbelt
would give you beyond like firmly
bracing yourself with your hands those
of you who have studied physics know
that's kind of patently ridiculous but
there was a common feeling that there
was no sense of doing anything about
vehicle crash worthiness because once
you got above a certain speed it was
inherently unsurvivable and I think it's
interesting to look at that today
because if we were to be in a collision
I think if any of us were to be in a
collision in around about 40 miles an
hour in a in a modern automobile we'd
probably expect to walk away you know we
wouldn't really be thinking about our
survival and so what this did is it led
to
a lot of public outcry and ultimately
the National traffic and Motor Vehicle
Safety Act in 1966 which established
nitzan established this set of federal
motor vehicle safety standards
now the process to get a new standard
made which is a rulemaking process in
government is very time-consuming
optimistically about the minimum time it
can possibly take is two years
realistically it's more like seven and
so if you think about going through this
process that's really problematic I mean
think about what we were talking about
with automated vehicles two years ago or
seven years ago I think about trying to
start seven years ago and make laws
they're gonna determine how those
vehicles operate on the road today it's
crazy right there's really no way to do
that and the other thing is is that if
you think about it our system evolved
from really this sense of failure of
imagination that the government needs to
say hey industry do this stop slacking
off
these are the requirements get there but
I think it's hard to argue today with
all the advances in automation that
there is any failure of imagination on
the part of industry people are coming
up with all sorts of ideas and concepts
for new transportation and automation
tech companies startup companies large
OEMs there's all sorts of concepts being
tested out on the road it's hard to
argue that there's still any lack of
imagination now the question is are
things like this legal it's an
interesting question right can I
actually legally do this well from the
federal level there's an interesting
report that came out about ten months
ago from the folks across the street at
Volpe who did scan and said well what
are the things that might prevent you
based on the current federal motor
vehicle safety standards from putting an
automated vehicle out on the road and
the answer was honestly not much if you
have a vehicle if you start and you
automate a vehicle that is currently
meeting all the standards because there
are no standards that relate
specifically to automation you can
certify your vehicle as meeting the
federal motor vehicle safety standards
therefore there's nothing at the federal
level that prevents in general an
automated vehicle from being put on the
road so it makes sense so if there isn't
a safety standard
that you have to meet then you can put a
vehicle out on the road that meets all
the existing ones and does something new
and there's no federal barrier to that
now there are a couple of exceptions
there were a few points in there that
referenced a driver and in fact Nitsa
gave a an interpretation of the rule
which is one of the things that they can
do is to say well we're going to give an
interpretation it's not making a new
rule but basically interpreting the ones
that we have and they said that actually
these references to the driver could in
fact refer to the AI system and so that
actually is now a policy statement from
from the department that many of the
references to driver in the federal
motor vehicle safety standards can be
replaced with your self-driving aai
system and the rules applied accordingly
so in fact there's very little that
prevents you from putting a vehicle out
on the road if it meets the current
standards so if it's a modern production
car automated federal motor vehicle
safety standards don't stop that now a
lot of the designs that I showed though
things that wouldn't have a steering
wheel or other things are actually not
compliant because there are requirements
that you have a steering wheel that you
have pedals again these are best
practices that evolved in the days of
course when people were not thinking of
cars that could drive themselves and so
these things would require an exemption
by Nitsa a process of saying that okay
this vehicle is allowed on the road even
though it doesn't meet the current
standards because it meets some
equivalent and studying that equivalent
can be a bit of a challenge okay so the
question then is well alright if the
federal government is responsible and
that's by the traffic safety act is
responsible for safety on the roads but
it can't prevent people from putting
anything out what do you do right one
approach is to say well let's get some
federal motor vehicle safety standards
out there but as we already said that's
probably about a seven year process and
if you were to start setting in best
practices now what would that look like
so we've got this challenge we want to
encourage this technology to come out
onto the roads and be tested because
that's the way you're gonna learn to get
the real-world data to get the
real-world experience at the same time
the federal government is responsible
for
safety on the nation's roads it can
recall things that don't work so if you
do put your automated system out on the
highway and it's deemed to present an
unreasonable risk to safety
even if you're an aftermarket
manufacturer the government can tell you
to take that off the road but the
question is how can you do better how
can you be proactive to try to have a
discussion here so we know standards are
maybe not the best way of doing that
because they're too slow we'd like to
make sure the public is protected but
this technology gets tested and so the
approach taken to sort of provide some
encouragement for this innovation while
at the same time looking at safety was
the federal automated vehicle policy
which rolled out in September so this
was an attempt to really say okay let's
put out a different framework from the
federal motor vehicle safety standards
let's actually put out a system of
voluntary guidance so what Anisa is
doing is to ask manufacturers to
voluntarily follow certain guidance and
submit to the agency a letter that they
have followed a certain safety
assessment now the interesting thing is
is that the way that this is set up is
not to tell manufacturers how to do
something but really to say these are
the things that we want you to address
and we want you to come to us to explain
how you've addressed them with the idea
that from this best practices will
emerge we'll be able to figure out in
the future what really is the best way
of ensuring some of these safety items
so this rolled out in September we've
got the BMI t car here on the side so
you see you've got the Massachusetts
license plate so thanks to Brian for for
bringing that if you do put gaudy
stickers on your card then you get
closer to the center so that's something
to consider for for for future future
reference but this was was rolled out in
Washington Washington DC by the
secretary and consists largely of of
multiple parts but I think the most
relevant to vehicle design is this 15
point safety assessment so these are the
15 points that that are assessed and I'd
like to kind of talk about a few of
these in some more detail and it starts
with this concept of an
operational design domain and minimal
risk or fallback conditions and what
that means is instead of trying to put a
taxonomy on here and say well your
automation system could be an adaptive
cruise control that works on the highway
or it could be fully self-driving or it
might be something that operates a
low-speed shuttle the guidance asked the
manufacturers to define this and the
definition is known as operational
design domain so in other words you tell
us where your system is supposed to work
is it supposed to work on the highway is
it supposed to work in restricted areas
can it work in all-weather or is this
sort of something that operates only in
daylight hours in the sunshine in this
area of South Florida all of those are
fine but the it's incumbent upon the
manufacturer developer to define the
operational design domain and then once
you've defined where the system operates
you need to define how you make sure
that it is only operating in those
conditions how do you make sure the
system stays there and what's your
fallback in case it doesn't
and that fallback can be different
obviously if this is a car which is
normally human driven as you see here
from the volvo drive me experiment it
might be reasonable to say we're gonna
ask the human driver to retake control
whereas clearly if you're going to
enable blind passengers or you are going
to have a vehicle that has no steering
wheel you need a different fallback
system and so within the the guidance it
really allows manufacturers to have a
lot of different concepts of what they
want their automation to be so long as
they can define where it works what the
fallback is in the event that it doesn't
work and how you have educated the
consumer about what your technology does
and what it doesn't do so that people
have a good understanding of the system
performance a few things if we go down
you see also validation methods and
ethical considerations are our aspects
that are brought up here as well and so
validation methods are really
interesting as it applies to AI so
really the idea is that there's lots of
different ways that you might tell
an automated vehicle you might go out on
the test track and run it through a
series of standard maneuvers you may
develop a certain number of miles of
experience driving in real-world traffic
and figure out how does the vehicle
behave in a limited environment there's
questions about a test track obviously
because you don't have the sort of
unknowns that can happen in the
real-world environment but if you test
in one real-world environment you also
have a question of is this transferable
information so if I've driven a certain
number of miles in Mountain View
California does that tell me anything
about how the vehicle is likely to
behave in Cambridge Massachusetts maybe
maybe not
it's a little bit hard to extrapolate
sometimes and then finally there's also
the idea of simulation and analysis so
if I can record these situations if I
can actually create a virtual
environment of the sorts of things that
I see on the road maybe I can actually
run the vehicle through many many of
these scenarios perturbed in some way
and actually test the system much more
robustly in simulation than I can ever
actually do out on the road so the
guidance is actually neutral on which of
these techniques manufacturers take and
allow manufacturers to approach it in
different ways and I think you know
based upon conversations when you think
about the way customers are companies
develop this they do take all these
different approaches a company like
Tesla for instance which is recording
all the data streams from all their
vehicles basically is able to run ideas
or technologies silently in their
vehicle they can actually test systems
out get real-world data and then decide
whether or not to make that system
active companies that don't have that
access to data really can't use that
sort of development method and may rely
much more heavily on simulation or test
track experience so the guidance really
doesn't have this particular blend of
this and in fact it does envision that
you might have over-the-air software
updates in the in the future so it is
interesting though to think about
whether you have data driven approaches
things like artificial neural networks
or whether you actually start to program
in hard and fast rules because as you
start to think about requirements on a
system how do you actually set require
on a system which has learned its
behavior and you don't necessarily know
what the internal workings or our
algorithms look like there's another one
that that comes up which is the ethical
consideration so I'm gonna pick on MIT
for a moment here so this is an area
that I actually did a lot of work on
with Stanford together with with some
philosophers who join joined our group
and so when people hear ethical
considerations in automated vehicles it
often conjures up the trolley car
problem and and so this sort of classic
formulation here about the fact that you
have a self-driving car which is heading
towards a group of 10 people and it can
either plow in and kill those 10 people
or it can divert and kill the driver
what do you do and these are classic
questions in philosophy you actually
look in fact at at the trolley car
problem which is I have a runaway
trolley car and I need to either divert
it to another track where it will kill
somebody who's wandering across that
track or the five people on the trolley
car are killed what do I do well in fact
it's this article points out it's like
you know before they the automated
vehicles can become widespread car
makers must solve an impossible ethical
dilemma of algorithmic morality so if
all this wasn't hard enough I mean your
understanding how tough the technology
is to actually program this stuff and
then you have to get the regulations
right and now we actually have to solve
impossible philosophical questions well
I don't think that's actually true and I
think you know it's good for engineers
to work with philosophers but not to be
so literal about this this is a question
that philosophers can ask but engineers
might ask a number of different
questions like who's responsible for the
brakes on this trolley why wasn't there
a backup system I mean why am I headed
into a group of 10 people without any
capability to stop so an engineer would
in fact have to answer this question but
might approach it much differently so if
I look at the trolley car problem I
might say ok let's see my options are
I've got a trolley car which is out of
control first of all I'd like to have an
emergency braking system let's make sure
that I have that well there's a chance
that that could break as well so
my emergency if my base breaking system
goes and my emergency braking system
goes my next option would be to divert
it to this sidetrack well knowing that
that's my option I should probably put
up a fence with a warning sign that says
do not cross runaway trolley track okay
now let's say that I've done all of that
the brakes fail the big emergency brakes
fail I have to divert the trolley and
somebody has ignored my sign and crossed
over the fence and now he's hit by the
trolley do I feel a little differently
about this whole scenario and then I did
at the beginning of just trying to
decide who lived and who died the
solution was made but by thinking of it
as an engineer trying to reduce risk and
not by thinking of levels of morality
and who deserves to live or die and so I
think this is a very important issue and
the reason it's in the guidance is not
to get basically have everybody solve
trolley car problems but to try to think
about these larger issues and so I think
ethics is is not just about these sorts
of situations which actually will be in
automated vehicles I think addressed
much more by engineering principles than
by trying to figure out from
philosophical merits who deserves to
live and die but there's broader issues
here just any time that you have concern
for human safety how close do I get to
pedestrians how close do I get to
bicycles how much care should I put in
to other people in the environment
that's very much an ethical question and
it's an ethical question that
manufacturers are actually already
addressing today if you look at the
automatic emergency braking systems that
most manufacturers are putting on their
vehicles they will actually use a
different algorithm depending upon
whether that obstacle in front of it is
a vehicle or a human so they're already
detecting and making a decision that the
impact of this vehicle with the human
could be far worse than the impact in
this vehicle with a vehicle and so
they're choosing to brake a little bit
more heavily in that case that's
actually where these ethical
considerations come in and the idea of
the guidance is to begin to share and
have a discussion openly about how
manufacturers are approaching this with
the idea of getting to a best practice
where not only the people in
automated vehicles but other road users
feel that there's an appropriate level
of care taken for their well-being
that's one of the areas where ethics is
important the other area where ethics is
important is that we have different
objectives as we drive down the road we
have objectives for safety we'd like to
get there we have objectives for
mobility we'd like you to get there
probably pretty quickly and we also have
the idea of legality we'd like to follow
the rules but sometimes these things
come into conflict with each other
so let's say you're driving down the
road and there's a van that's parked
where it has absolutely no business
parking you've got a double yellow line
is it okay to cross well at least in
California there's no exception to the
double yellow line representing the lane
boundary for a vehicle that's parked
where it has no business being parked so
according to the vehicle code you're
supposed to kind of come to a stop here
I don't think any of us would right in
fact actually when you're in California
and you're riding through the hills and
you come upon a cyclist virtually every
vehicle on the road is deviating across
the double yellow line to give extra
room to the cyclists that's also not
what you're supposed to do by the
vehicle code you're supposed to stay on
your side of the double yellow line but
slow to an appropriate speed to pass
right so there's behaviors where our
desire for mobility or our desire for
safety are outweighing our desire for
legality this becomes a challenge if you
think about how do I program the
self-driving car should it be based on
the way that humans drive or should it
be based on the way that the legal code
tells me to drive of course the legal
code was never actually anticipating a
self-driving car from a human standpoint
that double yellow line is a great
shorthand that says maybe there's
something coming up here where you don't
want to be in this other Lane but if I
actually have a car with the sensing
capability to make that determination
itself this is a double yellow line
actually all that meaningful anymore
these are things that have to be sorted
out speed limits being another one you
know if we're out on the highway it's
usually a little bit flexible do we give
that same flexibility to the automated
vehicle or do we create this wonderful
automated vehicle
roadblocks of vehicles going to the
speed limit when nobody else around them
is do we allow them to accelerate a
little bit to merge into the flow of
traffic do we allow vehicles to speed if
they could avoid an accident is our
desire for safety greater than our
desire for legality these are the sort
of ethical questions then I think are
really important these are things that
need to be talked through because I
believe if we actually have vehicles
that follow the law nobody will want to
drive with them and so we need to think
about either ways of giving flexibility
to the vehicles or to the law in the
sense that vehicles can drive like
humans do so this brings up some really
interesting areas I think with respect
to learning and programming and so the
question is you know should our
automated vehicles drive like humans and
exhibit the same behavior that humans do
or should they drive like robots and
actually execute the way that the law
tells them that they should drive
obviously fixed rules can be one
solution to this behavior learned from
human drivers could be another solution
to this we might have some sort of
balance of different objectives that we
do more analytically in terms of how
much we want to obey the double yellow
line when there are other things
influencing it in the environment now
what's interesting is that is you start
to think about this there's limits to
any of these approaches in the extreme
you know as we found with our
self-driving racecar if you're not
learning from experience
you're not making use of all the data
you're not gonna do as well and there's
no way that you can possibly pre program
an automated vehicle for every scenario
it's going to encounter somehow you have
to think about interpolating somehow you
have to think about learning at the same
time you can say well why don't we just
measure humans well human error is
actually the the cause or a factor the
primary factor in 94 percent of
accidents it's either a lack of judgment
or lack of perception on the part of the
human so if we're simply following
humans we're actually only learning how
well humans can do things and we're
leaving a lot on the table in terms of
the potential of the car and so this is
a really interesting discussion that I
think will continue to be
both in the development side of these
vehicles in the policy side what is the
right balance what do I want to learn
versus what do I want a program how do I
avoid leaving anything on the table here
so because it's the point where you know
I've had a bunch of slides with words
here I want to give people a little bit
of a sense for what you could be leaving
on the table if in fact you don't adapt
this is Marty
marty is a DeLorean that we've been
working with in my lab now DeLoreans are
really fantastic cars unless you want to
accelerate brake or turn it really
didn't do any of those things terribly
well there's no power steering there's
an underpowered engine and and very
small brakes all of these things are
fixable in fact what's nice about the
DeLorean is it separates quite nicely
the whole fiberglass tub comes up you
can take out the engine you can take out
the brakes you can make some
modifications to the frame stiffen the
suspension work with renova motors start
up in Silicon Valley to put in a new
electric drivetrain and put it all back
together and when you do you come up
with a car that's actually pretty darn
fun and when we've programmed to drive
itself this is Adam Savage from
Mythbusters going along for a drive
[Music]
what do you see is Marnie doing
something at a level of precision that
we're pretty sure no human driver can
meet Junior said there's no way he can
do this you see it's going into a
perfect drift doing a perfect doughnut
around this cone and then it launches
itself through the next gate sideways
into the next cone now it's doing this
you see it shoots through the gate
missing those cones and then launches
into a tight circle around the next cone
it's actually doing this as sort of an
algorithm similar to orbital mechanics
if you think about how it's how it's
actually orbiting these different points
as it sets the trajectory now the limit
on this as tires as you can see as it
comes around here the tires disintegrate
into many chunks flying at the camera as
we do this but the the ability of the
car to really continue even as the tires
heat up to execute this pretty pretty
nice trajectory here you see it going
through the gates again and launching
into a stable equilibrium putting pretty
much the tire tracks right over where
they were in the previous run and then
finally ending so this is a sort of
thing that I think is possible as you
look at these vehicles there's a huge
potential out there for these things to
not drive about as well as an average
human but to far exceed human
performance in their abilities to use
all the capabilities of the tires to do
some amazing things so maybe that's not
the way that you want your your daily
drive to go although when we first
posted some of this some of this video
one of the commenters was like I want
this car that way I can like go into the
store to buy donuts while it sits in the
parking lot doing donuts wasn't a use
case that I had thought of but that's
one of one of the things that we thought
of this really how if you limit yourself
to only thinking about what the tires
can do before they get to the saturation
of the friction in the road you're only
taking to account one class of
trajectories there's a lot more beyond
that that could be very advantageous in
some emergency situations would it be
great if the car had access to that now
that's not a way that we're going to get
if we only sort of monitor day to day
driving we're not going to get that
capability in our cars so one other
aspect that came through in the in the
policy which I think is extremely
important as we think about neural
networks and learning is this idea of
data sharing and there's a huge
potential to accelerate the development
of automated vehicles if we can share
some information about edge case
scenarios in particular so if you think
about trying to train a neural network
to handle some extreme situations that's
really much easier if your set of
training data contains those extreme
situations right so if you think about
the weird things that can happen out on
the road if you had a database of those
and those comprised your training set
you'd have a head start in terms of
being able to get a neural net where I
can begin to validate that it would work
in these situations so the question is
you know is there a way for the
ecosystem around self-driving cars to
actually share some of this information
so that different players can actually
share some information about the
critical situations and be able to make
sure that if you learn something that
yes you can make your cars safer but
actually all the cars out on the road
gets safer now clearly you need to
balance this with some other
considerations there's there's the
intellectual property concerns of the
company there's privacy concerns of any
individuals who might be involved but it
does seem to me that there's a big
potential here to think about ways of
sharing certain data that can contribute
to safety and this is a discussion
that's going to be ongoing and I think
academia can do a lot to sort of help
broker this discussion because you know
the first level people say you know data
sharing
I don't know companies aren't going to
share we're not going to get the
information we need but most of the time
people stay in the abstract as opposed
to saying well what information would be
most helpful what information it's
really going to give people confidence
in the safety of these cars it's gonna
let regulators understand how they
operate and at the same time is going to
protect the amount of development effort
that companies put in there I think
there is a solution here and in fact if
you look at aviation there's a really
good example that already exists it's
known as the Esaias system it's started
with only four Airlines
that decided to share safety information
with each other and this goes through
mitre which is a federally funded R&D
center and it's actually now up to 40
Airlines and if companies get kicked out
of the mitre a project they really try
very hard to get back in now this is
anonymized data its anonymized data so
that you know companies actually get a
assessment of what their safety record
is like and they can compare it to other
airlines in the abstract but they can't
compare it to any identifiable airline
so there's no ranking of this it's not
used for any enforcement techniques and
it took people a long time to kind of
build up and begin to share that but now
there's a huge amount of trust and
they're sharing more and more data and
looking at ways that they can perhaps
actually start to code in things like
weather and time of day which had been
removed for anonymization purposes and
the original version of the system so I
think there's some good examples out
there and this is something that's very
important to think about for automated
vehicles and I think as this discussion
goes forward those of you who are
interested in developing these vehicles
using techniques that rely on data are
going to be an important voice for the
importance of data sharing I think
there's a there's a large role here to
kind of make people aware that this
actually does have value in the larger
ecosystem so this is something that I
was able to work on more broadly as well
so I was part now is the d-o-t
representative on the National Science
and Technology Committee's Subcommittee
on machine learning and artificial
intelligence and this was one of the
recommendations that was really pushed
forward as well because AI has tended to
really make great advances with the
availability of good datasets and in
order to make those sort of good
advances in transportation this group is
also advocating that those datasets need
to be made broadly available so this is
a little bit about the vision behind the
the automated vehicle policy what the
goal was to really achieve here the idea
of trying to move towards a proactive
safety culture not to necessarily put in
regulations prematurely and try to set
standards
honestly we don't know the best way to
develop automated vehicles but to allow
the government to kind of get involved
in discussions with manufacturers early
and be comfortable with what's going out
on the roadway and actually to kind of
help the u.s. to continue to play a
leading role in this obviously if
vehicles are going to be banned from the
roads it would be very difficult for the
country to continue to be a place where
people could could test and develop this
technology and then the belief really
that there can be an acceleration of the
safety benefits of this through data
sharing so each car doesn't have to
encounter all the weird situations
itself but in fact can learn from what
other vehicles experience and the idea
is that really this is meant to be an
evolving framework so it comes out as
guidance it really generates
conversations it generates best
practices which can eventually evolved
into standards and law and there's a
huge opportunity here because the belief
isn't that the National Highway Traffic
Safety Administration will be doing all
of the development of these best
practices but that that'll really evolve
from what companies do and what all of
us at universities are able to do to
sort of generate ways to solve these
problems in creative manners ways to
actually keep the innovation going but
ensure that we have safety so as you
start to think about all of the AI
systems that you're developing and you
start to flip around a little bit and
think about how does a regulator gonna
get comfortable that it's not going to
do something weird these are great
research questions I think these are
great practical questions and these are
things that will need to be worked out
going forward so I you with that as a
challenge to think about to think as you
take this course not only about the
technology that you're learning but how
do you communicate that to other people
and where are the gaps that need to be
filled because I think you'll find some
great opportunities for for research
startup companies and ultimately work
with policy and government there so
thanks for the opportunity to talk to
all of you and I want to stop there
because probably the things that you
want to talk about are more interesting
than the things that I wanted to talk
about so I'm happy to take questions
along there
good we had a quick hand here yeah
accidents were part of our economies the
excess rates are extremely low do you
think some of these safety requirements
may roll back like I do I think that's a
great question and okay so the question
thanks for reminding me so the question
was whether in the future when you have
all vehicles automated would we be able
to actually roll back things like
airbags and seatbelts and other things
that we have on there what we might know
is as passive safety devices in vehicles
I believe that we will in fact actually
one of the things that I think is most
extraordinary if you think about this
from a sustainability standpoint when
you look at the average sort of mass of
vehicles and average occupancy of
vehicles in the u.s. you know with
single with passenger cars we're using
maybe about ninety percent of the energy
to move the vehicle as opposed to moving
the people inside and one of the reasons
for that is crashworthiness standards
which are great because that's what's
enabled us to be surviving these crashes
at 40 miles an hour but if we do have
vehicles that are not going to crash or
if they are going to have certain modes
which might be designed with very
carefully design you know crush areas or
things like this we could potentially
take a lot of that mass out particularly
if these are low-speed vehicles which
are designed only for the urban
environment and they're not going to to
crash because they're going to drive you
know somewhat conservatively or in some
ways separated from pedestrians then I
think you can get a lot of the mass out
and then you start to actually have
transportation options which you know
from an environmental standpoint are
comparable to cycling so so I think I
think that's actually a really really
good goal to strive for although we
either have to kind of limit the
environment or think in the far future
with some of those techniques
to apply it which you guys learn
good yeah that's a great question so
what are we what are we doing with
Shelly is our mission really just to
drive as fast as possible and faster
than a human or are we trying to learn
from this something that we can apply to
other automated vehicles it really is a
desire to learn from other automated you
know for the development of other
automated vehicles and we've often said
that at the point where you know the
difference between Shelly's performance
in the human driver you know starts to
be really mundane things like you know
our shift pattern or something which
isn't applicable we kind of lose
interest at that however you know up to
this point every insight that we've
gotten from Shelly has been directly
transferable and we've programmed the
car to do some emergency lane changes in
situations where you don't have enough
room to brake and we've actually been
demonstrating in some cases that the car
can can do this much faster than a human
even an expert humans response can be so
there's certain scenarios that we've
done like that and I would say from the
bigger picture what's really fascinating
is that we originally started out with
this idea of let's find the best path
around the track and track it as close
as we can but in fact when you look at
human race car drivers what they're
doing is actually very different they're
pushing the car to the limits and then
sort of seeing what paths that opens up
to them and it flips the problem a bit
on its head in a way that I think is
actually very applicable for developing
safety systems out on the road but it's
not a way that people have looked at it
to the best of my knowledge up to this
point and so you know that's really what
we're hoping is that the inspiration in
trying to reproduce human performance
there leads us to better safety
algorithms so long you know so far
that's been the case and when that
ceases to be the case I think we are
definitely much less interested
yeah so so liability is a good question
so what what who is liable if I can can
sort of rephrase you know for an
accident in an automated vehicle on the
one hand that's kind of an open question
on the other hand we do have a court
system and so whenever there are new
technologies these things are actually
generally figured out in the courts and
it can be different from state to state
so this is one aspect where you know
potentially some discussions so that
manufacturers aren't subject to
different conditions in different states
would be helpful but the way that it
works now is that it's it's usually not
binary we have in the US a sense of
joint and several liability and so you
can actually assign different portions
of responsibility to different players
in the game you have had companies like
Volvo and in fact Google make statements
that if there are vehicles are involved
in accidents then they would expect to
be liable for it so people have often
talked about needing something really
new for liability but I'm not sure
that's the case we do have a court
system that can ultimately figure out
who is liable with new technologies and
we have some manufacturers that are
starting to make some statements about
assuming product liability for that the
one thing that really could be helpful
as I mentioned is perhaps some
harmonization because right now
insurance is something that is set
state-by-state and so the rules in one
state as to who's at fault for an
accident may be very different in
another state
okay so what what if companies you know
as they send in the safety letters are
are using criteria to set safety that
that may not be broadly acceptable to
the to the public whether the public
would like these vehicles to have
greater safety I think you know the the
nice thing about this process is first
of all we would know that right so we
would have a sense that companies are
developing with certain measures of
safety in mind and there could actually
be a discussion as to you know whether
that is setting an acceptable level it's
it's a difficult question because it's
it's not clear that people really know
what an acceptable level is is it does
it have to be safer than then humans
drive now you know my personal feeling I
would say yes and does it have to be
much much safer well that that's hard to
say you know you start to then get into
the situation of we're comfortable to a
certain extent with our existing legal
system and with the fact that humans
could cause errors that have fatal
consequences do we feel the same way
about machines right you know we tend to
think the machines really should to have
a higher level of perfection so we may
as a society be less tolerant people
will often say well so long as the
overall national figures go down that
would be good but that's really not
going to matter much to the families who
are impacted by an automated vehicle
particularly if it's a if it's a
scenario with very very bad optics and
what do I mean by that it's if you think
about the failures of mechanical systems
because they're different than the
failures of human beings they can often
like look really bad right if you sort
of think about a vehicle that doesn't
detect something and then just continues
to plow ahead you know visually that's
that's really striking and that's the
sort of thing that you know we'd get
replayed and be in people's
consciousness and raise some fears and
so you
I think that's that's an issue that's
going to have to be have to be sorted
out these are average being different
you know between research in other parts
of the world to exchange technologies
yes so that's that's a good question
what's being done really from a global
standpoint to sort of share ideas to
share research and to kind of work
through some of these things
particularly on the policy side so most
of the auto manufacturers are global
corporations and so a lot of the
research in this is done in very
different parts of the world so
renault-nissan for instance is doing a
lot in Silicon Valley in Europe and and
in Japan and I think you see a lot of
that with the different manufacturers
one of the cool things that I got to do
as part of my role was to go with the
Secretary of Transportation to the g7
transportation ministers meeting in
Japan and address the ministers about
sort of the the u.s. policy on on
automated vehicles and one of the parts
of that discussion was well the US has a
very different set of rules so we have
this manufacturer self certification as
opposed to pre market certification but
testing for instance is something that
has to be done regardless so either it's
testing that's done by a manufacturer or
it's testing that's done by for instance
in you know in Germany the the the tooth
and other agencies that are responsible
for for road safety and so the idea is
maybe we should be