Modeling human-AV interactions for safety and acceptance of automated vehicles (Gustav Markkula)

talk to and share my screen it's a great
pleasure to be here and thanks for
calling and just confirm that you see my
powerpoint slides here yeah I guess hey
bro so yes
modeling human abs in Oatman v/o it
returns for safety and acceptance of
automated vehicles and our body's
recommendation I've prepared for about
30 minutes but I've also put in a couple
of places during the talk when they you
might want to ask questions I mean you
can stop me at any time basically but
I've made a couple of breaks Midway as
well or four questions so yes I'll go
for rides with this picture and what
this picture shows for the purposes of
this presentation is basically traffic
with a lot of people right so people in
cars people on foot and various kinds of
vehicles because in relation to
automated vehicles it's not becoming
increasingly clear that one of the main
reasons or maybe the main reason while
why automated vehicles are not sort of
getting deployed on such a big scale so
rapidly as we thought maybe a few years
ago is because there are people in
traffic right and there are people on
drugs and these pesky humans are quite
hard to what's that sorry in the sound
sorry what was that Makati no no that's
fine this one I was checking it's funny
that okay
perfect just stop me if it's Matrixyl
yes yeah so these humans in traffic are
quite hard to understand and predict and
so on on that and that is a big
challenge for for automated vehicles so
one kind of question that we might ask a
very general question is how can we make
a vs or many vehicles that can
successfully coexist
with humans and what else in this talk
is that one important thing I would
argue I'm not a sufficient component but
a necessary component is to develop high
fidelity models of human Road user
behavior I'd argue that we need that as
one of the things to be in place to make
AV second successfully coexist with
humans hopefully you understand what I
mean by the end of the talk and then
more specifically what kinds of models
do we need and there I'll argue that we
need I think a combination of models
that are data-driven machine learned
models and models which are built on
more sort of theoretical clear grounds
or mechanistic models based on cognitive
neuroscience or psychology and so on so
that's something like what I mentioned I
have worked with with a bunch of
different people's file so both athletes
and other places and I'll the one part
of the talk will be covering a bit of
what we've done a bit of in the future
we think so those bits I'll generally
pass quite quickly I'll sort of just
want to give you a flavor and an
overview and then if there's something
you're interested in you can talk to me
afterwards or check the references at
the end of the talk or ask a question
right so yes on the topic of questions I
thought I'd before I sort of go straight
into it I just try to connect a little
bit with what I think you and the AI
tech projects oh yeah for those maybe
calling in from other places and
Delftware the the people who are hosting
this talk are part of a project in
that's called AI tech working on this
concept of meaningful human control of
automation so I thought I'd just put a
couple of questions out there at the
beginning
either it's discussion questions or
maybe as discussed you sort of questions
from me to you a couple of scenarios so
on the one hand if you think of a road
user who is effectively interacting with
an automated vehicle the road user the
human road user couldn't sort of
understand how the automated vehicle
behaves and how it what it does and why
and is therefore able to affect the
behavior of Av with their own behavior
just like we do in traffic all the time
right so as humans we affect the
behavior of other humans with our
behavior right and sometimes quite
intentional ways so it to some extent we
can call that humans controlling other
humans in traffic right in in some sense
of the word control so the scenario I
are with the human that transparently
affects the behavior of the AV is that
meaningful human control of automation
to some degree another question of the
same nature if you think of an engineer
developing automated vehicles who is
studying and justing how an AV will
interact humans by using computer
simulations with powerful high fidelity
models across a wide range of scenarios
and then the engineer sort of tweaks the
behavior of the AV until it seems like
interaction with human as good as it can
be is that meaningful human control
motivation just leaving those with you
for now and then some of launching
straight into the the very pragmatics of
it alright so there the reasons why
people are holding back on AV the flame
deployment to some extent today is that
there are risks associated with it I'm
worried that there are two main risks
one is that we call human frustration so
you might have read news reports on way
more cars I think they've been most
extensively report on reported on being
sort of disliked by people sharing the
roads with them for many reasons but one
important reason seems to be that
they're not quite getting the subtleties
of local interactions in traffic right
they're not behaving quite like humans
would like them to be in traffic they
get stuck turning across traffic that
kind of thing right so that causes
frustration which leads to bad
acceptance of automated vehicles and
obviously another thing which leads to
bad acceptance is human injury that's
that's obvious right so I mean there's
been a number of high-profile cases in
the news where where people have been
harmed by vehicle automation so yeah
crashes costs human injury but not only
crashes sorry or important here but also
near crashes right because crashes can
also cause human frustration especially
if there aren't sort of new kinds of
near crashes that humans typically
wouldn't that course so those are the
risks that's sort of what's at stake so
I'm
it seems I'm that I'm arguing that part
of minimizing those risks is having high
fidelity models of human behavior and
why do I say that do I say it because I
think we need to make babies drive like
humans maybe we do to some extent that's
not my main motivation for it so this is
just an example of some leads research
that I wasn't so involved in myself
actually but but where we looked at how
how humans for example extra safety
margins when passing a parked car and
then looked into to extent humans wanted
AVS to do that similarly so on some
course level for that kind of thing we
need models of how humans do stuff but I
wouldn't quite necessarily go as far as
to say that we need high fidelity models
that are very detailed and exact the
other thing that maybe most Navy
engineers think about when we talk about
moles of human behavior it is its own
lines of real-time on-board AV
predictions about human behavior right
so there's a lot of literature on this
this is just one example so basically if
we have some sensor data about
surrounding road users with some history
we try to apply some algorithm or method
to predict what these guys are gonna do
in the future right really important for
for a v's maybe planning algorithms and
so obviously they they rely to some
extent on models models are maybe not
very advanced and I my interpretation of
this is that a big part of the limiting
factor is still just things like dealing
with the sensor data and uncertainty in
that and so on so so of course if we had
super good - ability models of human
behavior they would be useful for this
kind of thing but that's not the sort of
prime concern at the moment but I think
one area where it is a prime concern
already and perhaps not sufficiently
appreciated is for as agents for virtual
environments for simulated AV testing so
you may or may not have heard that
people are talking about the importance
of simulate simulation based testing so
basically running lots and lots of
simulated miles or kilometers or driving
so there the image here is from
from a Wayman report it's their platform
where for example they can replay log
data so the the the surrounding road
users might come from logged data log
scenarios any quick vehicle either
driven by a human or an automated
vehicle have collected these log data
and then as soon as you update your
algorithms your software on the on the
orbit vehicle you can rerun all of that
long beta to see that you haven't
introduced something which causes
stranger or dangerous situations right
so that's really important but of course
as soon as your update changes the
behavior of this logged vehicle even the
slightest rights the the surrounding log
producers they're they're sort of just
being replayed open-loop so there's no
interaction so if you change your
behavior as Navy here you don't know how
the others would have responded to that
so that's the point where you start
needing models and people have been
talking about it but unless there is
lots of stuff happening under industry
hoods that isn't being published at all
to me it seems like just as it was three
years ago we we don't know how to model
how humans interact in traffic it's a
really complicated problem so in in one
sense it's fair to ask or I mean these
simulations are important people are
saying that's how we're gonna know that
automated vehicles are safe because
we're gonna run billions of miles of
simulated testing but if there aren't
really any humans in that simulated
testing is that really gonna cut it it's
not good enough so I mentioned 800
models at the start and I think these
are super important there's a lot of
work on this as well this is one example
where yeah Bonita extracted the
trajectories from camera data and then
used sort of imitation learning machine
learning algorithm to create agents that
would behave as much as possible like
the the observed humans did in this
situation and other traffic situations
so they these methods begin to achieve
realistic looking routine traffic which
is great and I think it's really
important but I think there are also a
couple of challenges in relation to
specifically those two main risks I
mentioned at the start right so when it
comes behavior of humans in near crashes
and crashes it's important to realize
that in any real traffic data set and
these models is machine normals you need
a lot of data from real traffic right
even if you have lots and lots of data
near crashes and crashes are gonna be
very very rare so it's gonna be very
hard to know to what extent your models
here generalize well to too near crash
situations which arguably are are very
important right for the simulated
testing in verification the other thing
is is with respect to human behavior and
local interactions so how know that
these models are capturing the important
subtleties of behaviour so it sort of
when you squint a bit and you look at
these simulations they look look sort of
like real thing but on the on the
detailed level how can we know that the
important bits are our rights and I
might suggest an stress poor part of an
answer is that we should complement
these blackbox machine learning problems
with white box neurocognitive models
sort of models based science and similar
that will then have the potential to
provide us with some insight into how
mechanisms generalized addressing this
issue of generalization to near crash
behavior which according mention is
something I've I and others have done a
lot of research on and it sort of gets
us into this scientific frame of working
where we can have a model and hypothesis
we can run a control experiment testing
it parameterizing it and so on and that
ultimately leads to something like
understanding right you start to
understand maybe what the important
subtleties are local interactions are
for example and I think we can't really
solve the problem of getting
subtleties of interactions right without
sort of trying to understand it to some
extent
black box was really important but we
also need to have some understanding
okay so that was sort of my main little
first part my first little part so I
thought I'd just pause for a moment here
and ask if there are any thoughts or
questions
that's far and if not I'll continue
straight on so just if you have a
question please raise your hand we're
surely not in real life or just post it
in the chat and we'll allow for in two
seconds if there are other questions
then go counting to 20 mi head have you
continued your kodi and we can catch up
here I have another slot and at the end
as well yeah okay Cheers okay yes so
let's say a bit of what I and others
have done if you start just sort of make
models and modeling frameworks for these
kinds of things so so one of the first
things I did in in driver modelling
research was to try to get a framework
that could cover both routine driving
and near crash driving and what I tried
to do there because I was interested in
how the brain does stuff right how
humans actually do things I I tried to
steal as many good ideas as I could from
from psychology so like perceptual
heuristics this idea that there are
specific sort of salient pieces of
information in our environment we make
use of as part of heuristics when we
control our behavior and decision-making
mechanisms especially in terms of
evidence accumulation that you may or
may not have heard of as a sort of a
model of how the brain makes decisions
it's ads together bits of evidence until
it reaches a threshold where a decision
on what to do is made so there's both
behavioral and neurophysiological prefer
for that kind of model and then also
another part that I've made useless
obviously the idea of motor primitives
that you can even potaters that or sort
of look like there continues and
sustained over time can be broken down
to minimal in blocks of control or
behavior and that's those bits together
in your framework that sort of explains
routine driving in the air crash driving
in the same language so where is it
conventionally rooty driving is often
described as a closed loop short delay
well adjusted or even optimal control if
you look at how many a crash driving it
described it's described as open-loop
behavior without concern of how
something is sort of playing out after
you've done it with a long random delays
before you do anything and under and
over reactions right maybe you brake too
little you steer too much that kind of
thing so anything but optimal whereas in
practice obviously these they have just
sort of be the same thing there's no
sharp edge between them right it has to
be a grayscale of some sort then very
briefly put you can sort of tie these
together by positing that there are
these intermittent motor primitive
adjustments that are cited on by
accumulation of evidence of various
kinds and you use maybe perception
heuristics to determine how much control
to apply and both mystics might be well
adapted to to routine driving but when
you sort of push them out into a context
near crash context where they were that
they were not learned for they might be
severely suboptimal a sensory
predictions also place in the test run
but you can could sort of get out of
this or models that can be used for
example as sort of cognitive the crash
test dummy right so that's something I
and collaborators have been publishing
on a bit so yeah so I've mentioned some
examples about what we've applied it to
- for example explain routine and near
crash braking so we've shown that among
a bunch of different models that we
compared this kind of accumulation model
was the best one at explaining response
time brake response time distributions
in normal traffic gifs or car following
traffic but also applying the same sort
of framework and in even more detail
actually to also explain the the brake
control to some extent we looked at the
naturalistic data from the shore
to data set that you might have heard of
a US large data set with actual crashes
in the air pressure then it crashes in
it log data we can also pull that sort
of model probability distributions can
be we can find model parameterizations
that are sensible and that help explain
this air crash braking as well and also
explain additional parts of it that were
difficult to understand without a model
so that's a preprint out right now the
link there and another type of extension
of this is to to pick up on these
sensory prediction aspects of it that I
mentioned to connect it with predictive
processing types of models and apply it
automation failures right so if you're
driving a car that is it's controlling
your your speed under the car in front
brakes which means that normally if you
were not doing anything and the system
wasn't doing anything you'd see a visual
looming this obstacle rising like this
black line but since you know that you
have an automated vehicle you're
expecting the loom to rise slower and
when as the sort of system is taking
care of the situation but what we were
then suggesting was that the kind of
delays that you see in responding to
these so yeah so if the system fails and
it doesn't actually break for you the
way it's supposed to do though typically
happens is that drivers do not respond
as quickly of course to the lead vehicle
this duration as if they have been in
control themselves but within the next
in bists by assuming that what they're
responding to now in this automated mode
is the prediction error between what
they were expecting to say for a working
automation compared to what they
actually saw so and if we fit the model
to the the the manual braking of drivers
we can just without any further
parameter changes just apply this
prediction error thing and get pretty
good predictions of the delay bounces
when people were experiencing silent
automation failures and yes another sort
of benefits of doing the
kinds of models which linked together
with cognitive science and psychology
concepts that have been studied on a
sort of more basic component level is
that you can also connect you to things
like your physiology so we have been
starting to do some work on for these
types of driving related tasks see if we
can like other time for more basic tasks
if we can see traces of these kinds of
evidence accumulation processes are
happening in the brain and it turns out
we can to some extent obviously it turns
out to be more complex than one might
have hoped from the start but but we do
see the same kinds of signatures as you
see in more typical lab based tasks yeah
so this is just some EEG data I won't go
through the details okay so that's sort
of work in the past and current work
focusing on sort of one we vehicle at a
time right but now we want to generalize
this to interactions so the first thing
we did in one project which is now
coming to an end
it has its final event tomorrow on
Friday I think you can I don't know if
you can still register yeah if you're
interested ask me afterwards there's you
can google interact project on
interactions between automated vehicles
and humans and we did some modeling work
in there on for example a pedestrian
crossing scenario also a sort of driver
turning scenario we spent most effort on
the pedestrian crossing scenario and
well we yeah in VR experiments had
people caught in front of oncoming
vehicles that behaved in different ways
every little panel you see here is for a
different scenario a different way that
this vehicle was approaching the
pedestrian who decided to either cross
before or after the car right so then
you get these rather complicated
probability distributions which are
often bimodal so either you sort of pass
before the car early or there's a gap
here in the middle where you it's too
scary in the past we've has a fter words
and also in this the seller rating stop
and kind of thing that you might
recognize from real traffic write your
own behavior or at least I tend to do
that quite often you see a car that in
hindsight you understand was stopping
for you all along but but
you end up waiting until it has come to
almost a complete stop before you pass
so we saw that in the these experiments
as well we started playing around with
how to maybe account for these kinds of
behaviour models and we had some rather
complex ideas of stringing together a
bunch of different decision-making
evidence accumulation units making
perceptual decisions on the nature of I
can make it across before the car or the
car is stopping for me which then would
excite or inhibit action in decisions
like I am crossing now right and we also
tried some simpler versions with just a
single evidence accumulation unit but
were the same kinds of inputs and what
we found was quite nicely that all these
models even the simplest ones actually
work quite well to capture the overall
gist of all these very complex
probability distributions and in current
work that we haven't yet published on
we're getting even sort of better fits
and a nicer models out of this so that's
good and yes then the tying back to this
kind of scenario I mentioned and one of
my portal questions in the store this
engineer who is who's using simulations
to tweak AV behavior so that's one ones
administration of that kind of idea we
can take this this first model for this
rather simple pedestrian crossing
tomorrow and we can start playing around
with it in simulation right so the
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okay and that was sort of an overview of
stuff we've done I don't know if you had
received any questions at this point or
if anyone in the audience wants to chip
in anything yeah I just wanted to
mention that for the last minute or so
when you explained the
the moon simulations and do you have
animations there we couldn't really hear
you it seems like your bandwidth is
limited and it was all taken by these
nice animations there so we could really
hear you for the last light
other than that it was working I thought
we already might have some questions by
this stage please raise your hand or add
the question yes Suresh wants to speak
Suresh can even use herself yes hello
kuzey yeah thank you very much I'll keep
it short I wanted to know more a bit
more about the looming prediction model
I didn't really understand how you
predicted yeah how you created the
predictions about what they think the
future states of the autonomous vehicle
would be
girl stuff could you hear the question
so I made the mistake he was starting to
go back through my slides and I go hit
those animations slide so I wanted to I
understood that you wanted me to go into
the prediction looming prediction model
yes but then I didn't catch your
question so it was if there was more to
the question could you please repeat it
sorry no not really sir I just wanted to
understand the basics of the model at
least as to how you make the predictions
about what the humans think that
enormous vehicle would be in the future
that's what it does it right yes yeah
okay yes okay let's see here we are yes
so basically the basic model since you
just take this visual looming this it's
it's sort of the the the relative
optical expansion rate of this this car
in front of you how how fasts relatively
speaking it's growing on your retina
over time so let's describe by this
black line and then the model just
accumulates that basically to a
threshold with some noise and that's
what gives the the normal behavior and
then the the key bit here is to run some
simulations when with the the system and
the system is in control and when it's
working like it should so so the looming
that a driver would see when the system
is working as it should is this dotted
line here so that's for a lead vehicle
deceleration scenario of the nature that
was studied in this study me the driver
from the training and maybe from real
world experience as well would have
learned it's the assumption that one
system works I will see a very moving
signal which looks like this dotted line
sorry so yeah I was protecting to the
wrong line for before see the actual and
the normal knew in signal is the dashed
one sorry obviously it's that one and
the one that it's the case when the
system is working as it should it's the
dotted one and then the prediction error
between the two so the difference
between these two lines is the black
line down here sorry I was pointing to
the wrong one
which as you can see actually sort of
rises more slowly which leads to this
prediction of slower break response time
when we just feed it to the exact same
model them parameterised on the manual
breaking behavior we get we hit quite
nicely these break response times that
we saw under study what's not a bit more
clear yeah that is very helpful yeah
thank you very much mmm so we have
another question from Monique Becker's
here and oft as well so do you want to
ask a question in person because we
posted on the chat right no we cannot
hear it we know it's almost very low I
think because it is my microphone
because Nick is in the same room with me
so you can hear maybe that's an accident
all right
can you hear me now yes perfect
so yeah I asked question in the chat
already thank you first of all for the
great presentation super interesting
already so I was wondering or you can
also read the question in chat but so
most of these behavioral models are
based on off the interaction with the
static actor for instance like a car
coming towards you with like a fixed
speed or anything so what are your
thoughts on you know when these
interactions actually become dynamic so
you influence each other so people like
their the AV and the human adapt to each
other do these people model still hold
these in so what do you think about what
are your thoughts about that I think
it's super super important question and
definitely like what we've done so far
with these role crossing models thus
does that and not not extend to that so
we're just modeling one agent and then
we're sort of like you said in those
studies for example but also in our
simulations we're sort of controlling
what the oncoming car is doing so
obviously then what you need is to have
models of both actors right
and that would then that means that the
pedestrian model in this case is you
could argue that in in some ways it is
sort of taking into account what the
this oncoming car is doing so if you had
a model of the car that can either
decide to pass or decelerate in
different ways depending on where the
pedestrian does this pedestrian model is
sort of equipped to respond to those
behavioral choices of the car right but
then you also need a model of the of the
the car and the car driver itself or
we're a navy right so oh but that's so
that that's stuff where we're starting
to broach now in currently ongoing
projects but it is an order of magnitude
more more challenging and for like you
said yeah I think you mentioned sort of
studies human-in-the-loop studies I
think that's definitely something that
we want to do more off so so we have
started Union lead since we have this
nice benefit of having a super nice
pedestrian simulator a cave as well as
sort of had multi display type question
simulators we also have a few different
very nice driving simulators so we've
started connecting those together to run
sort of cold simulations where humans
can interact so that's that's one method
of trying to get data on that kind of
thing but of course then the complexity
of setting those studies up as you can
imagine yes also in order of magnitude
more larger so yeah not not an easy
problem but a very important one for
sure great thank you yeah looking
forward to reading about it so good I
think we're running out of time for the
questions for this part of the talk so
they want to keep going yes let's do
that
no no just keep animations now with
a station of the jitsi to prioritize the
graphics over my voice yes so a little
bit like you were saying in this last
question here right there's lots more to
interactions in traffic than what was
covered in these flights so this is just
a figure from my recent paper I'm
drinking too much had recent paper where
we still wanted to look a bit more
conceptually I mean what is this beast
what are interactions right and try to
so this is not a modeling paper it's
more of a concepts and terminology paper
where we defined space sharing conflicts
as an observable situation from which it
can be reasonably infer that your moral
user and your users are intending to
occupy the same region of space at the
same time in the near future but with it
where we then can also define an
interaction that's a situation where the
behavior at least two road users can be
interpreted as being influenced by a
space sharing conflict between they're
all users so that's so the the key thing
right is that they people are competing
over space here or negotiating or
collaborating in some cases and if you
look at what behaviors people then
exhibit there you can see to the left
here a bunch of different types of space
sharing conflict that generate
interactions and to the right you see
different types of behaviors if you look
at the literature what people have seen
and studied different types of behaviors
to either achieve own movement or
perception moving around looking around
but also behaviors that sort of seem to
have the purpose of signaling about own
movement signaling about own perception
and requesting movement or perception
from others and then also behaviors for
for signaling appreciation for the
thank-you or the fu to the other road
users so yeah you can see you can map
out stuff that's been studied and it's
and solves these the crosses are just
examples from papers or what people have
looked at all these different categories
of a breeze which can then overlap right
so when a pedestrian is adapting its
trajectory to yield to a navy it's both
more movement achieving but it's also
moving and signaling so one one aspect
of this which again connects to that
question of how do they adapt to each
other is when you start to think about
strategic behavior or game theoretic
behavior right so that's
Gamze here is the typical thing that you
get into right if you're thinking that
that agents in the world are trying to
maximize some on and some benefit for
themselves they're trying to get through
there the goal on time and safely for
example but then if you have multiple
actors doing that at the same time for
example competing over space like I just
said then you get into a game theoretic
type considerations right so that's
something we have done
Beethoven leads with a PhD student
called the Fontan camera where we've
been able to formulate sort of simple
game theoretic versions of some of these
scenarios and also has also been able to
estimate some of the payoffs in this
situation so how much people prefer
progressing over avoiding crashes how
much crash risk you can accept basically
to get some progress which is really
interesting and elucidates a lot of
interesting things but there's a further
complication to it though which is that
we know from sort of basic studies of
game theory type situations that human
behavior is quite often not game
theoretically optimal even when we can
know exactly what these payoffs these
cost functions value functions are
people don't always behave like Nash
said they should right and and often we
don't know the payoff matrix even right
so the humans value strange things so
sometimes people aren't just
prioritizing getting to their goal
without kalaiy and sometimes people seem
to value actually being polite or
helping helping each other so so yeah
there's challenging things to get into
these these rewards for value functions
but but nicely enough again there's been
on the basic level quite a bit of work
on obviously is in psychology on why and
how people are not getting theoretically
optimal and unsocial orientation in game
theoretic type situation so I just
highlighting here and one interesting
recent paper which connect together game
theorists reasoning with accumulation
models
where you can quite nicely sort of
follow how an agent reasons sort of
iteratively if I do this then he would
do that but if he knows that I would do
that if he does that then I should do
this instead that kind of thing so
that's something to maybe leverage other
important areas for viewer for their
mobile developments that we need to get
into there's these things is human
recognitions of actions and intentions
and communication so again these
pedestrian models I mentioned they do a
bit of that right so they're recognizing
if the car stopping may have also I
didn't mention it but we've also
incorporated some of these new ideas of
external human machine indications for
the AV sort of signals with some
flashing lights are flashing headlights
even to the pedestrian that it's gonna
stop and that can then affect the
behavior of the pedestrian but but if
you look at this in a more bigger
picture for for more kinds of scenarios
you need some more general ideas for how
it works me and then nicely enough there
are again from from cognitive
computational science and computational
neuroscience there there is models and
modeling ideas that sort of address this
kind of thing this is a one example of a
model where in a sort of joint
coordination task the agents need to
sort of understand what the other agent
is doing or is he going left or right
with this arm and then the agents might
sort of exaggerate their movement like
this AV in the simulation data at
exaggerated iteration to be more
understandable and that kind of thing
has been studied and modeled in basic
more basic contexts another complication
that is important to bear in mind in
some situation is that humans can only
look in one direction at a time right so
in some situations it's especially if
there's more than two actors you might
need to actually get your attention and
get allocation modeling to some extent
again there there are models out there
in the more basic literature that you
can consider leveraging and that sort of
fit together quite nicely there again
the nice thing about having this
cognitive science based framework is
that it's relatively easy easy of course
I'm not quite the word but it's feasible
to to pick these are the components
upright so overall the contemporary
condition according to neuroscience so
it provides the needed so that's if a
modeler is allowed to dream you might
say that maybe we can head towards a
more complete neuro cognitive modeling
framework for for interactions in
traffic I won't talk through this in
detail you can look at it later insights
or the recording in oral written ask
questions or whatever that's sort of
maybe works for multiple agents and for
more different scenarios but can still
sort of get these local the subtleties
of local interactions reasonably rights
so yes that's what we're working at the
moment and mainly in a project called
commotions some other ones as well we're
aiming for more complete and recording
to models of interactions and we want to
also as part of the project investigate
the complementarity with more machine
learning models so we're trying to do a
bit of both and see if we can sort of
benchmark them against each other maybe
sort of get the best of both worlds
that kind of thing a few months back we
published a green paper a short one with
some more information about the project
if you want to read that please do there
also some sort of questions and
invitation for input so feel free to get
in touch about that so yeah in summary
I've argued that safe and acceptable
automated vehicles are complementing
data-driven models of human behavior
with models and we and obviously lots of
others as well are working on the
challenge and just very happy to have
input on further discussion Thanks
and any question and let's see I mean
maybe I should open this chat window
Derek would like to speak that's fine
with me yeah great thank you very much
that was it was really interesting and
look at these neuro cognitive models
aligned with the autonomous driving
systems you only wait into it a little
bit but these kind of motor primitives
and this sort of broader vision of how
the brain can handle some things very
quickly I wonder if there's a more sort
of speculative vision that you have for
how this research might progress over
time
respect to the prepare to specifically I
mean sorry on what you're thinking uh
yeah I guess with respect to motor
primitives or I'm thinking about you
know some of these aspects of cognition
that we don't get to very often because
they're deep and the weeds of the motor
system central pattern generators and
things of that nature I just I wonder
whether there's a kind of general
approach to how you see the the code
development of the neuroscience work and
the AI work coming together you know
over a broader timescale but basically
I'm asking you to speculate a little bit
how do you see Venice or say oh yeah
wait I mean even you can I guess you
could start from the motor criminal with
a name that's sort of just branched out
in such a multitude of different ways
okay it's great I don't know and and
there's so many directions you can go I
did write so that what I've done with it
basically just look at it from I've used
this as my sort of minimal building
blocks and I haven't looked for all I
said for me the motor controls are stops
there I can issue a command and
something makes that motor primitive
happen but there's obviously lots of
detailed motor neuroscience work on
exactly how how that works on a more
fine-grained level right there's there's
further hierarchical levels beneath that
where the the more a neuromuscular
system sort of that makes you tries to
ensure that the the desired motor
behavior actually happens
and then there's another connection to
robotics for sure I mean it's not
literature I'm deeply familiar with but
I know that there's been a lot of work
on using motor primitives for for
robotics and I mean I think it's there's
a lot I mean it fits together with this
general perspective of hierarchical
control right so that's so the the level
which which issues the command for motor
primitive can very clearly sort of be
thought of us as one hierarchical level
above another but then I think and I
think there others have said this as
well I'm sure you can sort of again
think of that as having further
hierarchy our he says Bob were above ler
where that motor primitive must be part
of a more compound primitive a behavior
primitive writes that sort of launches a
set of motor primitives in sequence and
turn that's sort of it well adapted to a
longer sort of longer temporal scale of
of the context so yeah as it fits
together with that kind of hard
hierarchical perspective as well I don't
know if anything of that connected a
little bit with what you were aiming for
oh it does No thank you I'll send you a
paper that I came across recently on the
hierarchical control of central pattern
generators
you know it's just it's all kinds of
weird things in the neurosciences is
such a huge field and this one in
particular had some very nice
visualizations and it it created a I
think a kind of practical view of
exactly what what you're talking about
I guess the place that I'm curious is is
what what are some of the implications
that that might have for training I mean
I'm trying to kind of do too much at one
so there's sort of these small responses
that we want to to train up and build
together I mean that that's kind of part
of my take away
mmm I mean when you're training the
automated vehicle for example yes
exactly
yeah I mean it's certainly about it we I
mean part of how we think we human
learns to write with this babbling right
but and you get a basic control of your
basic motor primitives and then you
start sort of compounding them together
to more advanced behaviors okay
yeah wanted to speak hello can I ask a
question yes can you hear me I can't hi
oh uh thank you very much very
interesting presentation
my question goes maybe a little bit
follow up with what Nick asked before
about this idea for motor interactions
and there we can have some let's say
some unexpected emergent properties deal
there and there I connect a little bit
from the work on ethics I don't know if
you're familiar with the what's called
the naturalistic fallacy
what is the impossibility to connect
aught so what we should do from what it
is so so it there it is not released
fastly that if something is natural
therefore it's morally acceptable but
it's very challenged that so in this
sense how do you see this kind of moral
values norms and the brothers and the
moral reasoning for autonomous vehicles
is that something you consider relevant
or yeah what are your thoughts on that
for sure relevant definitely definitely
I must confess I haven't thought much
about how that connects in here so
fortunate try to turn on my video and
maybe this limited bandwidth recording
was mentioning was playing some tricks
to me because I couldn't catch all of
your questions or your question but you
know I haven't I haven't thought much
about the connection between the
modeling work and the more elements but
you were saying something about natural
and yeah it's called a naturalistic
fallacy so what it means in ethics is
the impossibility of the rife in the art
from ease so you should not derive
what the autonomous vehicle should do
based only only what the humans do for
example take the human factor at the
center of what is right because there in
this discussion and if they say there
may be there are some moral principles
or some other things that should also be
considered on these aspects and I'm here
I'm not really talking about trolley
problems these kind of things but enough
more practical gem moral reasoning here
yeah I think one possible connection
maybe it's in terms of sort of but I
mean I'm not the connection hole
completely but but to some extent I mean
things that we that we dislike in others
like we dislike others to do or I mean
I'm not sure whether that fits exactly
with the definition of what's moral or
not but there are some behaviors in
traffic that others do which I would
dislike and I would maybe say that that
was sort of an immoral thing to do so I
think my connection to the models there
is that if we are able to get as I'm
hoping to these more sort of models
where the this negotiation process is
covered some explicitly this in
theoretical oshi ation then in theory it
should possible to say so from a given
model simulation whether the model was
happy or not with what the other actor
did mm-hmm so so I'm not sure whether
that sure it's it's true that if the
model was unhappy with what AV
did whether that means that the baby
behaved immorally I'm not sure I'm not
sure if that is true but maybe there's
some weak link at least yeah yeah I can
definitely some some connections on does
also a moral acceptance of this exactly
society there I can definitely see some
some links there and okay Larry and
maybe I'll get in touch them soon to
discuss some other ideas I have there
yes please okay thank you very much
thanks for a really nice question we
also have a question from
I'm not sure if I'm pronouncing the name
correctly do you want to ask a question
person or posted and shot
show me or sell me um right
sorry working then yeah you could type
your question in check I mean while can
I ask a question myself so I'm sure good
stuff you mentioned the really important
point in the beginning that you think
these newer cognitive models of humans
are necessary but what suffusion all
right I agree absolutely agree that is
necessary but I'm biased here of course
but I was wondering on European whether
you think the data driven models that
you mentioned are also necessary or we
could get away with them without them
just using European models and if you
think they are necessary and what is
your take on integrating for instance in
the context of virtual simulations of
babies integrating data-driven models
and neurocognitive ones good question
thanks and I would say that in in theory
they are not necessary but in practice
they are necessary the data-driven
models because in theory we could make
fantastic neurocognitive models that
sort of capture human behavior very
nicely at cause across the sort of vast
multitude of different scenarios but
that's just a very tall order and and
it'll take some time if it's ever
possible right so human behavior is just
so super complex so so yes we can make
these I think what we can hope for is to
make neurocognitive models that can sort
of deal with some local situations with
with a few actors and then maybe sort of
build a catalog of different scenarios
that we can believe that we can address
reasonably but but to actually do then
these large-scale simulated verification
and so on we need models that can can
scale up much more to bigger variety of
of scenarios and so on
there I think that's where the
data-driven machine learn models can
shine more so they we can't be sure that
they get every sort of detail right we
can be sure that they get the near crash
stuff right necessarily but then we can
on those for those sites we can sort of
complement it with at the very least we
can sort of do both things right that's
the simplest way of combining the two
approaches that was the second part of
your question how can we combine and so
the simplest way is just to do both in
parallel right we can test our IDs both
with machine learning models and with
neurocognitive models but then in terms
of combining them we're just sort of
scratching the surface of that question
but there's a there's a bunch of
different approaches I mean so if the
argument is that we have too little data
of near crash situations maybe we can
generate near crash data with the newer
counter models that's one kind of
connection and another kind of
possibility would be to sort of look at
the newer models and see if their
architecture can inspire the
architecture of the typically neural
networks that we that we would use in
the machine learn caso so maybe by
figuring out some aspects of the newer
content models that are important to get
right we can sort of get them in to help
the Machine run bottles get those
park-like by by controlling their
architecture so so yeah so I think
there's a some some different approaches
some of them connecting them together a
bit more others keeping them a little
bit more separate interchanging this
data but I think we're just sort of
beginning that journey
good thanks very interesting appear so
sell me posted that question in the chat
they want to read the Torah can read
file yes I'm not sure whether the data
use for model is from naturalistic data
and for a manual driving car if so how
would it influence when considering the
human users interaction with ABS what do
you think yeah so yes so I guess so the
data we have used has been for some of
it's been firm from naturalistic data
sources from other it's been controlled
experiments
in in the pedestrian crossing situation
I mentioned that's been in controlled VR
experiments with with a car that it's
computer-controlled I mean it's not
necessarily I mean you can either put a
little human in there that the the
participant may or may not see and then
it's sort of in quotation marks a human
driven car or it can be an AV especially
if it has those flashing external HM
eyes but but I do agree it's an
important distinction sort of if we in
terms of generalization right if we I
think what I'm trying to focus on at the
moment is to get to understand human
human interaction and model that that's
sort of my starting point but I agree
that an important part of it is this all
also to make sure that if the the AV
then starts behaving a little bit
differently from from the human in some
way or another that the models also
generalize correctly to that so that's
again sort of a challenge to the data
driven models right so if the AV that
you're trying to test thus something I
hadn't thought about that specific angle
before actually so thanks for that does
something differently from what
typically humans would do in that
situation how do you know that your your
machine learning models are generalizing
correctly then and of course that's
something you would also need to test
with with more white box Theory driven
model as well I hope that's answered
your question to some extent okay do we
have any other questions we are
technically out of time but there is
time for one question
maybe now that's fine I'm in no rush
person right okay maybe last question
so we were mostly talking about
interactions between a human and baby
right which implicitly assumes that
there are two agents here a human and an
AV so as far as I'm aware the
neurocognitive models for instance of
decision-making attention models they
are tested and very limited lab based
situations so there is very early more
than two agents which we can account for
in this kind of box so do you think this
kind of generalization to multi agent
interactions is possible i I I would
hope so but ever I agree that's another
sort of further complication and and how
like you said this when people have
studied in the lab interactions it has
been I can't think of any paper doing
that with more than two people either
when we're doing it modeling at that
sort of detailed level so yeah so the
the I mean I'm certainly trying to in my
modeling work now try to sort of take
height or whatever if that's an
expression in English to sort of have
ideas about how how you could consider
multiple agents but it is a complication
in its own rights for sure and I'm not
necessarily one like you say there is
great support for currently and
computational cognitive science so that
it would be nice if more computational
cognitive science scientists addressed
that in the lab for sure so we could
steal their models for these applied
purposes okay thanks
yeah if there are no more questions
let's wrap it up
so thanks a lot good stuff for the
really really interesting talk I think
that this meeting has hit a record at
some point we had 50 more than 50 people
present so that is something we haven't
done before so there is a lot of
interest in what you're doing yeah
thanks a lot
that was very interesting and very
stimulating looking forward to getting
into more detail of discussions with you
and also I'm pretty sure that you'll be
getting some more emails in the in the
in the coming days yes please don't it
please don't hesitate to get in yeah
thanks a lot peace on earth states I'm
sure everyone listening to get in touch
if you have thoughts or questions or
whatever and yeah it's been an absolute
pleasure I had good fun and
thanks oh good okay bye-bye
Thanks