In college, I was biking home from class and I got hit by a car. Hard. Here’s a picture of the
aftermath – but don’t worry my bike was fine and after a couple unintentional flips in the
air I was able to walk away. This incident was the other driver’s fault. I had a green light,
but they just didn’t see me coming. So I can’t help but think that,
(1) I got really lucky. It should have been worse. And (2), if that same incident happened today – it
might not have happened at all. The tech in the car could have seen me coming and intervened.
The research for this type of tech is happening right now and it’s looking really good:
here’s a peek at what it looks like – if you want to deep dive into this with the
actual researchers go sign up for Keysight World Innovate and watch the full talks – think Ted
Talks for Engineers - but I was watching these talks and my crash kept replaying in my head,
and I can’t help but get excited thinking this new technology could prevent other people from going
through what I went through. 94% of accidents are caused by human error – my accident was – and good
systems could dramatically lower that number. There are a couple clever avenues of research
in this space that I find fascinating, here are three of them that were explored in-depth
during Keysight World Innovate and I’ll break them down a bit.
The first clever engineering trick uses invisible waves – not the ones you’re thinking,
not electromagnetic waves, but sound waves to see around corners. Sound is one of those things
you don’t think about while driving but then when a song on the radio has a siren sound in
it, and you’re like where’s the cop, where’s the ambulance, oh it was the song. So sound
can be used to see things you can’t see. That’s a big challenge with Advanced Driver
Assistance Systems, or ADAS, is that you can’t see around blind corners.
There are proposed Vehicle to Everything systems (V2X) that could use, basically
cell pings to figure out where people and other vehicles are, but this method works without 6G or
V2X networks – it simply uses a microphone array on the vehicle, so you can hear what’s coming.
And if you’re a clever engineer, you can even use reflection patterns like this – there’s
a car coming from the right, and the sound is also reflecting off of the wall. So you
could train a machine learning model on what a car sounds like, and use other systems to know
there’s a wall there, and figure out there’s a car coming long before LIDAR or cameras
will pick it up – an entire second sooner based on this research from Dariu Gavrila,
head of the intelligent vehicles group and professor at Delft University of the Netherlands.
This same group is also using Dynamic Bayesian Networks to predict what pedestrians and cyclists
will do a few seconds before they actually do it. If you know what they’re going to do,
you know how the vehicle should respond. I confess, I had to look up what Dynamic Bayesian
Network is – I’ll spare you the gory details but it’s pretty cool and this is something you going
to hear more about in the years to come so you might as well get the crash course now. Basically,
a Bayesian Network – in this type of application – lets you predict what’s going to happen based on a
set of weighted variables. Take a pedestrian for example, how close to the curb are they,
how fast are they moving, and in what direction? Those are each variables, and pretty obvious clues
about what they’re going to do. For example, if they’re 10 feet away from the road and not walking
towards the road, they’re probably not going to be something your vehicle has to worry about.
But what about more nuanced things like are they looking at your vehicle coming towards them or are
they looking somewhere else? That’s a variable. What about their body position? Are they going
for it, are they slowing down? Or other vehicles, if another car has a blinker on, we need to weight
that heavily because they’re probably going to turn. These are things we inherently understand
as humans, but we need to specify each of these as variables in a Bayesian Network. There’s also
“latent” variables which are things you aren’t directly observable, it’s kinda like a state
switching on or off, for example a cyclist wanting to make a turn – they hopefully signal with their
arm, but then put their arm down to make the turn. The fact that they signaled with their arm is
important and should be a weighted variable, even if they aren’t actively doing in that moment. It’s
kinda state-machiney. But basically, you have a bunch of variables, they’re all inter-related,
and an expert in Bayesian Networks and machine learning can lay out the variable relationships
and weight variables appropriately to predict what’s going to happen. Will
that person step into the street or not? And then, a Dynamic Bayesian Network,
or DBN adds the word dynamic because it includes time – which
is helpful because we live in time. So, a DBN is an expert-driven prediction model, which is
nice because, 1, you get those latent context variables, 2, it only takes 10s or hundreds of
variables to be effective, and 3, you can explain how it made it’s prediction – you can trace the
math and that’s a really good thing for safety. A DBN is different than a data driven model like
a neural network or transformer, those need 10s or hundreds of thousands of parameters,
and you don’t really know specifically how it arrived at a certain answer. A data driven model
has proven to have better performance IF you have sufficient training data – which is a huge
huge challenge, so DBNs are worth researching. So, here’s a DBN in action thanks to Professor
Dariu at the Pantheon in Rome, tracking people and this is a nightmare driving scenario for me,
but it’s something a Level 5 autonomous driving system would have to handle without driver input.
A more simple case is this one. We know blue fella isn’t going anywhere, but what about these other
two. Brown is approaching the curb, but it looks like he’s slowing down, and he’s looking at the
car. These are all variables in our DBN, and the DBN chooses which context variables matter here –
which ones are we going to use - and we predict he’s going to stop. And it turns out he does.
This joker over here, though, he’s not looking and he’s still walking,
those variables become very important and the vehicle decides to stop so we don’t take him out.
Another area of research – from one of the Keysight world innovate talks by Chris Gerdes,
a professor at Stanford – is basically summed us as “racecar go fast” vroom vroom – or if
you want to be more formal, “vehicle dynamics” So, remember the guy who wasn’t paying attention
and walked in front of the car. Our DBN decided he wasn’t going to stop, so now we have to slam
on the brakes or plot out a route around them. But how fast can we stop? How sharp can we
turn? What if it’s windy or rainy or the tires are bald?
The car we’re in needs to know how far it can push itself before the wheels lock up – which is bad
for traction and performance. So you have what’s called a friction circle – Each of your tires gets
about a palm sized connection with the ground – and we want to take advantage of it. So Chris and
his team are able to realtime plot the friction circles of each tire while autonomously ripping
around a racetrack. The line is like the force vector for each tire, and the circle is the limit
of how much each tire can be pushed. The size of the friction circle changes based on how the
car is moving – a left turn means more friction on the right tires. You don’t want your vector,
or line to go outside the circle or you lose traction in that wheel. If you lose traction you
can’t turn as sharply or stop as fast. Their test car can brake each wheel individually and update
braking forces 200 times per second – making near real time route planning and braking decisions.
So, if your model is telling you that someone is going to walk out in the road in front of you,
you can now plot a new path and do everything physically possible to avoid them. I’m a decent
driver, but I bet machines could do it better. With systems like this – which exist now and
are getting better and better thanks to folks like Chris, Dariu, and their teams,
we can dramatically reduce incidents like this. If you stuck around for this whole thing, you
definitely should go sign up for Keysight World Innovate and watch the full talks – it’s all 100%
free – and this was just my personal highlights from one session – there’s a lot more there on
a lot more topics – I’ll put an easy link for ya, go check it out – and don’t drive like my brother,
drive like Big brother - well probably don't drive like big brother...Drive safe.