Imagine you're driving home from work. It's,
um, it's dusk. Right. The sun is just starting
to dip below the horizon. Exactly. Casting those
really long shadows across the pavement. Now
for you, as a human driver, this is a pretty
standard, relatively safe time to be on the road.
Yeah. You just flip your headlights on, adjust
your visor, and you don't even think twice about
it. You really don't. Yeah. But if you are a
self -driving car, This exact time of day is
actually the deadliest. Which is wild to think
about. It is. Statistically, autonomous vehicles
are like more than five times more likely to
crash when the sun goes down compared to human
drivers. It completely upends our intuitive understanding
of safety, honestly. I mean, we tend to assume
a machine equipped with infrared sensors, radar,
and 360 -degree cameras would practically own
the night. Right. We assume the tech is infallible.
Exactly. But the data tells a drastically different,
much more complicated story. Welcome to today's
Deep Dive. Today is Thursday, March 26, 2026,
and we are staring down a massive, comprehensive
Wikipedia repository that details the exact current
state of self -driving cars. There is a lot of
material here. So much. And our mission for you
today is to cut through the sci -fi marketing
hype, bypass those terrifying news headlines,
and extract the absolute reality of autonomous
vehicles right now. Yeah, we're going to get
into how they actually work, why they still crash.
The incredibly messy ethical dilemmas they create,
and what this all means for you and your morning
commute. It's a lot to navigate. I mean, we're
looking at a web of rapidly evolving tech, deeply
complex legal frameworks, and shifting social
data. Right. But understanding the actual mechanics
behind the headlines is really the only way to
grasp where this is all heading. Which is exactly
why you are the perfect guide to help us synthesize
all of this today. Okay, let's unpack this. Let's
do it. Before we can even judge if self -driving
cars are successful, we have to define what self
-driving actually means. And surprisingly, reading
through these sources, the industry doesn't even
have a universally agreed -upon standard right
now. Yeah, that is the core of the problem right
there. For years, the technical community relied
on the SAE, the Society of Automotive Engineers.
Right. They created that scale of automation,
right? Level zero to level five. Exactly. But
for anyone who follows technology, we know this
scale has basically become this convoluted marketing
tool. Oh, totally. The critical part that most
consumers miss is that these levels aren't just
some checklist of cool features. They are strictly
based on the division of legal responsibility
between the human and the machine. Right, so
let's breeze through those real quick because
they are everywhere in the marketing. Level zero
through two. You are the driver. You are legally
responsible, period. Even if the car is actively
steering and braking to keep you in the lane,
your eyes must be on the road. Exactly. And level
three is where the car genuinely drives, but
you have to be ready to intervene. Right, like
if the car gets confused, it pings you. Yeah,
and level four means the system drives and can
safely pull itself over if you ignore that alarm.
And finally, level five, which is just science
fiction right now. That's a car that can drive
anywhere, anytime, in a blizzard or a hurricane,
without a steering wheel at all. But the confusion
comes from how these levels interact with this
concept called ODD. Operational Design Domain.
Right. The ODD dictates the exact environmental
boundaries where a car can operate safely. Yeah.
Like, is it raining? How fast are we going? Are
we on a pre -mapped highway or some random...
dirt road. It's all about context. There's an
analogy in the source material that perfectly
nails this mechanism. Think of it like a human
being asked to stand unassisted on one leg. Okay.
That action is the dynamic driving requirement.
Now, if you do that on solid ground, your designated
operational design domain, you're totally fine.
You can balance all day. Sure. But if you try
to stand on one leg on a tightrope suspended
in the air, suddenly your environment has changed.
Your capability hasn't changed, right? No. But
the Duane has. and you need support to keep from
falling. What's fascinating here is how car makers
exploit this exact confusion between a vehicle's
capability and its operational domain. How do
you mean? Well, a company might loudly advertise
that they have a level four car. But what they
actually mean is that the car has a level four
feature, like, say, automated valley parking.
Oh, right. But that feature only works inside
a very specific geofence pre -mapped concrete
garage. The moment you take that exact same car
out of the garage, and onto the highway, the
system downgrades to level two. Wow. Yeah, you
have to keep your hands on the wheel. which is
incredibly deceptive. I mean, if I buy a smartphone,
it's a smartphone, whether I'm in my living room
or the grocery store, it doesn't downgrade into
a rotary phone when I cross the street. No, it
doesn't. It's no wonder consumers are confused,
which is why the CEO of Mobileye proposed a totally
different way to talk about this in the sources,
moving away from those rigid SAE numbers. Yes,
Mobileye suggested terms that actually describe
human behavior, which is so much more intuitive.
Like what? They use categories like eyes on,
hands on, or eyes on, hands off. Then, eyes off,
hands off, and finally, no driver. clarifies
the legal liability immediately. Like if you
are in an eyes -on system, it doesn't matter
what the steering wheel is doing. You are the
driver. If you hit something, it is your fault.
Exactly. It removes the ambiguity. So now that
we know the playing field and the terminology,
how are these companies actually trying to build
these machines? How do these cars practically,
you know, see the road? Oh, this is a huge debate.
Because going through the research, there's a
massive philosophical civil war happening in
the tech world. regarding the sensors. There
really is. It essentially boils down to two main
sensory approaches. On one side, you have companies
like Waymo. They use an incredibly hardware -heavy
approach. Right, the spinning things on the roof.
Yeah, they rely on LiDAR, which stands for light
detection and ranging. LiDAR basically bounces
millions of light lasers off surrounding objects
every single second to create this hyper -accurate
3D point cloud of the environment. Sounds intense.
It is. But Waymo pairs that live LiDAR feed with
incredibly detailed, pre -recorded maps. We're
talking maps that know exactly where every lane
line, stop sign, and curb is, down to the centimeter.
Before the car even turns onto the street. Exactly.
But the mechanism there seems totally flawed
for scalability. I mean, those maps require massive,
constant upkeep. If a construction crew moves
a traffic cone overnight, or a storm knocks down
a stop sign, the pre -recorded map is suddenly
out of date. And the car's logic could just break.
Yeah. Although I did read in the sources about
systems like MIT's MeepLite. They tried to fix
this by using just basic 2D GPS maps paired with
live sensors, so it doesn't need to be updated
constantly by a whole fleet of mapping vans.
That's true. But then on the totally opposite
end of the spectrum, you have the vision -only
approach. Championed most famously by Tesla,
right? Yes. They have completely stripped out
the expensive LiDAR arrays, and they've even
removed traditional radar. They rely entirely
on optical cameras feeding visual data into what's
called an end -to -end neural network. OK, let's
pause right there, because end -to -end neural
network is one of those massive tech buzzwords
that gets thrown around a lot. It really does.
If I understand the source material correctly,
this means the AI isn't given explicit hard -coded
rules by a programmer. Right? Like, a human didn't
write a line of code saying, if you see a red
octagon with white letters, apply the brakes.
No, not at all. Instead, they feed the computer
millions of hours of video footage of real humans
driving, and the computer essentially learns
to mimic that behavior organically. That's right.
It's like learning a language by living in a
foreign country rather than reading a grammar
textbook. That is a perfect way to describe it.
The system organically learns the concept of
stopping at an intersection by processing how
human drivers navigate thousands of different
intersections. Oh, wow. And Tesla's rationale
for this is simple. Human beings navigate the
world using only our eyes and our brain. Therefore,
a car should be able to do the exact same thing
using cameras and a neural network. Plus optical
cameras are vastly cheaper than lidar rigs. Oh,
significantly cheaper. But if you're listening
to this and thinking, wait a minute... I rely
on my rear -view mirrors, my peripheral vision,
and my hearing to merge onto a highway. I listen
for sirens. I use intuition to guess that a parked
car is about to pull out. Yeah. You aren't crazy.
No, you're absolutely right. That is exactly
why engineers are fighting over this. I have
to push back on the vision -only logic. If a
radar sensor can ping solid objects through a
nighttime snowstorm that completely blinds an
optical camera, Why in the world would you limit
a two -ton vehicle to just eyes? It is a highly
debated engineering choice, but the technical
justification for avoiding multiple types of
sensors comes down to the processing burden,
and something called sensor fusion. Sensor fusion.
Yeah. When you have cameras, radar, and lidar
all feeding gigabytes of information to the car's
computer simultaneously, the computer has to
fuse all that data together to paint one unified
picture of reality. OK, that makes sense. But
what happens when the optical camera says the
road ahead is perfectly clear, but the radar
bounces off a metallic object? sign overhead
and insists there is a stationary object dead
ahead. Oh, the sensors disagree with each other.
Yes. So it's like a jury where the two star witnesses
are giving entirely conflicting testimonies and
the car's brain just freezes trying to figure
out who was lying. Exactly. Synthesizing that
conflicting data takes computational time. It
can cause the AI to hesitate, break suddenly,
or make these dangerous jerky corrections. Thanks.
It's like a human trying to read a book, text
a friend, and listen to a deep dive all at the
exact same time. Eventually, the processing lag
causes a mistake. So by using only vision, the
system theoretically processes one type of data
much faster. Right. But as you pointed out, it
sacrifices the superhuman penetrating capability
of radar. And ultimately, the hardest part of
all this isn't just seeing the road, it's behavior
prediction. Meaning, it's not enough to just
identify a pedestrian standing on the sidewalk.
Right. An autonomous vehicle has to calculate
that pedestrian's future trajectory. Like, are
they waiting for the light? Are they looking
at their phone? Exactly. Are they going to suddenly
dart into the street to chase a rolling ball?
The computer has to compute the position, speed,
and likely intent of every single object around
it multiple times a second. So we have these
two computers. the competing philosophies, the
heavy light arm mappers, and the vision only
purists. But how are they actually performing
out in the wild? That's the real question. Because
when you read the news, it feels like autonomous
vehicles are crashing into things every other
day. But the statistics in the sources tell a
much more nuanced story. They do. The gap between
statistical safety and human trust is just staggering
right now. Let's look at that massive 2024 study
published in Nature Communications. OK. They
compared the safety records of autonomous vehicles
to human driven vehicles across tens of thousands
of incident reports. The good news is actually
phenomenal. Yeah. Autonomous vehicles have far
fewer crashes involving pedestrians. About three
percent of crashes compared to 15 percent for
human drivers. That's a huge drop. And the machines
are significantly safer than humans in heavy
rain or fog. I mean, they don't get distracted
by their phones, they don't drive drunk, and
they don't get tired after a 12 hour shift. But
the bad news is deeply concerning, which brings
us right back to your opening point. The study
found that autonomous vehicles are more than
five times more vulnerable to collisions at dawn
and dusk. Because of the shadows. Yeah, the rapidly
changing light, the long shadows, the sun glare.
It directly messes with the contrast required
by cameras and optical sensors. And when these
systems do fail, they fail in ways that humans
find entirely illogical, which just shatters
public trust. Let's look at exactly how they
fail. because the mechanism is fascinating. The
sources detail a 2022 incident where a cruise
robotaxi completely blocked a fire engine that
was responding to an emergency. Why did it do
that? Why did it just freeze in the middle of
the road? Well, the AI is programmed to obey
traffic laws flawlessly. When a fire engine turns
its sirens on and drives on the wrong side of
the road to bypass traffic, it breaks the AI's
programmed logic tree. It literally doesn't know
what to do. Right. The AI's hard -coded rules
hit a conflict. Fire trucks are vehicles. Vehicles
must stay in the right lane. This vehicle is
in the wrong lane moving toward me. I cannot
predict its next legal move. So the system defaults
to the only safe action it knows. It just halts
in confusion. If we connect this to the bigger
picture, we have actually been through this exact
psychological and technological barrier before.
Think about the automatic elevator. Elevators.
Like in an office building. Yes. Automatic elevators
were invented around 1900. But for decades, people
were absolutely terrified to step into a metal
box suspended in a dark shaft without a human
operator standing there physically pushing the
levers. I mean, I kind of get that. Right. They
didn't trust the machine's logic to stop at the
right floor. It took operator strikes, massive
advertising campaigns and the invention of a
giant reassuring red emergency stop button to
finally build public trust. Oh that makes so
much sense. We are currently in the elevator
phase of self -driving cars. People want to know
they can hit a button to stop the machine when
its logic fails. Here's where it gets really
interesting. Let's look at the data from the
National Highway Traffic Safety Administration.
Out of roughly 4 ,000 autonomous vehicle incidents
reported between 2019 and 2024, nearly 54 % of
them involve Teslas. More than half. More than
half. Yet Tesla aggressively markets its level
two system under the brand name full self -driving,
which has led to multiple federal investigations
for deceptive marketing. Because legally, under
that mobile IFA framework we talk about, you
still have to have your eyes on the road. It
perfectly illustrates how terminology shapes
consumer behavior. If you name a software package
full self -driving, people are naturally going
to treat it like it's fully self -driving. Regardless
of the fine print in the owner's manual telling
them to keep their hands on the wheel. Exactly.
And the sources bring up some really tragic incidents.
Tesla's first fatal crash in 2016 happened because
the car literally didn't distinguish a white
tractor trailer crossing the highway against
a brightly lit sky. It just didn't see it. Right.
And there's that 2025 Cybertruck crash on full
self -driving. Waymo hit a bus in 2016 trying
to avoid sandbags in the road. And we have to
mention the 2018 Uber Advanced Technologies group
fatality. That was a perfect storm of human and
machine failure. The safety driver inside the
car was looking down at their phone, watching
a show, and the pedestrian victim crossed the
street illegally with meth in her system. The
car's sensors detected her, but the software
couldn't classify what she was until it was too
late. It's just awful. This brings us to the
underlying decision making of the AI, moving
beyond just the mechanics of safety into the
ethics and the economics. It's not just about
a car freezing in front of a fire truck. It's
about how the AI decides who or what to prioritize
when a crash is mathematically unavoidable. This
is where the engineering hits hard societal reality.
For example, a study from researchers at Georgia
Tech revealed a deeply troubling flaw in AI detection
models. I read this part. It's wild. They found
that autonomous detection systems were about
5 % less effective at recognizing pedestrians
with darker skin tones compared to lighter skin
tones. Which is an astonishing engineering oversight.
Yeah. But if we look at the mechanism of why
that happens, it goes right back to those neural
networks. Yeah. If an AI is trained primarily
on data sets or video footage that lacks diverse
demographic representation, the AI literally
develops a blind spot. It only knows what it
has been shown. Exactly. It forces us to confront
the classic philosophical trolley problem, but
programmed into real life. Oh, the trolley problem.
Right. Imagine an autonomous vehicle is driving
down a narrow street. A pedestrian suddenly darts
out. The car computes that it cannot physically
break in time. It has two choices. Hit the pedestrian,
or swerve into a solid brick wall, which will
almost certainly kill the passenger inside the
car. How do you program that decision? The sources
cite extensive surveys on this exact dilemma,
and the results are so revealing about human
psychology. They really are. Broadly, people
say they want autonomous cars programmed to minimize
overall harm. So, logically, sacrifice the one
passenger to save three pedestrians on the sidewalk.
It's pure utilitarianism. Unless the person taking
the survey is the hypothetical passenger in the
car, then suddenly they want the car programmed
to protect them at all costs. Naturally. But
I have to push back on that logic. If every single
car on the road is programmed by its manufacturer
to be entirely selfish, to prioritize its own
passengers at the expense of everyone else, doesn't
that make the roads vastly more chaotic and dangerous
overall? It's like an arms race of selfish algorithms.
This raises an important question. When an AI
ultimately does make the wrong choice and someone
gets hurt, who actually goes to jail? Yeah, who
is at fault? Is it the human occupant who was
legally eyes off and wasn't driving? Is it the
corporate car manufacturer? Or is it the software
developer who wrote the logic tree code five
years ago? That's a legal nightmare. Our entire
legal framework of liability is fundamentally
built around human error, human negligence, and
human intent. It is completely unprepared for
machine liability. Which brings up a question
for you listening right now. Would you put your
family in a car that is legally programmed to
sacrifice you in order to save three strangers
on the street? It's chilling to think about.
It really is. But beyond the ethics of the code,
the economic reality of this technology is also
hitting a massive wall. The sources note that
Waymo robotaxes currently operate at a staggering
loss. It cost them between $7 to $9 per mile
to run a robotaxi, compared to about $1 a mile
for a standard personal car. And we have to look
at why it's so expensive. It's not just the electricity
to run the car. It's the fact that those LiDAR
arrays cost thousands of dollars. It's the massive
data storage required. And crucially, it's the
remote human assistance centers. Whenever a robotaxi
gets confused by a construction zone, a human
sitting in a control room miles away has to digitally
step in and guide it. McKinsey estimates it's
going to take until 2035 at the earliest to scale
the technology enough to get those operating
costs under $2 a mile. And if they do manage
to get those costs down and scale up, the labor
displacement is going to be historic. The research
points out there are 2 .9 million driving jobs
in the US alone. Long -haul truck drivers, taxi
drivers, city bus operators, if those jobs are
automated away by fleets of AI, the economic
displacement would actually surpass the total
job losses of the 2008 Great Recession. We're
talking about restructuring the entire blue collar
labor force. Furthermore, it's not just a domestic
economic issue. What else is there? The sources
highlight objective reports of severe geopolitical
tensions, specifically between the United States
and China, over autonomous vehicle data. There
are very real concerns currently being debated
regarding imported self -driving tech. Right,
because if you think about how LIDAR works, bouncing
millions of lasers to create millimeter -accurate
3D maps of its surroundings, a self -driving
car is basically a roving internet -connected
supercomputer. Exactly. The geopolitical fear
is that imported economist vehicles could be
used to facilitate esk denige by constantly mapping
out critical infrastructure, power grids, and
military bases in foreign countries and then
just beaming that data back overseas. When a
car is no longer just a mechanical vehicle, but
a massive data harvesting node covered in high
definition sensors, it naturally elevates from
a transportation issue to a matter of national
security. Okay, we have covered an incredible
amount of ground today. We are clearly navigating
a very messy transitional era. We have cars on
the road right now that are statistically safer
than you in a heavy rainstorm, but might be dangerously
blind when the sun goes down. We have complex
systems branded and sold to consumers as full
self -driving that legally require you to babysit
the steering wheel. And we have brilliant engineering
that can predict complex traffic patterns, yet
still struggles with diverse pedestrian recognition
data and the basic legal frameworks of liability.
So what does this all mean? It means what we
always see in this show. Technology is moving
exponentially faster than human trust and infinitely
faster than our legal frameworks. We are building
the airplane while we're flying it, or I guess
programming the car while it's driving itself.
And essentially, we are all the beta testers.
That's a great way to put it. Before we go, I
want to leave you with one final thought to chew
on today. We've talked a lot about how self -driving
cars struggle to predict what erratic, unpredictable
human drivers are going to do next. Yeah, that's
the hardest part for them. Well, if these autonomous
vehicles ultimately prove to be drastically safer
than humans, but only when they can perfectly
communicate and predict the movements of other
robot cars on a closed network, how long will
it be until human driving is outright banned
on public highways? Oh, wow. Will governments
decide that human unpredictability is just too
dangerous to the machines? Are we rapidly approaching
a future where driving a car yourself is an illegal,
obsolete hobby? Just something to think about
next time you put your hands on the wheel. Thanks
for joining us on this deep dive and as always
we hope this keeps you the most well -informed
person in the room.