This is lecture 2 of course 6.S094: Deep Learning for Self-Driving Cars (2018 version). This class is free and open to everyone. It is an introduction to the practice of deep learning through the applied theme of building a self-driving car.

OUTLINE:
0:00 - Intro
9:59 - Different approaches to autonomy
38:36 - Sensors
49:51 - Companies in the self-driving car space
58:18 - Opportunities for deep learning

INFO:
Slides: http://bit.ly/2HeCLkF
Website: https://selfdrivingcars.mit.edu
GitHub: https://github.com/lexfridman/mit-deep-learning

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LINKS:
Playlist: https://goo.gl/SLCb1y
Lecture 1: Deep Learning - https://youtu.be/-6INDaLcuJY
Lecture 2: Self-Driving Cars - https://youtu.be/_OCjqIgxwHw
Lecture 3: Deep Reinforcement Learning - https://youtu.be/MQ6pP65o7OM
Lecture 4: Computer Vision - https://youtu.be/CLOAswsxudo
Lecture 5: Deep Learning for Human Sensing - https://youtu.be/Z2GfE8pLyxc

Guest talk: Sacha Arnoud, Waymo - https://youtu.be/LSX3qdy0dFg
Guest talk: Emilio Frazolli, nuTonomy - https://youtu.be/dWSbItd0HEA
Guest talk: Sterling Anderson, Aurora - https://youtu.be/HKBhP9JISF0

2017:
Guest talk: Sertac Karaman, MIT - https://youtu.be/0fLSf3NO0-s
Guest talk: Chris Gerdes, Stanford - https://youtu.be/LDprUza7yT4

Welcome back to 6S094 Deep Learning for Self-Driving Cars Today we will talk about autonomous vehicles Also referred to as driverless cars, autonomous cars, robo cars.

Where for many autonomous vehicles have the opportunity to transform our society into a positive direction.

1.3 million people die every year in automobile crashes globally.

35, 38, 40,000 die every year in the United States.

So the 1 opportunity that's huge, that's 1 of the biggest focus for us here at MIT, for people who truly care about this, is to design autonomous systems, artificial intelligence systems that saves lives.

And those systems help work with, deal with, or take away what NHTSA calls the 4 D's of human folly.

Drunk, drugged, distracted and drowsy driving.

Autonomous vehicles have the ability to take away drunk driving.

So taking shared mobility to another level.

Eliminating car ownership from the business side is the opportunity to save people money and increase mobility and access.

Making vehicles, removing ownership makes vehicles more accessible because the cost of getting from point A to point B drops an order to magnitude And the insertion of software and intelligence into vehicles makes those vehicles, makes the idea of transportation, makes the way we see moving from A to point B, a totally different experience.

Much like with our smartphone, it makes it a personalized, efficient, and reliable experience.

Now for the negative view, for the dystopian view.

Any technology throughout its history, throughout our history of human civilization, has always created fear that jobs that rely on the prior technology will be lost.

This is a huge fear, especially in trucking.

Because so many people in the United States and across the world rely, work in the transportation industry, transportation sector, and the possibility that AI will remove those jobs has potential catastrophic consequences.

The Idea, 1 that we have to struggle with in the 21st century Of the role of intelligence systems that aren't human beings Being further and further integrated into our lives is the idea that a failure of an autonomous vehicle, even if they're much rarer, even if they're much safer, that there is a possibility for an AI algorithm designed by probably 1 of the engineers in this room will kill a person, Where that person would not have died if they were in control of the vehicle.

The idea of an intelligence system, 1 in direct interaction with a human being, killing that human being, is 1 that we have to struggle with on a philosophical, ethical and technological level.

Artificial intelligence systems, in popular culture, less so in engineering concerns, may not be grounded, ethically grounded.

At this time, much of the focus of building these systems as we'll talk about today and throughout this course, the focus is on the technology.

But of course, decades out, years or decades out, the ethical concern starts arising.

For Rodney Brooks, 1 of the seminal people from MIT, Those ethical concerns will not be an issue for another several decades, at least 5 decades.

It continues the thought, the idea of what is the role of AI in our society.

When that car gets to make a decision about human life, what is it making that decision based on?

Especially when it's a black box, what is the ethical grounding of that system?

A car that's not even artificial intelligence based, a car that's software based as they're becoming more and more.

Millions, most of the cars on road today are run by millions of lines of source code.

The idea that those lines of source code, written again by some of the engineers in this room, get to decide the life of a human being, means then a hacker from outside of the car can manipulate that code to also decide the fate of that human being.

For us, from the engineering perspective, the truth is somewhere in the middle.

We want to find what is the best positive way we can build these systems to transform our society, to improve the quality of life of everyone amongst us.

But there's a grain of salt to the hype of autonomous vehicles.

We have to remember, as we discussed in the previous lecture and it will come up again and again, Our intuition about what is difficult and what is easy for deep learning, for autonomous systems is flawed.

If we use our, if you use ourselves as an example, human beings are extremely good at driving.

Our intuition has to be grounded in understanding of what is the source of data, what is the annotation, and what is the approach, what is the algorithm.

So you have to be careful about using our intuition, extending it decades out and making predictions, whether it's towards the utopian or the dystopian view.

And as we'll talk about some of the advancements of companies working in the space today.

You have to take what people say in the media, what the companies say, some of the speakers that will be speaking at this class say about their plans for the future and their current capabilities.

I think a guide I can provide is when there's a promise of a future technology, future vehicles that are 2 years out or more.

That has to be, that's a very doubtful prediction.

1 that is within a year, as we'll give a few examples today, is skeptical.

The real proof comes in actual testing on public roads or in the most impressive, the most amazing, the reality of it is when it's available to consumer purchase.

I would like to use Rodney Brooks as a, so it doesn't come from my mouth, but I happen to agree.

His prediction is no earlier than 2032, a driver's taxi service in a major US city will provide arbitrary pickup and drop-off locations fully autonomously.

And by 2045, It will do so in multiple cities across the United States So think about that That a lot of the engineers working in the space A lot of folks who are actually building these systems Agree with this idea And that is the earliest I believe this will happen and Rodney believes But As all technophobes have been wrong who could be wrong?

This is a map on the x-axis a plot on the x-axis of time throughout the 20th century, and the adoption rate on the y-axis from 0 to 100% of the various technologies, from electricity to cars to radio to telephone and so on.

And as we get closer to today, the technology adoption rate, when it goes from 0 to 100%, the number of years it takes to adopt that technology is getting shorter and shorter and shorter.

As a society, we're better at throwing away the technology of old and accepting the technology of new.

So if a brilliant idea to solve some of the problems we're discussing comes along, it could change everything, overnight.

So let's talk about different approaches to autonomy.

We'll talk about companies, players in this space.

And then we'll talk about AI and the actual algorithms and how they can help solve some of the problems of autonomous vehicles.

Here's a useful taxonomization of levels of autonomy, useful for initial discussion, for legal discussion, and for policymaking, and for blog posts and media reports.

But it's not useful, I would argue for design and engineering of the underlying intelligence and the system viewed from a holistic perspective, the entire thing, creating an experience that's safe and enjoyable.

This is presented by SAE report J3016, the most widely accepted taxonomization of autonomy.

No automation at level 0, level 1 and level 2 is increasing levels automation.

Level 1 is cruise control, level 2 is adaptive cruise control lane keeping.

There's a lot of people that will explain that level 3 is conditional automation, meaning it's constrained to certain geographic location.

I will explain that from an engineering perspective, I'm personally a little bit confused of where that stands.

I'll try to redefine how we should view automation.

Level 4 and level 5 is high, full level automation.

Level 4 is when the vehicle can drive itself fully, for part of the time.

There's certain areas in which you can take care of everything no matter what no human interaction, input, safekeeping is required.

Level 5 automation is the car does everything.

I would argue that those levels aren't useful for designing systems that actually work in the real world.

But first a starting point that every system to some degree involves a human.

It starts with manual control from a human.

Human getting in the car, and a human electing to do something.

What we're talking about when the human engages the system, when the system is first available and the human chooses to turn it on, that's when we have 2 AI systems.

Human-centered autonomy, when the human is needed, is involved, and full autonomy, when AI is fully responsible for everything.

From the legal perspective, that means A2 full autonomy means the car, the designer of the AI system is liable, is responsible.

And for the human-centered autonomy, the human is responsible.

For human-centered autonomy, And we'll discuss examples of all of these when a human interaction is necessary.

The question then becomes is how often is the system available?

So for traffic bumper to bumper, is it available on the highway?

Is it sensor based like in the Tesla vehicle, meaning based on the visual characteristics of the scene, the vehicle is confident enough to be able to control, to make control decisions, perception control decisions.

The other factor, not discussed enough, and I think poorly, imprecisely discussed when it is, is the number of seconds given to the driver, not guaranteed, but provided as a sort of feature to the driver to take over.

In the Tesla vehicle, in all vehicles on the road today, that time is 0.

There is some, there's some room, sometimes it's hundreds of milliseconds, sometimes it's multiple seconds, but really there's no standard of how many seconds you get to say, wake up, take control.

Then tele-operation, something that some of the companies will mention or playing with, is when a human being is involved remotely, controlling the vehicle remotely.

So being able to take over control of the vehicle when you're not able to control it.

So support by a human that's not inside the car.

That's a very interesting idea to explore.

But for the human-centered autonomy side, all of those features are not required, they're not guaranteed.

The human driver, the human inside the car is always responsible.

At the end of the day, they must pay attention to a degree that's required to take over when the system fails.

And no matter under this consideration, under this level of autonomy, the system will fail at some point.

That is the collaboration between human and robot, is the system will fail and the human has to catch it when it does.

And then full autonomy is AI is fully responsible.

Now that doesn't, Again, as we'll present some companies in the marketing material and the PR side of things, they might present that there is significant degrees of autonomy.

If you're talking about L3 or L4 or L5, you have to read between the lines.

You're not allowed to have teleoperation.

If a human is remotely operating the vehicle, a human is still in the loop.

It's still a human-centered autonomy system.

You don't get the 10-second rule, which is just because you give the driver 10 seconds to take control, that somehow removes liability for you.

If you say that, that's it, as an AI system, I can't take, can't resolve, can't deal, can't control the vehicle in this situation and you have 10 seconds to take over that's not good enough.

The driver might be sleeping, That driver may have had a heart attack They're not able to control the vehicle Full autonomous systems must find safe harbor They must get you full stop From point A to point B That point B might be your desired destination or might be a safe parking lot but it has to bring you to a safe location.

This is a clear definition of the 2 systems.

And the human of course, as far as our certain current conception of artificial intelligence and cars today, is a human always overrides the AI system.

So we should, in the general case, the human gets to choose to take control.

The AI can't take control of the human except when danger is imminent.

Meaning sudden crashes like in AAB events.

We're not yet ready for the AI systems to say, as a society, to say, no, no, no, You're drunk, you can't drive.

So beyond the traditional levels, from level 0 to level 5, the starting point is level 0, no automation.

Level 1, level 2 and level 3, I would argue fall into human-centered autonomy systems, A1.

Because they involve some degree of a human.

Then L4, L5, to some degree, there's some crossover, fall into full autonomy.

Even though with L4, with Waymo, as you can ask on Friday, and anyone cruise Uber playing in this space, there's very often a human driver involved.

1 of the huge accomplishments of Waymo over the past month, incredible accomplishment, where in Phoenix, Arizona, they drove without, the car drove without a driver.

Meaning there was no safety driver to catch.

There was no engineer staff member there to catch the car.

A human being that doesn't work for Google or Waymo got into that car and got from A to point B without a safety driver.

And that particular trip was a fully autonomous trip.

So full autonomy A2 system is when you do nothing but ride along.

Human-centered autonomy system is when you have some control.

So the 2 paths for autonomous systems, A1 and A2.

In blue on the left is A1, human-centered, on the right is A2, full autonomy.

And then blue is from the artificial intelligence perspective is easy, easier, and then red is harder.

Easier meaning we do not have to achieve 100% accuracy.

Harder means everything that's off of 100% accuracy No matter how small Has a potential of costing human lives And huge amounts of money for companies So let's discuss we'll discuss later in the lecture About the algorithms behind each of these methods On the left and the right But this summarizes the 2 approaches The localization mapping for the car to determine where it's located.

For the human centered autonomy, it's easy.

It has to localize itself within the lane.

It has to find all the neighboring pedestrians and the vehicles in order to be able to control the vehicle to some degree.

But because a human is there, it doesn't have to do so perfectly.

When it fails, a human is there to catch it.

Scene understanding, perceiving everything in the environment from the camera, whether it's LiDAR, radar, ultrasonic.

The planning of the vehicle, whether it's just staying within the lane or for adaptive cruise control, controlling the longitudinal movement of the vehicle, or it's changing lanes as the Tesla autopilot, or higher degrees of automation.

All of those movement playing decisions can be made autonomously when the human is there to catch.

It's easier because you're allowed to be wrong, rarely but wrong.

The hard part is getting the human-robot interaction piece right.

As we'll discuss about how deep learning can be used to interact.

First perceive everything about the driver and second to interact with the driver.

That part is hard, because you can't screw up on that part.

You have to make sure you help the driver know where your flaws are so they can take over.

If the driver is not paying attention, you have to bring their attention back to the road, back to the interaction.

You have to get that piece right because for a flawed system, 1 that's rarely flawed, the rarity is the challenge in fact, has to get the interaction right.

The autonomous vehicle, a fully autonomous vehicle must communicate extremely well with the external world, with the pedestrians, the jaywalkers, the humans in this world, the cyclists.

That communication piece, 1 at least that is part of a safe and enjoyable driving experience is extremely difficult.

On a way more vehicle, I wish them luck if they come to Boston.

I'm getting from point A to point B because pedestrians will take advantage.

A vehicle must assert itself in order to be able to navigate Boston streets.

For a Tesla vehicle, for a human-centered autonomy vehicle L2, L3, the way you deal with Boston pedestrians is you take over, roll down the window, yell something and then speed up.

Getting the piece for an artificial intelligence system to actually be able to accomplish something like that, as we'll discuss on the ethics side and the engineering side, is extremely difficult.

That said, most of the literature in the human factors field, in the autonomous vehicle field, anyone that studied autonomy in aviation and in vehicles is extremely skeptical about the human centered approach.

It's deeply irresponsible because, as argued, because human beings, when you give them a technology which will take control part of the time, they will get lazy, they will take advantage of that technology, they will over trust that technology, they'll assume it'll work perfectly always.

This is the idea that this, this idea extended beyond further and further means that the better the system gets, the better the car gets at driving itself, the more the humans will sit back and be completely distracted.

It will not be able to re-engage themselves in order to safely catch when the system fails.

This is Chris Urmson, the founder of the Google Self-Driving Cars program.

And now the co-founder of 1, the other co-founder is the speaker of this class on next Friday, Sterling Anderson of a company called Aurora, a startup.

He was 1 of the big proponents, or the, I should say, opponents of the idea that human-centered autonomy could work, they tried it.

Publicly spoken about the fact that Google, in the early self-driving car program, they've tried shared autonomy.

They've tried L2 and it failed because their engineers, the people driving their vehicles fell asleep.

And we'll talk about why that may not be true.

There's a fascinating truth in the way human beings can interact with artificial intelligence systems that may work in this case.

As I mentioned, it's the human-robot interaction.

Building that deep connection between human and machine of understanding, of communication.

It's fun, but it's also representative of what society believes happens when automation is allowed to enter the human experience and driving where human life is at stake.

That you can become completely disengaged.

It's kind of a natural thing to think, but the question is, does this actually happen?

The amazing thing that people don't often talk about is that there is hundreds of thousands of vehicles on the road today equipped with autopilot, Tesla autopilot that have a significant degree of autonomy that's data, that's information So we can answer the question what actually happens.

So many of the people behind this team have instrumented 25 vehicles, 21 of which are Tesla Autopilot vehicles.

Now with over collected recording everything about the driver.

2 cameras on the, 1 camera on the external roadway and collecting everything about the car, including audio, the state, the pulling everything from the cam bus, the kinematics of the vehicle, IMU, GPS All of that information now over 300,000 miles over 5 billion video frames all as we'll talk about analyzed with computer vision You extract from that video of the driver Of everything they're doing The level of distraction The allocation of attention The drowsiness, emotional states The hands-on wheel, hands-off wheel body pose, activity, smartphone usage All of these factors All of these things that you would think would fall apart When you start letting autonomy into your life We'll talk about what the initial reality is that should be inspiring and thought-provoking.

As I said, 3 cameras, single board computer recording all the data, over a thousand machines in Holyoke in distributed computation running the deep learning algorithms that I've mentioned on these 5 plus billion video frames.

Going from the raw data to the actionable useful information.

The slides are up online if you'd like to look through them.

And this is the video of 1 of thousands of trips we have in autopilot in our data.

A car driving autonomously a large fraction of the time on highways from here to California, from here to Chicago, to Florida and all across the United States.

We take that data and using the supervised learning algorithms, semi-supervised, the number of frames here is huge.

For those that work in computer vision, 5000000000 frames is several orders of magnitude larger than any data set that people are working with in computer vision.

So we want to use that data for understanding the behavior of what people are actually doing in the cars and we want to train the algorithms that do perception and control.

A quick summary, over 300,000 miles, 25 vehicles, the colors are true to the actual colors of the vehicles.

A little fun fact, Tesla, Model X, Model S, and now Model 3.

500,000, 500 plus, sorry, miles a day and growing.

Now most days in 2018 are over a thousand miles a day.

In red is manual driving across the Boston area.

This is giving you the sense of just the scope of this data.

This is a huge number of miles with automated driving.

Several orders of magnitude larger than what Waymo is doing, that what Cruze is doing, and what Uber is doing.

The miles driven in this data with autopilot confirming what Elon Musk has stated is 33% of miles are driven autonomously.

This is a remarkable number for those of you who drive, and for those of you who are familiar with these technologies.

That is remarkable adoption rate, That 33% of the miles are driven in autopilot.

That means these drivers are getting use out of the system.

It's also incredible because under the decades of literature From aviation to automation and vehicles To Chris Urmson and Waymo The belief is such high numbers are likely to lead to crashes, to fatalities, to at the very least, highly responsible behavior.

Drivers over trusting the systems and getting in trouble.

We can run the glance classification algorithms.

Again, this is for next Wednesday discussion The actual algorithm It's the algorithm that tells you the region That the driver is looking at And it's comparing road, instrument cluster, left, Rear view, center stack and right Does the allocation of glance change with autopilot or with manual driving?

It does not appear to in any significant noticeable way.

Meaning you don't start playing chess, You don't start, you don't get in the backseat to sleep.

You don't start texting in your smartphone while watching a movie, at least in this data set.

There's promise here for the human-centered approach.

The observation to summarize this particular data Is that people are using it a lot The percentage of miles, the percentage of hours Is incredibly high At least relative to what would be expected From these systems And given that, there's no crashes there's no near crashes in autopilot the road type is mostly highway traveling at high speeds the mental engagement looked at 8,000 transfer of control from machine to human.

So human beings taking control of the vehicle, saying, you know what, I'm going to take control now.

I'm not comfortable with the situation for whatever reason.

Either not comfortable or electing to do something that the vehicle is not able to like turn off the highway, make a right or left turn, stop for a stop sign, these kinds of things.

Physical engagement, as I said, glance remains the same.

It's just something that I'd like to really emphasize As we talk about autonomous vehicles in this class And the guest speakers who are all on the other side So I'm representing the human center side all our speakers are focused on the full autonomy side.

Because that's the side roboticists know how to solve.

That's the fascinating algorithm nerd side.

It's just my belief stands that solving the perception control problem is extremely difficult and 2 3 decades away.

So in the meantime we have to utilize the human robot interaction to actually bring these AI systems onto the road to successfully operate.

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MIT Self-Driving Cars (2018)