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  • Translator: Joseph Geni Reviewer: Morton Bast

  • Let's face it:

  • Driving is dangerous.

  • It's one of the things that we don't like to think about,

  • but the fact that religious icons and good luck charms

  • show up on dashboards around the world

  • betrays the fact that we know this to be true.

  • Car accidents are the leading cause of death

  • in people ages 16 to 19 in the United States --

  • leading cause of death --

  • and 75 percent of these accidents have nothing to do

  • with drugs or alcohol.

  • So what happens?

  • No one can say for sure, but I remember my first accident.

  • I was a young driver out on the highway,

  • and the car in front of me, I saw the brake lights go on.

  • I'm like, "Okay, all right, this guy is slowing down,

  • I'll slow down too."

  • I step on the brake.

  • But no, this guy isn't slowing down.

  • This guy is stopping, dead stop, dead stop on the highway.

  • It was just going 65 -- to zero?

  • I slammed on the brakes.

  • I felt the ABS kick in, and the car is still going,

  • and it's not going to stop, and I know it's not going to stop,

  • and the air bag deploys, the car is totaled,

  • and fortunately, no one was hurt.

  • But I had no idea that car was stopping,

  • and I think we can do a lot better than that.

  • I think we can transform the driving experience

  • by letting our cars talk to each other.

  • I just want you to think a little bit

  • about what the experience of driving is like now.

  • Get into your car. Close the door. You're in a glass bubble.

  • You can't really directly sense the world around you.

  • You're in this extended body.

  • You're tasked with navigating it down

  • partially-seen roadways,

  • in and amongst other metal giants, at super-human speeds.

  • Okay? And all you have to guide you are your two eyes.

  • Okay, so that's all you have,

  • eyes that weren't really designed for this task,

  • but then people ask you to do things like,

  • you want to make a lane change,

  • what's the first thing they ask you do?

  • Take your eyes off the road. That's right.

  • Stop looking where you're going, turn,

  • check your blind spot,

  • and drive down the road without looking where you're going.

  • You and everyone else. This is the safe way to drive.

  • Why do we do this? Because we have to,

  • we have to make a choice, do I look here or do I look here?

  • What's more important?

  • And usually we do a fantastic job

  • picking and choosing what we attend to on the road.

  • But occasionally we miss something.

  • Occasionally we sense something wrong or too late.

  • In countless accidents, the driver says,

  • "I didn't see it coming."

  • And I believe that. I believe that.

  • We can only watch so much.

  • But the technology exists now that can help us improve that.

  • In the future, with cars exchanging data with each other,

  • we will be able to see not just three cars ahead

  • and three cars behind, to the right and left,

  • all at the same time, bird's eye view,

  • we will actually be able to see into those cars.

  • We will be able to see the velocity of the car in front of us,

  • to see how fast that guy's going or stopping.

  • If that guy's going down to zero, I'll know.

  • And with computation and algorithms and predictive models,

  • we will be able to see the future.

  • You may think that's impossible.

  • How can you predict the future? That's really hard.

  • Actually, no. With cars, it's not impossible.

  • Cars are three-dimensional objects

  • that have a fixed position and velocity.

  • They travel down roads.

  • Often they travel on pre-published routes.

  • It's really not that hard to make reasonable predictions

  • about where a car's going to be in the near future.

  • Even if, when you're in your car

  • and some motorcyclist comes -- bshoom! --

  • 85 miles an hour down, lane-splitting --

  • I know you've had this experience --

  • that guy didn't "just come out of nowhere."

  • That guy's been on the road probably for the last half hour.

  • (Laughter)

  • Right? I mean, somebody's seen him.

  • Ten, 20, 30 miles back, someone's seen that guy,

  • and as soon as one car sees that guy

  • and puts him on the map, he's on the map --

  • position, velocity,

  • good estimate he'll continue going 85 miles an hour.

  • You'll know, because your car will know, because

  • that other car will have whispered something in his ear,

  • like, "By the way, five minutes,

  • motorcyclist, watch out."

  • You can make reasonable predictions about how cars behave.

  • I mean, they're Newtonian objects.

  • That's very nice about them.

  • So how do we get there?

  • We can start with something as simple

  • as sharing our position data between cars,

  • just sharing GPS.

  • If I have a GPS and a camera in my car,

  • I have a pretty precise idea of where I am

  • and how fast I'm going.

  • With computer vision, I can estimate where

  • the cars around me are, sort of, and where they're going.

  • And same with the other cars.

  • They can have a precise idea of where they are,

  • and sort of a vague idea of where the other cars are.

  • What happens if two cars share that data,

  • if they talk to each other?

  • I can tell you exactly what happens.

  • Both models improve.

  • Everybody wins.

  • Professor Bob Wang and his team

  • have done computer simulations of what happens

  • when fuzzy estimates combine, even in light traffic,

  • when cars just share GPS data,

  • and we've moved this research out of the computer simulation

  • and into robot test beds that have the actual sensors

  • that are in cars now on these robots:

  • stereo cameras, GPS,

  • and the two-dimensional laser range finders

  • that are common in backup systems.

  • We also attach a discrete short-range communication radio,

  • and the robots talk to each other.

  • When these robots come at each other,

  • they track each other's position precisely,

  • and they can avoid each other.

  • We're now adding more and more robots into the mix,

  • and we encountered some problems.

  • One of the problems, when you get too much chatter,

  • it's hard to process all the packets, so you have to prioritize,

  • and that's where the predictive model helps you.

  • If your robot cars are all tracking the predicted trajectories,

  • you don't pay as much attention to those packets.

  • You prioritize the one guy

  • who seems to be going a little off course.

  • That guy could be a problem.

  • And you can predict the new trajectory.

  • So you don't only know that he's going off course, you know how.

  • And you know which drivers you need to alert to get out of the way.

  • And we wanted to do -- how can we best alert everyone?

  • How can these cars whisper, "You need to get out of the way?"

  • Well, it depends on two things:

  • one, the ability of the car,

  • and second the ability of the driver.

  • If one guy has a really great car,

  • but they're on their phone or, you know, doing something,

  • they're not probably in the best position

  • to react in an emergency.

  • So we started a separate line of research

  • doing driver state modeling.

  • And now, using a series of three cameras,

  • we can detect if a driver is looking forward,

  • looking away, looking down, on the phone,

  • or having a cup of coffee.

  • We can predict the accident

  • and we can predict who, which cars,

  • are in the best position to move out of the way

  • to calculate the safest route for everyone.

  • Fundamentally, these technologies exist today.

  • I think the biggest problem that we face

  • is our own willingness to share our data.

  • I think it's a very disconcerting notion,

  • this idea that our cars will be watching us,

  • talking about us to other cars,

  • that we'll be going down the road in a sea of gossip.

  • But I believe it can be done in a way that protects our privacy,

  • just like right now, when I look at your car from the outside,

  • I don't really know about you.

  • If I look at your license plate number,

  • I don't really know who you are.

  • I believe our cars can talk about us behind our backs.

  • (Laughter)

  • And I think it's going to be a great thing.

  • I want you to consider for a moment

  • if you really don't want the distracted teenager behind you

  • to know that you're braking,

  • that you're coming to a dead stop.

  • By sharing our data willingly,

  • we can do what's best for everyone.

  • So let your car gossip about you.

  • It's going to make the roads a lot safer.

  • Thank you.

  • (Applause)

Translator: Joseph Geni Reviewer: Morton Bast

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A2 US TED driver gps position data predict

【TED】Jennifer Healey: If cars could talk, accidents might be avoidable (Jennifer Healey: If cars could talk, accidents might be avoidable)

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