Η Jennifer Healey is a PhD of computer science from MIT. Works on Intel Corporation Research Labs and researches devices and systems that will enable significant innovations. In a lecture on "If cars were talking, accidents could probably be avoided" he gave for TED Talks, showed us how he imagines a world without (traffic) accidents.
Η translation in Greek by Nikolao Benia and edited by Dimitri Katevati.
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Let's face it: Driving is dangerous. It's one of the things we don't want to think about, but the fact that religious pictures and charms are placed on boards around the world betrays the fact that we know this to be true. Traffic accidents are the leading cause of death among 16- to 19-year-olds in the US — the leading cause of death — and 75% of those accidents are not drug or alcohol related.
So what happens? No one can say for sure, but I remember my first crash. I was a new highway driver and in the lead car I saw the lights on the brakes. I think "Okay, all right, the guy slows down, I'll slow down too". I brake. But no, the guy did not slow down. The formula stopped, completely stopped completely on the highway. Did he go from 100 kilometers to 0? I hit the brake very hard. I felt the ABS kicking and the car was still moving, and it was not going to stop, and I know it will not stop, and the airbag opens, the car is broken, and fortunately, no one was injured. But I had no idea that that car would stop and I think we can do something much better than that. I think we can turn the road experience by letting our cars talk to each other.
I want you to think about how the road experience is now. You're in the car. Close the door. You are in a glass bubble. You can not directly feel the world around you. You are within an extension of yourself. You must navigate it on partially visible highways, within and between metal giants, at superhuman speeds. Correctly; And the only ones that guide you are your two eyes. Right, these are the only ones you have, eyes that are really not designed for this purpose, but people ask you to do things like when you want to change the lane, what is the first thing that they ask you to do? Take your eyes off the road. Correctly. Stop looking at where you go, turn around, look at your blind spot and continue driving without seeing where you are going. You and all the others. This is the safe way of driving. Why are we doing this? Because we have to choose, see here or see there? What's most important? And we usually do an amazing job choosing and choosing what to watch out on the road. But, occasionally something will get away. Occasionally we feel something either wrong or too late. In countless accidents, the driver says, "I did not see it coming." And I believe that. I believe that. So we can be careful.
But now there is the technology that can help us improve this. In the future, with cars exchanging data between them, we can see not only three cars in front and three cars back, right and left, all at the same time, panoramic view. we can see inside these cars. We can see the speed of the car ahead, see how fast someone goes or stops. If someone stops completely, I'll know.
And with calculations, algorithms and forecasting models, we will be able to see the future. You may think this is impossible. How to predict the future? This is something very difficult. To be precise, no. For cars, it is not impossible. Cars are three-dimensional objects that have a specific position and speed. They move on a road. Pre-arranged itineraries often follow. It really is not that difficult to make reasonable predictions about what a car will be in the near future. Even if when you are in a car and a motorcyclist comes - boom! - at 140 km per hour, alternating lanes - I know you have had this experience - this guy did not "come from nowhere". This guy has probably been on the road for the last half hour. (Laughter) Right? I mean, someone has seen him. 15, 30, 50 kilometers before, someone has seen him and as soon as a car sees him and places him on the map, he is on the map - location, speed, good estimate that he will continue to go at 140 km per hour. You will know, because your car will know, because the other car whispered something in his ear, such as, "By the way, five minutes, motorcyclist, watch out." You can make reasonable predictions about how cars behave. I mean they are Newtonian objects. That's the good thing about them.
So how do we get there? We can start with something as simple as sharing our location data between cars, just GPS sharing. If I have a GPS and a camera in my car, I have a pretty accurate idea of where I am and how fast I'm going. With machine vision, I can estimate where the cars around me are, roughly, and where they're going. Same with the other cars. They can have an accurate idea of where they are and a vague idea of where the other cars are. What happens when two cars share this data, if they talk to each other? I can tell you exactly what's going on. Both models are improving. Everyone wins. Professor Bob Wang and his team have done computer simulations of what happens when fuzzy estimates are combined, even in light congestion, when cars simply share GPS data, and we've carried this research except of computer simulations on robot testbeds that have the real sensors that are now in cars, in those robots: stereo cameras, GPS and 2D laser rangefinders, which are common in backup systems. We also adapted a discreet short-range wireless and the robots talk to each other. When these robots approach each other, they accurately track each other's position and can avoid each other.
Now we're adding even more bots to the space and we've tackled a few problems. One of the problems, when you receive a lot of chatter, it is difficult to process all the packets, so you have to prioritize and this is where the predictive model helps. If the robot cars follow the predicted paths, you don't pay much attention to these packages. You prioritize what seems to be going a little off course. This guy can cause a problem. And you can predict a new course. So not only do you know it's going to go off course, you know how. And you know which drivers to warn to pull over.
And we wanted to do — what's the best way to notify everybody? How can cars whisper "Should you pull over?" It depends on two things: first, the ability of the car and second, the ability of the driver. If someone has a really nice car, but they're on the phone or, you know, doing something, they're probably not in the best position to react to a sudden situation. So we started a separate investigation by modeling the driver's condition. Now, using a three-camera array, we can detect whether the driver is looking ahead, looking away, looking down, on the phone or drinking coffee. We can predict the accident and we can predict who, which cars, are in the best position to move off course to calculate the safest route for everyone. Basically, these technologies exist today.
I believe that the biggest problem we face is our willingness to share our data. I think it's a very worrying concept, the idea that our cars will be watching us, talking to us on other cars, gossiping us all the time. But I think it can be done in such a way as to protect our privacy, just as now, when I see your car outdoors I really do not know about you. If I look at your traffic signs, I do not know who you are. I think our cars will talk about us behind our back.
(Laughs)
And I think it will be something very good. I want you to think for a moment if you really do not want the abstract teenager behind you to know that you are braking, that you intend to stop. With the voluntary sharing of our data, we can do the best for everyone.
So leave your car gossiping you. It will make roads safer.
Thank you.
