Jennifer Healey If cars were talking, accidents could be avoided

Η Jennifer Healey is a PhD of computer science from WITH. 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.

The translation into Greek was done by Nikolao Benia and the editing by Dimitri Katevati.
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Let’s face it: Driving is dangerous. It's one of the things we do not want to think about, but the fact that religious images and charms are posted on billboards around the world betrays the fact that we know this to be true. Road accidents are the leading cause of death for people aged 16 to 19 in the United States - the leading cause of death - and 75% of these accidents are not related to drugs or alcohol.

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.

How do we get there? We can start with something as simple as sharing our location data between cars, just sharing GPS. If I have a GPS and a camera in my car, I have a fairly accurate idea of ​​where I am and how fast I go. With mechanical vision, I can appreciate my surrounding cars, about, and where they are going. The same as the other cars. They can have a precise idea of ​​where it is and a vague idea of ​​what other cars are. What happens when two cars share this data if they talk to each other? I can tell you exactly what is happening. Both models are improving. Everybody wins. Professor Bob Wang and his team have computer simulations of what happens when unclear estimates are combined, even in low congestion, when cars simply share GPS data, and we have transferred this research off computer simulations to robot test beds that have real sensors that are now in the cars on these robots: stereo cameras, GPS and two-dimensional laser telephones, which are common to backup systems. We also adapted a short-range radio signal and the robots speak to each other. When these robots approach each other, they accurately monitor one another's position and can avoid each other.

Now we add more robots to the site and we have some problems. One of the problems, when you get a lot of chatter, it's hard to edit all the packages, so you have to set priorities and here the predictive model helps you. If robot cars follow the predicted paths, you do not pay much attention to these packages. You give priority to what seems to be going a little out of the way. This type can create a problem. And you can predict a new course. So, not only do you know it will go out of the way, you know how. And you know which guides you should warn to do aside.

And we wanted to do - what is the best way to alert everyone? How can cars whisper "Do you have to step aside?" It depends on two things: first, the ability of the car and second, the ability of the driver. If someone has a really good car but is using the phone or, you know, doing something, they are probably not in the best position to react in a sudden situation. So we started a separate research by modeling the driver's condition. Now, using a series of three cameras, we can detect if the driver is looking ahead, looking away, looking down, is 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.