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.
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.
Τι συμβαίνει λοιπόν; Κανείς δεν μπορεί να πει με σιγουριά, αλλά θυμάμαι το πρώτο μου accident. Ήμουν νέα οδηγός σε εθνική οδό και στο προπορευόμενο αυτοκίνητο είδα να ανάβουν τα φώτα των φρένων. Σκέφτομαι «Εντάξει, όλα καλά, ο τύπος επιβραδύνει, θα επιβραδύνω και εγώ». Πατάω φρένο. Αλλά όχι, ο τύπος δεν επιβράδυνε. Ο τύπος σταματούσε, τελείως, σταμάτησε τελείως στην εθνική οδό. Πήγε από τα 100 χιλιόμετρα στο 0; Πάτησα πολύ δυνατά το φρένο. Ένιωσα το ABS να κλωτσάει και το αυτοκίνητο ακόμα προχωρούσε, και δεν επρόκειτο να σταματήσει, και το ξέρω ότι δεν θα σταματήσει, και ανοίγει ο αερόσακος, το αυτοκίνητο διαλύθηκε, και ευτυχώς, κανένας δεν τραυματίστηκε. Αλλά δεν είχα ιδέα ότι εκείνο το αυτοκίνητο θα σταματούσε και πιστεύω ότι μπορούμε να κάνουμε κάτι πολύ καλύτερο από αυτό. Πιστεύω ότι μπορούμε να μετατρέψουμε την οδική εμπειρία αφήνοντας τα αυτοκίνητά μας να μιλούν μεταξύ τους.
I want you to think for a moment about what the road experience is like now. You get in the car. You close the door. You are in a glass bubble. You cannot directly sense the world around you. You are inside an extension of yourself. You have to navigate it on partially visible highways, in and between other metal behemoths, at superhuman speeds. Correctly; And the only things that guide you are your two eyes. Right, those are the only ones you have, eyes that aren't really designed for that purpose, but people ask you to do things, like when you want to change lanes, what's the first thing they ask you to do? Take your eyes off the road. Correctly. Stop looking where you're going, turn around, look in your blind spot and keep driving without looking where you're going. You and everyone else. This is the safe way driving. Why do we do this? Because we have to, we have to choose, do I see here or do I see there? What is more important? And we usually do an amazing job of picking and choosing what to look out for on the road. But, occasionally something will escape us. Occasionally we feel something either wrong or too late. In countless accidents, the driver says, "I didn't see that coming." And I believe that. I believe that. That much we can notice.
But now there is technology that can help us improve this. In the future, with cars swapping data between them, we will be able to see not only three cars in front and three cars behind, on the right and left, all at the same time, panoramic view. we will be able to see inside these cars. We will be able to see the speed of the car ahead, see how fast someone is going or if they are stopping. If someone stops completely, I'll know.
And with calculations, algorithms and predictive models, we will be able to see the future. You may think that this is impossible. How to predict the future? This is something very difficult. Actually, no. For cars, it's not impossible. Cars are XNUMXD objects which have a specific position and speed. They move on a road surface. They often follow predetermined routes. It's really not that hard to make reasonable predictions about where a car will be in the near future. Even if when you're in a car and a motorcyclist comes – boom! – at 140 km/h, switching lanes — I know you've had that experience — that guy didn't "come out of 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 once a car sees him and puts him on the map, he's on the map — position, speed, good estimate that he'll keep going at 140 km per hour. You'll know, because your car will know, because the other car 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 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'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 brown. 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.
