September 20, 2020

Labeling: the factor that should not fail in an autonomous car | Technology

Labeling: the factor that should not fail in an autonomous car | Technology


In 2018, autonomous driving accelerated the pace. In the United States, dozens of companies have launched a flood of test vehicles on the roads. One of the consequences has been the increase in accidents where these cars converge. Some as the outrage carried out by a vehicle of Uber or the shock of a Tesla in function Autopilot, have killed people.

Technology is in a process of improvement, to which these tests contribute. One of the aspects that have to be polished are the databases. And within these there is a factor of enormous importance. It deals with the classification or labeling of the images.

The labeling belongs to the technical insides of the autonomous driving systems. It can be compared with the cognitive capacity that every vehicle that wants to guide itself should have.

Of course, the labeling of the images is one of the keys of the vehicle that can not fail. The specialist in deep learning Lucas García, from the MathWorks company, which develops analytical software for autonomous cars, highlighted the importance of this factor in a conference during the Big Data Spain event. In conversation with EL PAÍS, this mathematician, who has also been a researcher at the Complutense University of Madrid, summarized the question: "A bad labeling of the data can result in an algorithm that is not able to solve problems correctly."

A sample of how an autonomous car perceives the objects in its environment.
A sample of how an autonomous car perceives the objects in its environment.

This coupled with a compromised situation on the road makes the vehicle more prone to an accident. If the cameras of an autonomous car are his eyes, the way he knows reality, the labeling of the database is his cognitive capacity. By comparing with the database the vehicle understands its environment.

For the car to make good decisions two basic circumstances are needed. "If we want to create an algorithm that detects pedestrians, cyclists and other vehicles very well, we must first have done a process of collecting data and signals provided by the sensors," Garcia points out, adding immediately: "And also we have to label them correctly. "

Labeling objects in an image is a finer job than it may seem. The classification will be transmitted to the algorithm of deep learning or neural network, which makes the decisions of the autonomous car. Therefore, the information has to be as accurate as possible. "One possibility of labeling is that in an image where a car appears we draw a rectangle above to identify that this is the car," says the mathematician "Another would be to say exactly which pixels of the image the car corresponds to".

The relationship is clear: the more precise the labeling, the better the algorithms that are nurtured from it. Like any system based on artificial intelligence, that of the autonomous car is probabilistic. "The analysis carried out by all these models of deep learning it's based on a probability, "he says." Even though the systems try to be very robust, if the model of machine learning It foresees that with a probability of 99.95% what is in front is a car, obviously there is a 0.05% chance that it is something else ".

All these artificial intelligence models have their failure rates. They are design errors that engineers obviously try to reduce. For this there is no other choice but to dedicate time and specialized people to do the labeling. There are solutions, like the ones García works on from MathWorks, to automate the process, but there must always be a human behind to validate the results.

Neural networks that use autonomous cars require millions of images tagged to work. It is an incredible job, with an inevitable manual component. And this only with respect to the images. But there are more sensors that complement the camera, such as radar, proximity detector or lidar. The latter much more expensive to label correctly.

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