How Autonomous cars are learning to pave the way?

Autonomous vehicles can follow the General rules of the road by recognizing traffic signs and road marking, marking of pedestrian crossings and other well-known features of the regulation of traffic. But what to do outside of well-marked roads, izezzhena up and down? On many roads outside the cities were a bit worn out paint, the signs are overgrown with ivy and trees, unusual intersections that are not marked on maps.

What to do following the rules of the Autonomous vehicle when the rules are unclear or do not exist? That should do his passengers when they discover that their car can’t deliver them where they’re going?

Warning the hidden

Most of the problems in the development of advanced technologies include the processing of rare or unusual situations or events that require performance beyond conventional capabilities of the system. It definitely works in the case of Autonomous vehicles. Some travel examples may include navigation through the repair area, meeting or horse buggies, or meeting with graffiti resembling a brake light. Outside of the road features include absolutely all manifestations of the natural world, like trees, blocked road, floods and big puddles — or even animals blocking the path.

In the Centre of advanced automotive systems at Mississippi state University researchers took on the task of training algorithms is to respond to the circumstances that almost never occur, which are difficult to predict and difficult to recreate. They tried to put Autonomous vehicles in the most complex scenario: drove car in the area that he had never seen before and did not know, without any reliable infrastructure like road paint and road signs in an unknown environment, where with equal probability you can meet the cactus and the polar bear.

In the process they combined the technology of virtual and real worlds. They have created an advanced simulation of realistic scenes outdoors, which was coached by artificial intelligence algorithms to read the camera stream and classifying what he saw: trees, sky, public way, any possible obstacles. Then they translated these algorithms on a specially designed test four-wheel drive vehicle and sent it on a dedicated test area, where he then reviewed the algorithms that collect data.

Let’s start with virtual

Engineers have developed a simulator that can create a wide range of realistic outdoor scenes, through which can move the transport. The system generates a variety of landscapes with different climates, forests and deserts, shows how plants, shrubs and trees grow over time. It can also simulate weather changes, solar and lunar light, as well as the exact position of 9000 stars.

In addition, the system simulates sensor readings, commonly used in Autonomous vehicles, such as lidar and cameras. These virtual sensors collect data that is then fed to the neural network as a valuable data for learning.

Will build a test track

Simulation is only as good as well they reflect the real world. The Mississippi state University acquired 50 acres of land, which scientists are developing a test track for driverless vehicles. The plot is suitable — it encountered tilts at an angle of 60 degrees and a lot of different plants.

Engineers have identified some of the natural features of the land, which they expect will be particularly difficult to cope self-driving cars, and reproduced them accurately on the simulator. This allowed them to directly compare the simulation results with the real attempts of navigating this earth. Eventually they will create similar real and virtual pairs of other types of landscapes, to improve the capabilities of the cars.

The collection of additional data

Also created test vehicles — Halo Project — with electric motor and sensors with computers that can navigate through a variety of off-road environments. Car Halo Project is equipped with additional sensors to collect detailed data about their context; they help to build a virtual environment to run the new tests.

Two lidar sensor, for example, is fixed under the cross-corners on the front of the car so that their beams scanning the approaching land. Together they can provide information about how rough or smooth the surface, and to read the data on the grass and other plants and objects on the road.

In General, research scientists gave some interesting results. For example, they have shown promising hints that the machine learning algorithms that are trained in simulated environments can be useful in the real world. As in the case with most research on the Autonomous vehicle, there is still a long way to go. Perhaps together they will help to make self-driving vehicles are not only more functional on modern roads, but the more popular and common method of transportation.

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