It is always amazing to see how scenarios in science fiction films that seemed like distant dreams of the future just a few years ago are gradually becoming reality. The situation is similar with self-driving cars. Even if this technology is still far from mature, we are rapidly approaching a solution that will work well in the long term. Whether it's the Internet of Things (IOT), 3D printing, machine learning, blockchain or the ease of use of and accessibility to civilian drones that anyone can now buy at an electronics store - the list of industry 4.0 solutions in practice is quite long already. As artificial intelligence platforms for capturing real-world data also become increasingly relevant, the as-yet futuristic-looking, wholly independent vehicles could very soon be seen on the roads. The first test vehicles can already be found driving on the roads today.
Presently, a number of solutions that provide a certain level of vehicle automation are already available on the market. To be more precise, road users can actively implement driving assistance technologies that autonomously perform steering, accelerating, and braking functions as well as give momentary emergency warnings to drivers. However, those still require the driver’s constant engagement in the driving process. Fully driverless cars are a different story. The New York Times makes the following forecast on their further elaboration: “to put it simply, even though the clear perspective of encountering self-driving vehicles on the highways is present, there is a long way to go before reaching this high automation level.”
But how do self-driving vehicles actually work? It’s mainly about cameras, data acquisition, and computing power. Driverless cars require external data to train their autonomous driving systems. Information about the surrounding objects and obstacles on the roads is collected in the form of images via a camera. Subsequently, the acquired data is processed by a computer, which recognizes vehicles, classifies them and trains the system to do this technique automatically, based on the stored references of visual training datasets. Given that there are numerous objects for detection and further identification on the roads, sometimes it’s really challenging for developers to acquire corresponding data in large quantities.
Another feature, which plays a crucial role in the launch of self-driving cars, is their connectivity to city infrastructure. One of the recent research topics in that field is dedicated to the Vehicle-to-Infrastructure (V2I) technology, investigated by the U.S. Department of Transportation. The innovation is planned to be designed to obtain real-time data on the road situation and transmit acquired information to the connected vehicles, enabling them to predict traffic flow, generate better routes, and avoid accidents.
The idea of self-driving cars is still waiting for the long-awaited implementation, but ROADIA devices can already provide some functions required for further development. The company uses artificial intelligence solutions to recognize different road users, classify them and count traffic. Contact us today to gather more information about our services and to exchange ideas about specific implementation possibilities.
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