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How will the development of machine learning help the emergence of aerodynamics in car designs?

Whenever engineers needed to test the aerodynamic properties of the new car or a plane, the method ordinarily took hours or even a day. Engineers have now essentially accelerated this procedure, making streamlines and parameters accessible continuously. Their technique is among the first to utilize machine learning model to display stream around constantly editable 3D objects.
The possibilities of machine learning are very wide, and they can be used in a variety of areas, including in the automotive industry.

It is in this direction that the Wayve start-up works, which with the help of machine learning  technologies decided to build a car with completely autonomous control.

How will the methods of machine learning help in creating car designs?

The modern approach to the automotive industry involves the use of a large number of sensors, radars or lidars in vehicles. However, this “stuffing” does not save the car from an accident: news about a collision of cars with other objects on the roads cause damage to the image of producers and hinder further development. This is largely due to the fact that previously tested car designs , a large number of resources spent on determining their current location. For this they loaded maps, analyzed the terrain.

The British company argue that machine learning, its capabilities and potential allow you to create a machine on completely different principles. Wayve decided to create, with the help of machine learning, a software package for vehicle control that would function on the same principles as the living person sitting behind the wheel. Of course, it will not be possible to do without radar in the new development, but the main emphasis will not be on calculating the current location of transport, but on finding people and other objects in close proximity and their correct identification.

What are the advantages of using machine learning  technologies in car design?

Compared with machines that are literally “stuffed” with different equipment, a similar tool based on machine learning  is distinguished by several advantages:

  1. cost reduction;
  2. increased security;
  3. increased fault tolerance of equipment;
  4. reduction of hardware resources.

Further development of machine learning will contribute to the replenishment of this list. However, in order to realize these advantages, the start-up will require significant financial investments in developing the software package and setting up serial production.

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