Volkswagen : When changing the environment or the sensor technology, neural networks in vehicles today have to be trained again and again from scratch. ...

This kind of learning gobbles up enormous resources. "Today, it takes 70,000 graphics processor hours to train an autopilot," explains Tobias Kalb, a doctoral student involved in the AI Delta Learning project for Porsche Engineering. In practice, numerous graphics processing units (GPUs) are used in parallel to train neural networks, but the effort is still considerable. In addition, a neural network needs annotated images, that is images from real traffic events in which important elements are marked, such as other vehicles, lane markings, or crash barriers. If a human performs this work manually, it takes an hour or more to annotate a snapshot from city traffic. Every pedestrian, every single zebra crossing, every construction site cone must be marked in the image. This labelling, as it is known, can be partially automated, but it requires large computing capacities.

In addition, a neural network sometimes forgets what it has learned when it has to adapt to a new domain. "It lacks a real memory," explains Kalb. He himself experienced this effect when using an AI module trained with US traffic scenes. It had seen many images of empty highways and vast horizons and could reliably identify the sky. When Kalb additionally trained the model with a German dataset, a problem arose. After the second run, the neural network had trouble identifying the sky in the American images. In the German images, it was it was often cloudy or buildings blocked the view.