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Self-driving cars definitely belong to the use cases in artificial intelligence, which have received the most media attention in recent years. This can probably be explained more by the idea of autonomous driving being more futuristic than by the actual consequences of the technology. Several machine learning problems are packed into it, but computer vision is an important core element in their solution. For example, the algorithm (the so-called “agent”) by which the car is controlled must be aware of the car’s environment at all times. The agent needs to know how the road goes, where other vehicles are in the vicinity, the distance to potential obstacles and objects, and how fast these objects are moving on the road to adapt to the changing environment continually. For this purpose, autonomous vehicles are equipped with extensive cameras that film their surroundings over a wide area. The resulting footage is then monitored in real-time by an image recognition algorithm. Similar to Customer Behavior Tracking, this requires that the algorithm can search for and classify relevant objects not only in static images but in a constant flow of images.

This technology already exists and is also used industrially. The problem in road traffic stems from its complexity, volatility, and the difficulty of training an algorithm so that even possible failure of the agent in complex exceptional situations can be excluded. This exposes Computer vision’s Achilles’ heel: the need for large amounts of training data, the generation of which is associated with high costs in road traffic.