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Recognizing the signs is probably one of the simpler tasks, yes. But for example the StVO (traffic law: https://www.gesetze-im-internet.de/stvo_2013/BJNR036710013.h... ) in Germany says:

"Das Ende einer streckenbezogenen Geschwindigkeitsbeschränkung oder eines Überholverbots ist nicht gekennzeichnet, wenn das Verbot nur für eine kurze Strecke gilt und auf einem Zusatzzeichen die Länge des Verbots angegeben ist. Es ist auch nicht gekennzeichnet, wenn das Verbotszeichen zusammen mit einem Gefahrzeichen angebracht ist und sich aus der Örtlichkeit zweifelsfrei ergibt, von wo an die angezeigte Gefahr nicht mehr besteht. Sonst ist es gekennzeichnet durch die Zeichen 278 bis 282."

The translation of this is: If a speed limit is combined with another sign indicating a reason for the speed limit (e.g. construction work, dangerous curve, train crossing...) the speed limit automatically ends without any additional sign.

So basically the AI has to understand why the speed limit is there and decide when it has passed the dangerous part of the road.

When starting to drive the car has to recognize if it is inside the city (50km/h or 30km/h limit depending on location), outside the city (100 km/h) or on the autobahn (no limit). This is simply impossible without having a map. You could save the state from the previous drive. But what happens if the car gets towed from the autobahn into a city. Will drive through the city without any speed limit?

I don't think this is simple at all.




That sounds like an exception that could be easily addressed by adding an additional sign or perhaps in most cases making the rule hold for a maximum of 100 yards or so. Even detecting construction work is relatively easy: you have lots of cranes, people wearing helmets, cement mixers, scaffolding, traffic cones, yellow tape, etc. Easy to teach to a machine learning system.




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