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Deep Learning Identifies Wet Road Hazards From Sound Input (thestack.com)
An anonymous reader writes: Researches have used recurrent neural network architecture to develop an audio-interpretation system that can understand how wet a road is, using techniques more commonly employed in speech recognition and music analysis. Every year 384,032 persons are injured and 4,789 persons killed through wet roads, and it's a problem that also threatens to hamper the usefulness of self-driving cars, which are likely to either become dangerous or prohibitively cautious in the absence of good information about the safety of road surfaces.
Don't know for sure, but elsewhere people would probably just notice that the road is wet after a rainfall and drive accordingly?
How many of those 384,032 people sued because no one put up a yellow "Caution! Wet Floor" sign?
bickerdyke
Not just that: a wet road after a mild shower doesn't have to be a problem in spring or fall, but the exact same shower in summer after a long dry spell can turn the road into a slippery slide, when the rubber and other crap that's been accumulating on the surface gets wet.
If construction was anything like programming, an incorrectly fitted lock would bring down the entire building...
Concrete an rough tarmac can be very predictable. Dirt, water, it just always grips. Smooth tarmac can easily get slippery even when dry.
The thing is drivers are more and more reduced to slow video game drivers. They just point the steering wheel. The net result is less accidents but the decrease in skill is dramatic. So is the decrease in attention i think. So the improvement in safety is partially cancelled because 'all other things being equal' does not apply.
It is from the oil on the road that floats when it rains after a long dry spell. It isn't nearly as big a problem as it used to be, I think in general new cars tend to be less leaky. You would be amazed at the amount of research that has gone into gaskets.
love is just extroverted narcissism