How do they work in winter then? You can't see much skin if someone is wearing a winter coat.
Right - self driving cars are a solution for Silicon Valley only only so they don't even bother testing those cars elsewhere.
The skin color is going to be the least of your issues in the winter time. How are the cars going to "see" the road under 10cm of snow? Granted humans shouldn't really be driving in those conditions either, but we do and mostly successfully, to avoid sleeping on the side of the road in -10C.
What is it that makes it so hard for all these algorithms to work on people with darker skin? This has been an issue for more than ten years, surely someone has started adding various skin colors into the training data. Is it a case of lack of training material, or is it just faster to focus on one skin type?
My employer makes a multi-band synthetic aperture radar that penetrate snow and is high-resolution enough to "see" painted road markings and reflectors beneath a layer of snow.
It is nowhere near small or cheap enough for self-driving car applications, but will be one day.
Another challenge is affordable real-time processing of the data. Churning through 3,200MB/s of phase-history data is expensive but again that will solve itself given time.
That is pretty neat, but what about the road with no markings? None of the road leading into the town where I live have road marking or reflectors or are you able to target the reflectors on posts by the side of the road as well? I mean see through the snow on those.
> Surely someone has started adding various skin colors into the training data.
What has to occur for this to happen:
* Someone has to take the time and effort to measure things, to identify that there is a problem.
* They have to get that message out so that it's heard.
* That message needs to:
* hit the public hard enough that people demand intervention from their elected representatives
* or, alert the company directly, and hope that the incentives align. (Will the company make more money by fixing this?)
There's plenty of easier alternatives:
* Call the problem too hard to solve
* Call it bad science
* Call it ragebait
* Call it woke
* Make up a bunch of equivalences and channel it into inertia:
* If people are wearing winter coats, then they won't show enough skin for the cars to be racist. And if the cars aren't racist in cold places, then it isn't a problem in warm places.
* People don't have radar/lidar either, and they're allowed to drive
Yup - as someone from a country where -10 degrees is considered a normal winter day I think that self driving cars should not rely on road markings at all. Even road signs can be unreadable after a particularly nasty blizzard.
I've yet to see self driving cars successfully navigating during bad winter conditions. They can't even avoid killing pedestrians in California.
> A team of researchers in the UK and China tested how well eight popular pedestrian detectors worked depending on a person's race, gender, and age.
- edit -
Sorry, I read the article too quickly and assumed it was talking about the countries UK and China. Perhaps they only bothered testing the cards in UK, Silicon Valley and China, Silicon Valley.
What is it that makes it so hard for all these algorithms to work on people with darker skin? This has been an issue for more than ten years, surely someone has started adding various skin colors into the training data. Is it a case of lack of training material, or is it just faster to focus on one skin type?