Is this a random thought or something that has seen more debate? Are there articles or papers comparing this approach to the other?
However one big problem with deploying systems using this approach today is that it happens to be very hard to do this with enough nines that your car isn't slowing down or emergency braking for spurious inputs. Floating plastic bag / leaf - trash on the road - asphalt line that looks wrong - camera glare - a puddle with a reflection - giant sticker of a photo of a traffic cone - etc. etc. etc. State of the art would probably be undriveably cautious today.
Most things that don't look like road, between lane markers on a highway, are still drivable space. Probably 99%.
That doesn't mean it's the right approach. Just that the alternative isn't really deployable today.
This should immediately preclude any approach to self driving cars that has pedestrians, bags of trash, debris, cross traffic, etc. Highway (and probably daytime) only. I don't understand the obsession with starting with corner cases and working back to the very real good that self-driving autonomy can do on highways at speed. Build back with lessons learned, rather than solving the whole problem in one shot. That seems like fundamental engineering, but the marketing team is in charge, not the engineers.
The cliché example is when people tried to build a tank classifier. Since they took the pictures of the different tank types on different days, the AI learned to detect the weather conditions instead.
So to determine if your AI is detecting the roads absence, you would first need a lot of testing images that look realistic in general, but with absent road. As far as I know, such a dataset does not publicly exist yet.
So yes, that commenters approach seems new to me.
To understand how their AI reached decisions, Müller and his team developed an inspection program known as Layerwise Relevance Propagation, or LRP. It can take an AI’s decision and work backwards through the program’s neural network to reveal how a decision was made.
In a simple test, Müller’s team used LRP to work out how two top-performing AIs recognised horses in a vast library of images used by computer vision scientists. While one AI focused rightly on the animal’s features, the other based its decision wholly on a bunch of pixels at the bottom left corner of each horse image. The pixels turned out to contain a copyright tag for the horse pictures. The AI worked perfectly for entirely spurious reasons. “This is why opening the black box is important,” says Müller. “We have to make sure we get the right answers for the right reasons.”
https://www.theguardian.com/science/2017/nov/05/computer-say...
There is, in general, a great deal of work on explaining the decisions of neural net. Explainable AI is a thing, with much funding and research activity and there's books and papers etc, e.g. https://link.springer.com/book/10.1007/978-3-030-28954-6.
And all this is becaue, quite regardless of whether that tank story is real or not, figuring out what a neural network has actually learned is very, very difficult.
One might even say that it is completely, er, irrelevant, whether the tank story really happened or not, because it certainly captures the reality of working with neural networks very precisely.
Incidentally, (human) kids should never be allowed to hug sheep or goats like that. They can easily catch something nasty (enterotoxic E. coli, mostly). See e.g.:
https://www.bbc.co.uk/news/uk-england-lancashire-35039878
Juliette Martin, of Clitheroe, took her daughter Annabelle, 7, to the 'Lambing Live' event at Easter last year.
The youngster, who had bottle-fed a lamb, suffered kidney failure and needed three operations, three blood transfusions and 11 days of dialysis.
This is probably the reason why this story has been repeated so many times (and with so many variations): beause it rings true to anyone who has ever trained a neural net, or interacted with a neural net for any significant amount of time. Unfortunately, the article you cite chooses to suggest otherwise.
In any case, if the tank story is an urban legend it has its roots firmly in reality.
I'm happy to accept my mistake if I have misunderstood Plyphon_'s comment.