161–170 of 264 posts
Earlier quoted context omitted.
A one-way street is also a drivable flat surface. As is a road with no lane markings. Can you guess why blindly driving down each of these will be dangerous?
Right, but can you guess why stopping in the middle of traffic for a fake stop sign is bad? Walk before you run is the essence of the argument here.
Realising that a flat surface is drivable is probably 1% of 1% of what an autonomous car does on a road. Same goes for humans, I'd say.
Earlier quoted context omitted.
There are already billboards with pictures of stop signs, stop lights, etc on them. It doesn't take any criminal intent. Things that were once just billboards are suddenly hazards.
Seems reasonable enough to ban that. Might confuse a tired human too.
I think the concept of the line of death, from browser security UI design, is relevant here. The space outside of the highway right of way is completely uncontrolled and the Tesla should know better than to look at a billboard hundreds of feet up in the air for regulatory signs. The MUTCD has rules about where signs are to be placed--look only in those places. [1] https://textslashplain.com/2017/01/14/the-line-of-dea…
But it is the unexpected we need to train computer vision for to compete with humans. We can have rules for things like billboards, sure, but those will never be completely implemented everywhere.
The software just needs to be improved to identify something as billboard.
Earlier quoted context omitted.
> There's no reason computers can't be better than people It is entirely possible, however, that the computers and software systems available today can't be better than humans.
There are advantages and disadvantages to each. The computer failure methods are new to us, but we're all too familiar with human failings. Humans have much slower reaction times. Humans get sleepy, drunk, or distracted. Humans sometimes get angry or impatient and do dangerous things with their vehicles. The important metric is accidents per mile driven. Right now, Teslas on autopilot seem to be significantly safer t…
Weather, type of road and so on has huge effect on number of accidents. Unless those are taken in consideration it's only lies...
I'f you put a fake road sign somewhere people are probably going to obey it as well.
I think the concept of the line of death, from browser security UI design, is relevant here. The space outside of the highway right of way is completely uncontrolled and the Tesla should know better than to look at a billboard hundreds of feet up in the air for regulatory signs. The MUTCD has rules about where signs are to be placed--look only in those places. [1] https://textslashplain.com/2017/01/14/the-line-of-dea…
One would hope Tesla's system has some depth awareness. Honestly I'd be impressed how they could do so much if it didn't.
[0] https://www.wired.com/story/tesla-model-x-autopilot-phantom-...
I think the concept of the line of death, from browser security UI design, is relevant here. The space outside of the highway right of way is completely uncontrolled and the Tesla should know better than to look at a billboard hundreds of feet up in the air for regulatory signs. The MUTCD has rules about where signs are to be placed--look only in those places. [1] https://textslashplain.com/2017/01/14/the-line-of-dea…
The problem is that they don’t have lidar and therefore can’t reliably measure depth, right? A billboard far away high up in the air may appear in the same direction as a closer road sign if you don’t have depth perception. You should be able to infer it from a camera with the right model but those methods are more fallible. Similar root cause to that time the guy got beheaded by a white truck perpendicular to the hi…
The idea is that if you can take an image and predict the distances to the objects in it, you can then create an image that is a transpose of the original based on your predictions, but at a different distance. In case of Tesla moving forward, that would be a split second later, closer to the original image. As the car moves forward, you actually take that second image and compare the virtual to the original.
At this point you probably get it wrong, but you adjust your predictions and recalculate until your virtual transposed image looks exactly like the real image, at which point you've got the distances pretty darn close.
I remember watching a talk about their computer vision, and it was pretty impressive. The challenge is that despite knowing where the billboards are, they still look that way because sometimes, in obscure situations, in little old towns with fuzzy rules, road signs actually do appear in weird places, and that's where the car often trips up because without sufficient data it just can't build a prediction model to know when to respond, and when to suppress the new input.