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I can't understand why instead of using all the possible senses: vision, auditory, radar, lidar they want to only use vision as if there's some sense of "purity" in doing that.
It's entirely cost.
Their automotive gross margin is 28% (Toyota is at 17.76%). If one automaker could afford LIDAR, it's Tesla.
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Unless it's hanging from a cable https://www.masterfile.com/image/en/700-08743685 . But then, it could be a streetlight so only vision can solve this problem https://www.123rf.com/photo_2753272_hanging-street-lamp.html
You need vision to determine it is or isn't a traffic light. Lidar would be able to tell you if the object should even be evaluated. A light hanging from a cable would still get a lidar ping back. The moon would be a void with no lidar ping.
Maybe is not acceptable (and blind in the rain/fog), so you must rely on vision to make the decision – which is why Tesla goes straight to the heart of the problem.
They haven't solved it yet, obviously, but neither has Waymo or anyone else. Tesla is probably collecting more images of 'moon or traffic light?' images than the rest of the industry combined already.
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Which of the above would solve this problem?
I'm not an expert, but I'm guessing Tesla's version of lidar wouldn't detect the moon. It's probably a bit out of range.
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> Tesla’s vision/ml systems are amazing More importantly, they're the only ones accessible to the average consumer. Waymo/Zoox/Daimler all have equally if not more impressive systems. A real issue with tesla is that they want to be vision-only, which is going to make getting to level 5 first almost impossible. BTW - knowing where the moon is happens to be an extremely solved problem.
> knowing where the moon is happens to be an extremely solved problem. It's true... but I don't think that's how Tesla would solve it. Their goal is to create a neural network "driver" which can drive in any place even if it has never seen it before. They'd rather teach their neural network that the moon and stoplights are not to be confused visually. Thought I suppose in searching for training examples they could us…
Isn't that impossible considering these networks need training and therefore have seen everything before?
I can't understand why instead of using all the possible senses: vision, auditory, radar, lidar they want to only use vision as if there's some sense of "purity" in doing that.
Which of the above would solve this problem?
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Lidar cannot identify the state of a traffic light but it can confirm and understand its physical position.
Lidar can't tell a streetlight from a traffic light, so this has to be resolved by vision anyway.
This is a problem with learning the appropriate context. In the case of a yellow traffic light, there should be a traffic light fixture and two other (unlit) lights. Without this context, it becomes apparent the yellow pixels in a circular shape (representation to the network) are not a traffic light at all. Tesla’s vision/ml systems are amazing. I would love to learn more about how unit testing for this type of erro…
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It's entirely cost.
Because Tesla lacks money? Their automotive gross margin is 28% (Toyota is at 17.76%). If one automaker could afford LIDAR, it's Tesla.