Tesla auto-pilot keeps confusing moon with traffic light then slowing down

Pure cost cutting at expense of safety certainly.

Which of the above would solve this problem?

It's entirely cost.

Using all the senses would not solve the problem. Vision is the only one with enough resolution to matter.

That said: - way cheaper - more reliable - less power hungry - simpler and easier to debug.

I think one of the main reasons for Tesla to use only vision is that then the mistakes the system makes will be more understandable and thus acceptable to humans. The way radar and lidar systems fail and cause wrecks will seem strange to humans, like running into stationary objects. Even if using these sensors would make system better overall, they might not be worth including if one is shooting for human acceptance of the system as fit for use.

An autocar seeing something that looks like a red light and breaking (or breaking for a bit and then continuing after deciding it was not a red light) is going to be acceptable to people because that is how they behave. Phantom breaking due to some radar or lidar input that the human can't perceive is going to be interpreted very negatively. I imaging auditory might be added at some point, if it is not already being used.

Three reasons:

1) They have hundreds of millions of cars sold already with cameras that they can up-sell this to. That means more revenue. But more importantly, more training data. Massive amounts of it. Training data is the real value here. Radar/lidar/etc. might be able to detect better what is obvious to a human just looking at a thing. But given enough training data, a machine learning can probably replicate that capability so you don't actually need the fancy sensors. Adding new sensors to the mix would set them back quite a while on that front.

2) Simplicity. More sensors means more complexity integrating all the signals and gathering the right training data. More failure modes, etc. It probably also means more compute power needed to process all that data. More complex testing, etc. Scaling by keeping the sensor platform simple is a good move here.

3) The hard part of autonomous driving is actually interpreting visual signals in complex or unusual/rare situations. Roads are designed for humans with eyes. Lidar sees a blob, radar detects a pole, a camera sees a traffic sign, road markings that mean something, etc. It's a much richer signal. All the important stuff on roads is clearly visible. So, cameras are far more important for this than lidar/radar. Those are really great for avoiding crashing into things. Not so much for interpreting and classifying those things. And Tesla seems to be doing pretty OK with not crashing into things. Mostly, the amusing edge cases have to do with misinterpreting visual signals for which radar and lidar are probably not that relevant.

It's an interesting approach that they clearly believe that they can make work. It does not actually stop them from later adding more hardware to enhance things if they decide those things are needed. But it's quite interesting how far they are getting with just cameras.

But in foggy or dark situations, a yellow light can indeed appear as a floating yellow circle.

> 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.

You can learn about their systems by watching talks by Andrej Karpathy. As a robotics engineer interested in vision, their architecture is inspiring. This talk [1] is a good overview but each talk he gives is a little different so search for more if you want to know as much as possible.

But the big thing is that their autonomy computer can be programmed to look for odd scenarios and send them back home. Tesla uses their fleet of hundreds of thousands of cars to collect edge cases like this, and then they have a kind of compartmentalized neural network system that breaks apart disparate tasks. With their collected examples they can create unit tests to ensure that the moon stops activating the stoplight detector. Once trained, the unit tests presumably help ensure they don't end up with future regressions.

So basically every time you see a Tesla do a weird thing, there is a good chance it will stop doing it soon enough. At least if it's hitting hacker news.

[1] https://www.youtube.com/watch?v=hx7BXih7zx8

I've definitely driven through places where there is only one light. Blinks red for stop (no stop sign). Many times, for cross traffic you'll have the one light but blinking yellow as a warning at the intersection.

Karpathy's latest talk says they have 6000 tests cases(video clips) that each new version of the model has to predict the right answer on, for it to be released.

There was another video of a Tesla being confused by a truck carrying traffic lights on the truck bed. I do like the idea of what Waymo does, which is to run their model through a virtual world to see how it performs. It's amusing to imagine each of these edge cases being added in as they are encountered.

> 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.

Permanent yield signals are often only one flashing yellow light. Crosswalk signals are often only flashing yellow lights when active. Temporary construction barrier signals are often only yellow flashing lights. Fire station signals often have only red and yellow lights, no green. Even when all three lights are present, they may also be oriented horizontally, or in triangular shapes.

Which isn't to say there isn't more context to learn from, but just about the only true unifying trait among all these indicators that you should perhaps pay attention and slow down is a bit of yellow light. It need not even be circular: yellow arrows are far from uncommon, including "straight ahead" yellow arrows for intersections where turns are forbidden.

If that would be a traffic light in front of the car, it would get bigger as a car moves towards it. Basic perspective knowledge. But it seems the system is not considering it or not putting enough weight on it.

An even harder case: https://futurism.com/the-byte/tesla-autopilot-bamboozled-tru...

If your head was in a vice and your eyeballs were superglued into the straight-ahead position you might have more trouble.

My three-year-old has mistaken a streetlight seen through her bedroom window for the moon more than once, but then I don’t let her drive either.

EDIT: Though even that isn't a traffic light, and not the other way around.

You also are making posts on the internet describing your abilities, you seem to have a much more advanced intelligence that might be hard for a computer in a car to replicate.

lidar isn’t able to read the state of traffic lights as far as I know, and I believe lidar based systems detect the location of traffic lights from maps

I wonder if the concern is that LiDAR looks "ugly" or nerdy on the front of the car. And a bit more aero drag.

Yeah, then they could never ever ship like every other Lidar company

And what if the moon is not visible but there's a traffic light where the moon should be?

Yeah, this is what I do when I drive. Otherwise I think it's a traffic light and I panic.

Less than average.

> The moon appears white/grey.

Not all the time. I've seen reddish/orangeish moons, especially if it's something near the horizon near sunset or sunrise. It's also reddish during lunar eclipses.