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After 6 months of working fine, Tesla software update drives at barriers again

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Re: After 6 months of working fine, Tesla software update drives at barriers again

#31

Post by Scoundreller »

jakeogh wrote: post #28
Scoundreller wrote: post #20

That’s a very strange way to close a highway. Where are the blinkenlights? The words? The flashing arrows? The plastic jersey barriers? The buckets full of sand or water? The repainted lines directing you to the right? Can’t tell if this is temporary or long-term closure, but if it’s a multi month thing, I would expect more “Don’t you dare fork to the left” signalling. Not everyone is a local.

Why would any of that be relevant?

Because the same things that confuse humans can confuse computers that are largely trained with human data.

Re: After 6 months of working fine, Tesla software update drives at barriers again

#32

Post by inertiatic »

bsaul wrote: post #16

I wonder how do unit tests work with NN (or if they're even a relevant concept at all). You could replay some testing video frames and make sure the objects are correctly identified, but i suppose that's already what training is about... If an issue like that resurface, does it mean that the original frames leading to the 2018 accident aren't part of the training (or at least frames from someone driving in this kind…

Well, testing in the strict sense would be measuring accuracy on data not in the training set, and even accuracy on the training set data (which isn't guaranteed to be 100% I believe)

Re: After 6 months of working fine, Tesla software update drives at barriers again

#33

Post by MockObject »

bdcravens wrote: post #13
sabareesh wrote: post #12

Since ModelX incident I have made sure not to drive on first lane. I always drive on the 2nd lane and it is much better.

For a $700 car I might consider crazy compromises, but $70k+? Absolutely not.

Folks might be even more likely to compromise with a $70k car than a $700 one. The psychology of sunk costs!

https://www.lesswrong.com/posts/tyMdPwd8x2RygcheE/sunk-cost-...

Re: After 6 months of working fine, Tesla software update drives at barriers again

#34

Post by sgt101 »

Scoundreller wrote: post #31
jakeogh wrote: post #28

Earlier quoted context omitted.

Why would any of that be relevant?

Because the same things that confuse humans can confuse computers that are largely trained with human data.

Upsetting as it is, it also turns out that different things also confuse the computers. Sometimes unexpectedly and suddenly.

Re: After 6 months of working fine, Tesla software update drives at barriers again

#35

Post by JanSolo »

hemogloben wrote: post #11
dragontamer wrote: post #9

Earlier quoted context omitted.

I don't think neural networks are wired to "remember" things. In theory, they could be hooked up that way. But your typical convolutional neural network is looking at things frame-by-frame. In theory, ANNs could have an output layer that passes data from one frame to another frame to assist things. But there's no real programming to "hardcode" something like object permanence into an ANN. You pretty much throw a bunc…

NNs are just the first step in the pipeline. Their outputs (detected objects, segmentation, etc) will be piped into other software that builds higher level models. Considering the path-planning requirements I would be absolutely shocked if Autopilot wasn't build history models and estimated paths for objects around the vehicle (other cars etc).

Agreed; I imagine they use neural networks to detect and classify objects which are then saved into a scene-graph for use in pathing.

I expect what happened was that they trained their NNs for improved detection in one area but unknowingly reduced it in another. Perhaps now it can detect tricycles 99% but road barriers went down to only 30%. Having worked with NNs it's very common to see gains in one domain which come at a cost of reduced performance in another.

Re: After 6 months of working fine, Tesla software update drives at barriers again

#36

Post by mslev »

Watching the video, '2019.5.15 - Try 2' is interesting. You can see the car moving normally, then it starts to follow the black crack in the road and moves to the right- at this moment, the white Nissan in front is blocking the white lines ahead where the lanes actually split.

Does AP use other cars as reference points, or just the road? Ideally in this situation it would be both: "The line has disappeared, and there's a new one now, but that car went over it". Instead it seems to just be following whatever lines it can see. Does that make sense?

Note- not at all defending the AP behavior here. Just thinking out loud.

Re: After 6 months of working fine, Tesla software update drives at barriers again

#37

Post by gojomo »

warp_factor wrote: post #26
bdcravens wrote: post #13

Earlier quoted context omitted.

For a $700 car I might consider crazy compromises, but $70k+? Absolutely not.

That's what I find so interesting with Tesla owner. They spend a fortune on a car, then they minimize every single issue they have with the car. My explanation for this is that for a lot of owners, the car is a way to be part of a hyped group more than being a utilitarian object (what a car should be).

https://en.wikipedia.org/wiki/Choice-supportive_bias

Re: After 6 months of working fine, Tesla software update drives at barriers again

#38

Post by robrenaud »

dragontamer wrote: post #9
x38iq84n wrote: post #2

I have always wondered... Does the AP have some higher-level notion of object permanence, continuity (road behind a horizon or after a curve) and things like that? Does it track a pedestrian that is momentarily hidden behind an obstacle and will probably reemerge in a second or two on the other side? Does it expect that kids may run after that ball that just flew from behind a car? Does it continuously track and impr…

I don't think neural networks are wired to "remember" things. In theory, they could be hooked up that way. But your typical convolutional neural network is looking at things frame-by-frame. In theory, ANNs could have an output layer that passes data from one frame to another frame to assist things. But there's no real programming to "hardcode" something like object permanence into an ANN. You pretty much throw a bunc…

There are architectures that use CNN on image inputs, and LSTMs across frames of the video to keep memory.

https://arxiv.org/pdf/1609.06377.pdf

Re: After 6 months of working fine, Tesla software update drives at barriers again

#39

Post by [deleted] »

x38iq84n wrote: post #2

I have always wondered... Does the AP have some higher-level notion of object permanence, continuity (road behind a horizon or after a curve) and things like that? Does it track a pedestrian that is momentarily hidden behind an obstacle and will probably reemerge in a second or two on the other side? Does it expect that kids may run after that ball that just flew from behind a car? Does it continuously track and impr…

[deleted]
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