vehicle icons jerking erratically on traffic display

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Joined
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#1
My Model 3 display was working fine but after Version 9 update, the vehicle icons surrounding my car icon on the left side in the traffic display are jerking around erratically. Often bumping into my car’s icon, and lurching over it at times.

Are other users having this issue? Any idea if this is in a future bug fix?
my thanks,
Moo
 

CaribbeanKing

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#2
Yep, same issue. I'm not even sure what would cause it either. I actually captured it on video once while I was stopped at a light. See attached. Very strange. I have to assume this is a known issue and will work itself out via updates.

Video:
 

twm01

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#4
They are doing the 'Tesla cha-cha', and when they bump your car, they are giving it the 'Tesla kiss'. I imagine they will fix it in short measure, but no harm to the self driving :)
 

Jayc

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#5
Does autopilot depend on thermal imaging at all? Just wondering whether the truck had an exhaust blasting hot air towards your AP cameras.
 

Rich M

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#7
I keep meaning to share a video clip I took the other day, when the Camry next to me seemed to be doing something far more intimate... :eek:
A couple times now I've had 'SUV icon' in the next lane directly on top of 'car icon' in the next lane humping away. :oops:
 

Trebonius

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#8
My theory, which I have no way of verifying, is that it is because the side and rear cameras haven't been used before, and their machine learning system doesn't have as much training data to consistently determine the precise location of those cars. They made a ton of changes to the neural net, so while it may be better in many respects, its ability to consistently determine a car's relative location visually is not necessarily better.

Actually, another thing just occurred to me. In front, a Tesla has stereo vision. It may be able to use that to more precisely determine vehicle location. On the sides and rear, it's one camera, so it has to guess whether it's a small car that's close, or a big car farther away.

I don't know anything about computer vision, so this is all probably way off base.
 

Bernard

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#9
My theory, which I have no way of verifying, is that it is because the side and rear cameras haven't been used before, and their machine learning system doesn't have as much training data to consistently determine the precise location of those cars. They made a ton of changes to the neural net, so while it may be better in many respects, its ability to consistently determine a car's relative location visually is not necessarily better.

Actually, another thing just occurred to me. In front, a Tesla has stereo vision. It may be able to use that to more precisely determine vehicle location. On the sides and rear, it's one camera, so it has to guess whether it's a small car that's close, or a big car farther away.

I don't know anything about computer vision, so this is all probably way off base.
Here is a possible partial explanation based on Tesla hardware:
Tesla has both cameras and proximity sensors, but the proximity sensors work only at very close distances. A single camera can easily estimate the distance from the size of the object (that's what we do ourselves for anything beyond 100ft, when stereo vision does not help much), but that means relying on object identification and I doubt that the Tesla system distinguishes subcompact, compact, intermediate, full-size, etc., so its estimates of distance may be too large for smaller cars and too small for larger cars.
So, for a bigger-than average or smaller-than-average car that is just at the limit of the proximity sensors' range, it could result in jumping back and forth, as the proximity sensors kick in and out. I noticed that a small Civic and a little Kia were jumping around a lot, but family sedans seemed more stable (except when they metamorphosed into SUVs ;-)
This could occur even if all cars were stopped, because of inherent noise in the readings of the proximity sensors and intrinsic uncertainty in the position of ICE vehicles. Indeed, I am curious whether this occurs as much when the cars around you are all BEVs -- ICE cars vibrate constantly, thus constantly changing their distances from a fixed observer (the change is tiny, but it may just be enough to push some of the neural net's nodes over/under their threshold), but BEV cars sit perfectly still.
 

TrevP

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#10
The new neural net in V9 is way more powerful but so far they haven’t put in any smoothing filters. What you’re seeing is the raw output from the vehicle detection bounding boxes.

Over time they should smooth out the display a bit so it won’t be as jerky.

If you haven’t seen the raw output of what the vision system sees here’s what I’m talking about so you understand

 
Last edited:

babula

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#11
Everyone on V9 has this so its just something thats going to improve with time, not a big deal.

IMO being able to see all the cars around you is superior to V8 + the better car view UI.
 

Rich M

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#12
IMO being able to see all the cars around you is superior to V8 + the better car view UI.
Yup! I don't care if the vehicles on the display are jittery or the wrong type of vehicle. So far, if it shows a vehicle in my blind spot, it's there. (Or to put it from a safety perspective, there's never been a vehicle in my blind spot that hasn't shown on the display)
 

Diamond.g

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#13
in the rain I have noticed the cars jump around more. A couple of times I also noticed that the car didn’t see a car coming up at speed in an adjacent lane while I was stopped.
 

Spiffywerks

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#14
Watching that video, makes you really appreciate how much data they have to program the car to interpret every millisecond. It’s a crazy amount of data for a computer. For humans, sure it doesn’t seem like much when you are looking about, but real time visual data like this for a computer to process is amazing!
 
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#19
Thx all for the reassurance, I was afraid it was just my car doing that. I have to admit seeing the truck next to me hump, and then eat the car in front of it, was quite entertaining.
 

Rick Steinwand

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#20
My experience is the truck to my right is pushing cars out into traffic, similar to throwing someone onto the path of an oncoming train.