One thing that has impressed me as I've read posts here, looked at Tesla (and other car makers') systems is just how much our brains do and how good they are at it without even 'thinking.' we instantly and subconsciously judge distance with both binocular vision and other depth cues. We keep track of a myriad of different objects and trajectories and plan our own, all while drinking our coffee and eating a burger!
good question - my assumption is it relies on binocular vision, measuring differences between two camera views and using that to triangulate. Radar and ultrasound are good for distance but Tesla has stated they are moving away from radar and ultrasound is really only good at short range (like parking sensors.)
I'm curious about this too.
binocular vision is likely used to measure parallax, however in recent years there have been lots of advancements in measuring distances (and stuff in general) from a single video output. lots of mathematical models have been developed that can, to a high degree of precision, measure distance using simple object reference analysis (sizes of objects in frames over time), among other methods. pretty sure google's measure app is based on some form of this lol. math is not my focus tho so please dont ask me to explain the nitty gritty lol but i'd expect Tesla, and all self driving car companies use this in some form.
Historically, PB has had much more of a problem on two lane roads and fewer problems on the multi-lane highways you describe so that may be part of the difference.I'm curious about this too.
After today's longer drive, I'm still not experiencing any PB. SalisburySam has a Model 3, and I have a Model Y. I doubt that makes a difference, but my experience on AP is FAR from useless.
I tried it on Highway 401 (12-lane highway within Toronto) with lots of traffic, and it handled fine. Lots of cars cut in front of me, and it gracefully slowed down, and sped up. Even handled all "clover" shape exits. Tried lane changes, and I had zero concerns on how it handled each time. But one time, the speed sign went from 100km/h to 50km/h instantly, and it did precisely that. Tesla needs to handle this better.
Don Valley is a curly 6-way highway with some aggressive drivers. No problem there either.
Also tried on 4-lane (very tight and busy) roads, that was ok too.
In all cases, my left hand was gently holding the steering, the arm weight was just enough to not bring up any warning.
For the heck of it, once I turned it off, and I manually focused hard to stay in the middle of the lane. My wife told me the AP is better than me!
Overall, it is now much more relaxing to drive. Even my wife told me that. I have to drive this route weekly, and I'm looking forward in using AP from now on.
That's why I drove almost 100km worth of 2-lane narrow winding roads few days ago, but that was pleasant as well. Too early to tell, but I'll keep observing.
keep in mind what you see in the visualization is not what the computer sees. that's a rendering for your benefit by the infotainment computer. it wouldn't show as a coherent image if they displayed the raw output, that translation leads to artifacts in the render like the car jumping around. a little speculation there but i really doubt the car thinks trucks are teleporting haha.
The other thing it doesn't really explain is all the PB events where there is nothing on the road at all. Semis in the oncoming lane are annoying and still a failure (especially compared to other systems on the road,) but at least you can prepare for them a they're somewhat understandable.
My point was just that between an algorithm introducing "randomness" in data interpretation and low-quality sensors introducing "false positives", I'll always lean to the latter. The visualization is just the algorithm translating the sensor input (raw data) into visual form for humans to see. Unless I get actual evidence to say otherwise, I wouldn't pin the jumpiness on the algorithm. Again, you may be better versed in this area than I am.
Very true. The semi example is just the most obvious one I can think of. One that's most easily explained. It's much harder to explain ghost objects or even overpasses, but it's not out of the realm of possibility that low-quality sensors can generate false positives out of nothing. I really should do some research on the camera specs myself so I'm not just pulling crap out of my behind, but given all of Tesla's constraints (age of company, investments, timelines, demands, etc.), it makes sense that the cameras may not be top-notch, i.e. relative to the need of a reliable FSD/AP.
i think classifying it as a failure because of that is a bit of an overstatement but at a minimum is a limitation / shortcoming of the system yes.
I think I'm just seeing now what you actually meant. I may be putting too much faith in the algorithm not being buggy. Something as complicated as interpreting and translating the raw sensor data is very unlikely to be bug-free, as with all software programs, so you're right the algorithm can play a major role in generating false positives. I was probably too idealistic to think the "rendering" process as simple and straightforward.
I had a case where my car virtually stopped in the carpool lane of I394 coming out of downtown Mpls. Turns out it was where the regular highway crosses under the carpool lanes. I think the car was using GPS and map data rather than sensor data. Issues like this won’t resolve until Tesla switches over from relying primarily on map data. Good news is it sounds like that will happen reasonably soon.
