I'm sure this has been explained before, but there are so many long threads that I thought I'd just straight up ask for a clear explanation.
My understanding is that when you file a bug on the non-beta fsd Tesla, Tesla never reviews this information, and thus, the car's ability to navigate on Autopilot doesn't improve. It's never been clear to me why Tesla would incorporate a bug filing feature but not use that function to improve the system. Given that there are an infinite number of situations a car can be thrust into, how do they decide what fixes to make with each iteration of Autopilot? For example, I've seen at least a dozen updates since I've owned my model 3, but it still can't go down a windy road without a driver taking over. I've filed many bugs and told Tesla tech support, but nothing changed.
Now I'm seeing the FSD beta videos, and it appears people are filing bug reports and Tesla is putting out very quick updates, fixing very big issues in a matter of days. Even with a very small number of beta testers, there must be thousands of driver bug reports coming in. How do they decide what to fix, and how are they doing it so quickly?
Is the car learning that every time a driver has to take over, that it must've made an error? Is it self correcting so that the next time a driver goes to the same spot it doesn't repeat the mistake?
Sorry if this is a stupid question, but it just seems like Tesla was ignoring basic issues like going down curvy roads, and yet now they're able to take and implement feedback immediately. People keep mentioning the neural network, but I don't understand how that neural network works (I assume it's doing what I mentioned above - using driver engagement as a sign that its decision was faulty.)
And then I've seen mentions of Dojo. What is Dojo and what does it have to do with the car learning to drive better?
Can anyone give a simple explanation?
My understanding is that when you file a bug on the non-beta fsd Tesla, Tesla never reviews this information, and thus, the car's ability to navigate on Autopilot doesn't improve. It's never been clear to me why Tesla would incorporate a bug filing feature but not use that function to improve the system. Given that there are an infinite number of situations a car can be thrust into, how do they decide what fixes to make with each iteration of Autopilot? For example, I've seen at least a dozen updates since I've owned my model 3, but it still can't go down a windy road without a driver taking over. I've filed many bugs and told Tesla tech support, but nothing changed.
Now I'm seeing the FSD beta videos, and it appears people are filing bug reports and Tesla is putting out very quick updates, fixing very big issues in a matter of days. Even with a very small number of beta testers, there must be thousands of driver bug reports coming in. How do they decide what to fix, and how are they doing it so quickly?
Is the car learning that every time a driver has to take over, that it must've made an error? Is it self correcting so that the next time a driver goes to the same spot it doesn't repeat the mistake?
Sorry if this is a stupid question, but it just seems like Tesla was ignoring basic issues like going down curvy roads, and yet now they're able to take and implement feedback immediately. People keep mentioning the neural network, but I don't understand how that neural network works (I assume it's doing what I mentioned above - using driver engagement as a sign that its decision was faulty.)
And then I've seen mentions of Dojo. What is Dojo and what does it have to do with the car learning to drive better?
Can anyone give a simple explanation?