Does anyone have details on what this means? To me this sounds like neural nets are now part of the low level control loops for the throttle/steering as well - but that doesn't seem particularly beneficial. Especially if you're interested in control stability/predictability.
Up until now the driving part had been hand coded in C++, but now it is a neural net that has learnt what would a human do in similar road situation (not sure what they trained it on). Is this an improvement? Who knows - should certainly be different. This doesn't mean the car suddenly got smart and now understands what it is looking at - it just means it's using a different set of rules for when to switch lanes, brake, etc.
As humans, we have this issue already. We call them "optical illusions." Optical illusions are really brain neural net failures where there are competing narratives between evaluations of the information provided by our senses we conclude invalid results. We don't have the ability to process absolute light values, so we infer colorspace by relative color within our 3D mental model, which can result in inferring a difference in color where there is none.
1. https://spectrum.ieee.org/slight-street-sign-modifications-c... 2. https://en.wikipedia.org/wiki/Checker_shadow_illusion
I find the argument that you can allow both longitudinal and lateral control to be expressed by a black box difficult to accept. The proof of the driving is in the handling.
There’s a video of an engineer at comma.ai mocking self-driving teams for semantically labeling objects on the road in SAE Level 2 and beyond systems.
Which screams stupid to me. Why would you be proud that your system has no idea what a pedestrian is. Or what a traffic cone or a speed limit sign are? Idiotic.
At least Tesla still semantically labels. But I argue that you can’t get smooth driving out of neural network controls because they inherently have no idea what smooth driving explicitly is. They estimate it.
The best they can do is attempt to mock it, which without programmer-defined rails WILL result in excessively high jerk and acceleration numbers, as proven by openpilot’s implementation.
Good but erratic lateral control at worst results in hugging a lane line.
Anything less than perfect longitudinal control means the driver will toggle it on and off during portions of traffic where it won’t follow the flow of traffic closely enough, or if it follows to closely, or if you’re experiencing a high cabin jerk factor.
You cannot screw up longitudinal control. It’s nearly the whole experience.