Indeed. Why do Tesla go for ML to poorly solve problems long solved by well understood PID controllers?

The cynical in me feels this is to do with employees at Tesla want to work with new and shiny ML instead of old boring PID tech for the sake of "resume driven engineering".

I suspect these decisions are driven from the top down. We know who loves a shiny tech choice at Tesla. The company has been quite proud of their end to end ML system in presentations. But it sounds like they’re struggling with execution.

My brother in law works as an engineer for Tesla Palo Alto: according to him these features are all driven from the very top. Always at the expense of fixing bugs, or getting even basic things working reliably in ways that would make driving safer and more enjoyable. Make your own conclusions about buying one. I would steer clear, literally, knowing some of the bugs in there.

This tracks with everything we’re seeing at Twitter now. A passion for high level feature ideas and an apparent disinterest in confounding issues or technical complications.

As a robotics engineer for a long time I thought they were ahead of the curve. But I am beginning to see that their big bet isn’t paying off, and I’ve taken more of an interest in going back and listening to those people who said it wasn’t going to work.

Where is the evidence it doesn't use any PID? The wobbling described in the parent is a very PID like behavior (poorly tuned)

I guess everyone is giving Tesla engineers the benefit of the doubt that if they are using a PID they would be competent enough perform a simple proven task for a PID that other car companies have no problem with. I refuse to believe the Tesla engineers are incompetent. So they must be forced into using a "full black box ML" paradigm. There may be a completely different issue that none of us are aware of (faulty sensor, poor mix of both, etc). It is blatant that Tesla vehicles are poor at parking compared to other electric vehicle offerings from BMW, Audi, and Ford.