Ask HN: Can machine learning solve autonomous driving?

I remember the enthusiasm about self driving cars a few years ago. Many car manufacturers where showing off self driving prototypes in well controlled environments. High-Tech giants were spending a lot of money in the technology. We thought it was going to be a revolution in our societies.

From a technical point of view we thought that we need to accumulate a lot of data and thanks to the magic machine learning, it will work out. We used lidars, cameras, or both.

Today the enthusiasm is gone, rumours say Apple canceled the project, Google hasn't released anything, Uber killed a pedestrian, PSA announced that the technology wasn't there, Renault forgot about it while his CEO was busy escaping countries, etc...

Then you have Tesla, with a insanely high stock price, that pretend to be close to full self driving thanks to machine learning. I rented a Tesla model 3 for the first time this weekend and it's insane. The Tesla AI is stupid and dangerous. The AI has issues with simple tasks such as managing the windshield wipers, but will also do dangerous thinks such as smashing the brakes once in a while.

I started to reflect about the limitations of machine learning because of one "phantom breaking" incident I experienced. I was driving on a motorway and I was catching up a car pulling a boat on a trailer and we entered a tunnel. I guess the AI wasn't trained for that situation, seeing a boat in a tunnel in a Norwegian motorway, because it decided to do an emergency stop. A vision based but non machine learning approach would have detected the moving object in front of me, estimated the distance and assumed I was safe.

So, do you think we simply need more data for training and machine learning will stop doing stupid things? We need to accumule data for all kind of objects people can put on a trailer in all light conditions? Is this kind of problem something machine learning can resolve eventually, or is it a major problem that will stay forever?