[tjpaudio]

To anyone with a detailed understanding of the current limits of machine learning, this should be no surprise; unsupervised learning is far from solved and ML in its current state will always be plagued by the cat and mouse game of edge cases. The reality is the industry has decided to go this way anyways because it has a good profit outlook if you can get it to work, which is the only thing funding the endeavor. Consider two ways of going about automated transportation:

1) AI. The car independently makes decisions and drives itself. 2) Networks. The car communicates with a grid to make decisions.

Why did we go for #1? Well, thats easy, capitalism. Consider:

AI: - Company gets to own the intellectual property to form a temporary monopoly. - Easier to sidestep governments involvement. - No need to build large infrastructure

Networks: - Shared, less opportunity for monopoly formation. - Will need the government to cooperate. Governments are slows.

If I had to guess, we will eventually go the network route. The research used for AI will drive safety features and failsafes, but not the meet of it. Anyways, why accelerate a line of stopped cars one at a time with autonomous vehicles when you could accelerate the entire line of cars simultaneously with a networked setup?

[streetcat1]

So I do not think that this is an unsupervised problem. As a matter of fact, this should be one problem where labeled data is not an issue.

I.e. just let humans ride the self-driving car, and record the human actions as well as all the sensors (at time t-1).

Combined that with all the humans on road X, and you have the a model for road X.

The problem here is that each road needs its own model as well as model per time of day.

Another problem is that each company will not share its supervised data.

[pasttense01]

And what happens if the network breaks down? Everything gridlocked. Conversely if one vehicle breaks down the rest of the cars just go on by.

[wilg]

I think #1 is easier for lots of reasons.