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Comment by naiyt | Hacker News Reader

I remember a few of these difficulties being mentioned in Sebastian Thrun's Self-Driving Cars course on Udacity. It's been awhile, but I seem to remember the snow discussion in particular. The problem wasn't just slippery conditions, but the question of how the vehicle can localize itself when the various visual cues that it uses are covered in snow (e.g. road lines).

Pretty interesting stuff.

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I think there are better potential solutions for a self driving car to orient itself in those kinds of conditions than for a person. I've driven in miserable weather, and it is very disconcerting - you just guess and go. A self driving car could take measurements of the road + gps coords + look its map database to know how many lanes there should be, then estimated where the lanes and limit lines are. Once my eyes fail me, I don't have a fallback.

That, and you have to consider that one of the chief issues this article brings up - that the self driving cars are relying on unreliable maps - is that once a self driving car traverses a road, it could propagate a much more detailed, or at least much more auto-appropriate map to the swarm of other cars.

The maps would get magnitudes better the year the cars were unleashed.

People have trouble with snow as well. If there are no tracks in front of you, you just guess where you're supposed to go and go for it. It's pretty fun on roads you know well, rather miserable on roads you don't.

Also, most 3-lane roads magically turn into 2-lane roads, and many 2-lane roads turn into 3-lane roads etc.

You just described the problem nicely. The "magic" that turns 3 lane roads into 2 lanes etc, is a situational awareness that is really, really difficult to impart on a learning system. The big problem is that probabilistic models don't have a notion of "common sense" solution to an odd situation. They need to have seen the situation, or something very similar to it, enough to make a reasonable calculation of what to do.

Isn't that last sentence describing common sense?

Not quite. I would define common sense as "a reasonable fallback solution given that the current situation is unfamiliar." This is something AI systems have a LOT of difficulty with, the self-driving car being no exception.

The 3 lane to 2 lane problem is solved already. These cars follow a centimeter-accurate 3D map that has the lanes precisely defined (as well as acceptable speeds, location of stoplights, etc).

The Google car knows the lane change is approaching long before it shows up on any sensor.

This isn't about a lane change approaching. This is about people disregarding the concept of lanes when there's fresh snow on the ground because they have no idea where the lanes are.

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