[nowarninglabel]

I don't really buy the crux of the argument much, which basically boils down to the idea that the car will have trouble with unmapped objects. I would be pretty surprised if all these cars didn't come equipped with an ability to phone home and update the central repository of maps with newfound objects. Thus, when encountering a new object the car could slow down, devote some processing power to mapping it, and then cars traveling through the area in the future should get the latest update downloaded and can handle the previously unmapped object automatically.

If you think about it, it's a really fun problem to get to solve, wish I was working on it.

[kijin]

I agree that "never" is a pretty strong word to use in the context of computing. But I don't think what you described is the most interesting or difficult aspect of the problems that remain.

We're talking about a car, not a mobile phone. It's a 3000-pound chunk of metal that moves fast enough to kill anyone who comes into contact with it, and sometimes even those who ride in it. The ability to consult with a remote server would be nice, but the car should perform just as well even when a neighborhood prankster jams the cell & GPS signals.

So the entire approach of relying on a map might be misguided, regardless of whether the map is precompiled or JIT-crowdsourced. It seems that the current generation of autonomous vehicles rely too much on maps and too little on situational awareness. The next generation will need to make a lot of advances on the latter. Ideally, a car should be able to make all millisecond-by-millisecond decisions by itself, offline if necessary, and use the map only as a hint.

[sebdiem]

And google's approach is not the only one that scientists are exploring at the moment. Some are relying on more "reactive" strategies see e.g. Professor Alberto Broggi's work with the university of Parma (http://en.wikipedia.org/wiki/Alberto_Broggi). His vehicle doesn't make use of such an heavy map as the one used by Google.

What I liked in this article is that it reveals to the public that google's communication on the topic is really skewed. They try to make people think that the problem of autonomous driving is basically solved while many big challenges remain.

[yen223]

To be fair, most of the hype around the self-driving car don't come from Google, they come from people who are ... let's just say enthusiastic but who lack exposure in the subject matter.

[papa_bear]

I agree that the argument is pretty weak. Even if it mapping is complicated and hard to scale, I think that cities themselves would go to great lengths to make their roads compatible with self driving cars. Cities could fund the mapping themselves, or could probably be convinced to install sensors at traffic lights that improve sensor reliability on the cars.

[2muchcoffeeman]

Why wouldn't you chose to spend the money on improving public transport instead?

[craigds]

I had the same reaction. This article says "there are some hard challenges" and then equates that with "it's impossible."

The cars at this early stage require everything to be meticulously mapped, but I'm sure Google are working hard on making them handle unmapped situations as well. They have a lot of sensors; surely at some point they can start relying on them for unmapped situations.

[ars]

> devote some processing power to mapping it

If the car was able to do that simply by slowing down you wouldn't need the map in the first place.

The whole point is that a human needs to map it. The car has no idea what to do.

For example it doesn't "see" a stop sign and act on it. It knows in advance there is a stop sign there because a human told it so.

It's not scanning the environment looking for traffic signs, all it's doing is looking for obstacles in the way and avoiding them.

It doesn't even see the road edge, or the lane markings - it knows that in advance.

[mixedbit]

I don't know how the system is currently implemented by automatic traffic signs recognition is already possible, see for example this video: https://www.youtube.com/watch?v=hU7yHQkg-7U

[Someone]

I think that is another argument against Google's approach. If one can reliably detect traffic signs, why build that database? For traffic signs, one could argue that it helps increase recall; if the car knows the locations where traffic signs were seen earlier, it can decrease detection threshholds for those locations. However, the car still would have to be able to reliably detect new signs (say for a temporary detour) on first sight.

Things are way worse for all kinds of changes to roads. Even if the first car correctly classifies that white spot on the road as a lost paper that it can drive over, what good does that do the next car? The wind may have blown it away or to a different location and into a different shape.

To me, Google's approach seems an attempt to build a model of what the entire world looked like a short while ago, while cars only need a rough model of what it looks like now.

Scaling Google's approach to millions of cameras in million of cars may improve the model and decrease its latency and might make the latency low enough, but I don't see why it would be the best approach.

[ars]

The road sign was just an example to illuminate my point.

There are a tremendous number of things to map. If they could all be understood automatically google wouldn't need to map them ahead of time.