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Comment by Dangeranger | Hacker News Reader

Question: Does the fact that the collection of the required volume of kms driven is performed over a shorter period of time based on a higher number of test subjects collecting the data impact the diversity and quality of the data negatively?

Said another way: Is there a downside to parallelizing the collection process across a broader horizontal population of collection vehicles over a shorter period versus the traditional 1-2 year collection period?

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The only obvious downside to an outside observer is that they will find it difficult to collect 'all weather' data in substantial volume in less than approximately a single year.

From the positive side, they will collect and be able to evaluate a much /wider/ range of driving data from a far larger set. They are much more likely to run across 'better morons' both in operators of the Tesla and in operators of other cars.

Really a lot of weather patterns are analogous; for vision heavy rain isn't terribly different from a blizzard, etc. With a large enough span across the US you can get pretty much all weather conditions inside a 6 month window from mid summer to mid winter.

...for vision heavy rain isn't terribly different from a blizzard...

I would say that heavy rain is incredibly different from a blizzard. Snow has a much greater horizontal-to-vertical velocity, reflects more light, and flutters around. Rain mostly moves in straight lines, even in high wind. Not to mention traction is completely different on water vs. slush vs. snow vs. ice, as well as with good snow/rain tires vs. worn all-season tires.

I'm sure Tesla knows all this, but just in case you haven't driven in a blizzard before, you will need to drive very, very differently from driving in heavy rain.

I hope they know that when there's a south eastern style afternoon thunderstorm that dumps several inches of rain in 20 minutes that it is best to just pull over and wait.

Just had a flashback to a near death accident in Louisiana on the highway at about 70mph this way. Cloudy no rain, pavement dry, then almost instantly a torrential downpour dumped what looked like a standing half inch of water on the road. The tires lost traction and I slid off into the median. Thankfully I the median was wide and grass and I coasted to a stop (sideways) about a yard from a tree. If you've driven I-10 through Louisiana you know what I mean.

Not sure what an autonomous car could have done in such a situation but anticipate that maybe we should preemptively pull over for a bit before it starts raining.

Hopefully any autonomous system will be able to make the call of whether it is safe to drive itself - whether because of heavy rain, blizzard, tornado etc. Some situations are just not safe to drive a car in, regardless of who's driving.

"Let's go, car, my child is bleeding to death and I have to get him to the hospital now!"

"I'm sorry, Dave, I can't do that. It's raining."

"Are you kidding me?! He's dying! Go! Now!"

"Did you try applying pressure to the wound?"

Let's hope that we never have cars that decide it's not safe for us to drive, "regardless of who's driving."

That's only helpful if you have a fairly random distribution across the US. I very much doubt that's the case for Tesla. You'll probably have far more Teslas driving around in California, maybe even the Silicon Valley, than in say Alaska.

It doesn't need to be random as long as it's big enough. (Also, other countries exist besides the US...)

> (Also, other countries exist besides the US...)

That's a common misconception, but I believe it was debunked by snopes.

It's becoming less common. Many people here in Australia are aware that they're living in the US.

But they have a lot of drivers in Norway.

The question whether a time average over a long period of a random quantity in one system and an average over multiple systems over a short period are the same is the question for ergodicity [1]. If both are the same, the random process or system is called ergodic.

The answer is: If the measurement period for the average over multiple systems is long enough that each random variable has visited a representative sample of states which make up the majority of contributions to the mean value, then the system is ergodic.

[1] https://en.wikipedia.org/wiki/Ergodicity

Here's an interesting problem I hadn't thought about before. Teslas are expensive and rich people drive in different places than poor people, so teslas will have more data about places rich people go. That's another interesting data imbalance.

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