According to their latest Californian DMV filing, Waymo's disengagement rate was 0.076 per 1,000 miles.

This means for every disengagement, they've driven for 13,158 miles on average. That's ~1000~ EDIT: 4, thank you; orders of magnitudes from your "5km".

Could a Mercedes in the 1990s drive itself for 13k miles without human intervention?

> This means for every disengagement, they've driven for 13,158 miles on average. That's 1000 orders of magnitudes from your "5km".

That's four orders of magnitude, not a thousand.

Thank you for the correction :)

FWIW, 1,000 orders of magnitude is an order of magnitude larger than a googol.

Also where these miles happen, and how long they take relative to a human driver (1.1x?) matter quite a bit.

edit: I've never shared a road with a self-driving car but I can't imagine I'd like it. I'm extremely patient with fellow meatbags (I work a lot of customer service, and enjoy it), but I'm not at all patient with software thinking it's smarter than it is. I also think people tend to underestimate how much "car-body language" is involved with driving as well, but I suppose that's the primary thing self-driving cars are learning.

Self-driving cars can also burgeon a surveillance state depending on how the fleet is implemented, particularly around what will be required for client-side redaction (pedestrians, license plates, windows etc). Keeping raw data locally for a period of time is fine in case of crash/malfunction, even backing it up encrypted with ephemeral keys can be well and good. But unfettered collection and access to unredacted data—perhaps something Google is drooling over—needs to be stopped cold.

To be fully pedantic, 1000 orders of magnitude (10^1000) is really 900 orders of magnitude larger than a googol.

10^1000 / 10^100 = 10^900

Woah, cool thank you.

I don't trust that this number is proving what you're saying it does. It's a meta point, but I found myself trusting such "data" less and less.

"Do you have data?" or "Do you have a peer-reviewed paper about it?" is an Internet meme at this point. Bringing some numbers or DOIs is table stakes now, in anything but dumbest of discussions.

These values - "0.076 per 1000 miles", "13158 miles on average" - have a history behind them. They're pulled out of a set of test drives. But how did the drives look? Was it a collection of similar drives, or a bunch of longer drives with significant disengagement plus a lot of short and easy drives to lower the average? To understand the true meaning of the numbers, you'd need to see (among other things) the distribution of distances covered by test drives.

I wish we had a way to require and embed context with the data, inline with the data. So that when I see "0.0076 per 1000 miles", I know it's:

  SELECT 1000
       * (SELECT sum(disengagements) FROM drives)
       / (SELECT sum(distance) FROM drives)

And so that I could click around and turn it into e.g. a probability distribution of:

  SELECT distance FROM drives

Or view a cumulative distribution of disengagements per distance:

   SELECT t1.distance, SUM(t2.disengagements)
   FROM drives t1
   INNER JOIN drives t2 ON t1.distance >= t2.distance
   GROUP BY t1.distance
   ORDER BY t1.distance

Now the problem usually is, this data is often not available - people release aggregate summaries instead of full data sets (whether that's the case with Waymo, I don't know - I didn't check, because I don't care about this particular case). But it should be made available, because point summaries of distributions are the easiest way to derive bullshit conclusions (and/or intentionally mislead people). And conversely, I don't trust point summaries unless I either a) review the underlying data (or at least know the shape of its distribution), or b) can trust that the person giving me the summary reviewed the underlying data (or knows the shape of its distribution) and isn't trying to mislead.

So there are two problems here: getting people to release more data, and creating tech infrastructure so that this data can be explored inline (preferably with nice UX that doesn't require user to type in SQL).

</end-of-meta-rant>

13000m ~> 20000km

    Order of 200000: 10^4
    Order of 5: 1^0

Conclusion: difference in order of magnitudes = 4 (not 1000)

There are endless ways to game that number. The biggest systematic bias of all must be simply California weather.

That's a pretty clever technology though right? Because ordinarily you'd expect Waymo cars driving in say Phoenix, Arizona to have er, Arizona weather?

More seriously, once you switch to "Actually I think in a snow storm this car isn't better after all" we're back to God of the Gaps style arguments which you can choose to lose incrementally if you want but it just looks kind of sad. It's definitely true that human drivers will set out in dangerous conditions and sometimes arrive safely. If, as Waymo promotes, the idea is self-driving cars are safer they just won't go in those conditions because it's unsafe.

This is a debated number, with people telling that they don't count anything, but actual "stall" when the system stands still and can't do anything, as disengagement.

And accounts of stalled Waymos in the middle of the road are too frequent to believe them

Disengagement is whenever the human test driver has to intervene. The car stalling in the middle of the road would only be counted as a disengagement if it doesn't recover on its own.

> And accounts of stalled Waymos in the middle of the road are too frequent to believe them

You gonna provide a source for that?

> The best proof is that even with billions spent, the best contenders got marginal improvement over what Mercedes had in 1990 with discreet algorithms and no neural/fuzzy logic

Can you elaborate on that? I haven't found much on that, e.g. w.r.t. the Mercedes system handling other vehicles, pedestrians, street signs, unexpected situations (roadworks, children playing, etc.) - from what I was able to gather, the two systems aren't even remotely comparable.

Edit: Mercedes was able to go on a highway, follow the lane, and keep distance to the car in front. Claiming "the best contenders got marginal improvement" is heavily biased at best - it feels like an attempt to justify an outdated opinion, rather than having facts inform it.

They haven't been relying solely on "fancy imaging/neural algo" - every self-driving car since the DARPA challenge has used LIDAR, and a good chunk of the self-driving R&D funding has gone into making those cheaper. The LIDAR they're bragging about in this blog post is them adding four more sensors to the car for short range, on top of the one they already had on top.

mm-wave radar gives them better sensing for long ranges and in bad weather, but isn't a fundamental change in approach from their already-sensor-heavy tech.

> them finally employing a mm-wave radar this time is an evidence to them backing off to more "dumb" and sure to work solution as a last line of defence once their fancy imaging/neural algo fails

AFAIK from little experience in the industry interpreting Radar images is not easy nor dumb

For example read here:

http://homepage.tudelft.nl/e15f9/pdf/Uysal_MAXWELL_19.pdf

> what Mercedes had in 1990 with discreet algorithms and no neural/fuzzy logic

Do you have any more information about it?

I could find only abstract info that the system existed: https://www.autoevolution.com/news/a-short-history-of-merced...

Full self driving is probably wishful thinking for the next decade at minimum. That said, even just getting to a point where you could use a human+tool combo to reliably shepard a second truck to a depot at the outskirts of a city would have profound logistics implications.