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The Waymo World Model

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Re: The Waymo World Model

#511

Post by scoofy »

raincole wrote: post #399
servo_sausage wrote: post #338

Earlier quoted context omitted.

Trains need well behaved people, otherwise they are shit. I don't want to hear tiktok or full volume soap operas blasting at some deaf mouth breather. I don't want to be near loud chewing of smelly leftovers. I don't want to be begged for money, or interact with high or psychotic people. The current culture doesn't allow enforcement of social behaviour: so public transport will always be a miserable containment vesse…

Roads (cars) need well behave people too. The only way cars filter some of the out is by the price.

The vast majority of the anti-social behavior on public transit not relevant in automobiles because (1) you can't turnstile jump the gas tank, (2) an automobile is effectively very expensive set of headphones, and (3) you can inhale whatever you want in your vehicle and your neighbor doesn't have to breath it.

Automobiles are a wildly inefficient and expensive form of transportation in urban areas. At the same time, we ought to be willing to ask why a significant amount of our urban population still prefers to pay all that extra money to sit in traffic.

Re: The Waymo World Model

#512

Post by takethebus »

scoofy wrote: post #479
takethebus wrote: post #460

Earlier quoted context omitted.

The government facing a budget deficit doesn't mean BART would be worse off with more subsidies.

Where does the extra money come from in a deficit period? > BART, Muni, Caltrain, AC Transit — which an independent analysis confirmed face annual deficits of more than $800 million annually starting in fiscal year 2027-28 https://www.usatoday.com/story/news/california/2026/01/06/ba... Nearly a billion dollar shortfall per year going forward. That’s nontrivial, and the state has lost patience with the systems after p…

Taxes? The same place tons of other stuff we buy as a society comes from. I expect the ballot measure this fall will pass, worst case they file bankruptcy and will probably need to reduce service

Re: The Waymo World Model

#513

Post by sdf2erf »

refulgentis wrote: post #496
AYBABTME wrote: post #490

Earlier quoted context omitted.

the rebuke is that lack of chaos makes people feel more orderly and as if things are going better, but it doesn't increase your luck surface area, it just maximizes cozy vibes and self interested comfort.

My dynamic range of professional experience is high, dropout => waiter => found startup => acquirer => Google. You're making an interesting point that I somewhat agree with from the perspective of someone was...clearly a little more feral than his surroundings in Google, and wildly succeeded and ultimately quietly failed because of it. The important bit is "great man" theory doesn't solve lack of dynamism. It usually…

Yeah people seem to be pretty poor at judging the impact of 'key' people.

E.g. Steve Jobs was absolutely fundamental to the turn around of Apple. Will Brin have this level of incremental impact on the Goog/Alphabet of today? Nah.

Re: The Waymo World Model

#514

Post by DoctorOetker »

0xfaded wrote: post #145
jellojello wrote: post #118

Earlier quoted context omitted.

Without Lidar + the terrible quality of tesla onboard cameras.. street view would look terrible. The biggest L of elon's career is the weird commitment to no-lidar. If you've ever driven a Tesla, it gives daily messages "the left side camera is blocked" etc.. cameras+weather don't mix either.

I have HW3, but FSD reliably disengages at this time of year with sunrise and sunset during commute hours.

This will considerably skew the statistics, a low sun dramatically increases accident rates on humans too.

Re: The Waymo World Model

#516

Post by acomjean »

cyanydeez wrote: post #380
robotresearcher wrote: post #250

Earlier quoted context omitted.

The depths you are trying to estimate are to the other cars, people, turnings, obstacles, etc. Could be 100m away or more on the highway.

ok, but the point trying to be made is based on human's depth perception, but a car's basic limitation is the width of the vehicle, so there's missing information if you're trying to figure out if a car can use cameras to do what human eyes/brains do.

Humans are very good at processing the images that come into our brain. Each eye has a “blind spot” but we don’t notice. Our eyes adjust color (fluorescent lights are weird) and the amount of light coming in. When we look through a screen door or rain and just ignore it, or if you look outside a moving vehicle to the side you can ignore the foreground.

If you increase the distance of stereo cameras you probably can increase depth perception.

But a lidar or radar sensor is just sensing distance.

Re: The Waymo World Model

#517

Post by rogerrogerr »

dbcurtis wrote: post #111
uejfiweun wrote: post #8

Earlier quoted context omitted.

I've always wondered... if Lidar + Cameras is always making the right decision, you should theoretically be able to take the output of the Lidar + Cameras model and use it as training data for a Camera only model.

No, I don't think that will be successful. Consider a day where the temperature and humidity is just right to make tail pipe exhaust form dense fog clouds. That will be opaque or nearly so to a camera, transparent to a radar, and I would assume something in between to a lidar. Multi-modal sensor fusion is always going to be more reliable at classifying some kinds of challenging scene segments. It doesn't take long to…

The goal is not to drive in all conditions; it is to drive in all drivable conditions. Human eyeballs also cannot see through dense fog clouds. Operating in these environments is extra credit with marginal utility in real life.

Re: The Waymo World Model

#518

Post by dyauspitr »

tantalor wrote: post #510
IMTDb wrote: post #373

Earlier quoted context omitted.

The idea is that, over time, the quality and accuracy of world-model outputs will improve. That, in turn, lets autonomous driving systems train on a large amount of “realistic enough” synthetic data. For example, we know from experience that Waymo is currently good enough to drive in San Francisco. We don’t yet trust it in more complex environments like dense European cities or Southeast Asian “hell roads.” Running t…

It's a pareto principal. You can get 80% of the way to "perfect" with 20% of the effort.

That’s just a platitude at this point. They for all intents and purposes solved the problem, atleast in the US.

Re: The Waymo World Model

#519

Post by dyauspitr »

caycep wrote: post #265

All this work is impressive, but I'd rather have better trains

Enough with the trains. I’m all for trains but theyre good for in city or 1-3 hour journeys. Taking a train across the US would take a day even with high speed trains.

I’d much rather have my own vehicle than share my space with a bunch of people.

Re: The Waymo World Model

#520

Post by DoctorOetker »

kypro wrote: post #322
senordevnyc wrote: post #225

Earlier quoted context omitted.

This is so wild to read when Waymo is currently doing like 500,000 paid rides every week , all over the country, with no one in the driver's seat. Meanwhile Tesla seems to have a handful of robotaxis in Austin, and it's unclear if any of them are actually driverless. But the Tesla engineers are "in the right place rather than hitting bottlenecks from over depending on Lidar data"? What?

I wasn't arguing Tesla is ahead of Waymo? Nor do I think they are. All I was arguing was that it makes sense from the perspective of a consumer automobile maker to not use lidar. I don't think Tesla is that far behind Waymo though given Waymo has had a significant head start, the fact Waymo has always been a taxi-first product, and given they're using significantly more expensive tech than Tesla is. Additionally, it'…

What causes LiDAR to fail harder than normal cameras in bad weather conditions? I understand that normal LiDAR algorithms assume the direct paths from light source to object to camera pixel, while a mist will scatter part of the light, but it would seem like this can be addressed in the pixel depth estimation algorithm that combines the complex amplitudes at the different LiDAR frequencies.

I understand that small lens sizes mean that falling droplets can obstruct the view behind the droplet, while larger lens sizes can more easily see beyond the droplet.

I seldom see discussion of the exact failure modes for specific weather conditions. Even if larger lenses are selected the light source should use similar lens dimensions. Independent modulation of multiple light sources could also dramatically increase the gained information from each single LiDAR sensor.

Do self-driving camera systems (conventional and LiDAR) use variable or fixed tilt lenses? Normal camera systems have the focal plane perpendicular to the viewing direction, but for roads it might be more interesting to have a large swath of the horizontal road in focus. At least having 1 front facing camera with a horizontal road in focus may prove highly beneficial.

To a certain extend an FSD system predicts the best course of action. When different courses of action have similar logits of expected fitness for the next best course of action, we can speak of doubt. With RMAD we can figure out which features or what facets of input or which part of the view is causing the doubt.

A camera has motion blur (unless you can strobe the illumination source, but in daytime the sun is very hard to outshine), it would seem like an interesting experiment to:

1. identify in real time which doubts have the most significant influence on the determination of best course of action

2. have a camera that can track an object to eliminate motion blur but still enjoy optimal lighting (under the sun, or at night), just like our eyes can rotate

3. rerun the best course of action prediction and feed back this information to the company, so it can figure out the cost-benefit of adding a free tracking camera dedicated to eliminating doubts caused by motion blur.

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