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Waymo

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I was interested in the story until I read that the Waymo had phantom braking events. That's where I realized it must be bogus, because we all know that Tesla is the only car with phantom braking. So many new EVs with ADAS, it's getting harder to differentiate and keep an edge. Don't take away our phantom braking!! 🤣😂
 
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Thanks for sharing. Drago is a captivating speaker, which is much appreciated when the content is dry and technical.

At around 26:30, Drago describes one way that Waymo is using self-supervised learning to train computer vision neural networks. The supervisory signal/training signal comes from internal geometric consistency and internal temporal consistency.

Self-supervised learning is, for me, the most exciting frontier in autonomous driving R&D because it gets rid of the bottleneck of human annotation, which is costly, slow, and severely limited in scale. The more computer vision tasks (and other tasks) that can be trained in a self-supervised manner, the closer we’ll get to an actual solution to the autonomous driving problem.

I’m most excited to see what Tesla does with self-supervised learning because they have 160,000+ FSD-capable vehicles to curate data from. Quantity has a quality all its own.

Yes, I think self-supervised learning is an absolute must-have. IMO, you are not going to solve FSD "by hand". ML must be automated in order to achieve the vast amount of training necessary in a reasonable amount of time.

And again, it should be pointed out that Tesla did not invent self-supervised learning. All major AV companies do it.
 
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What’s interesting about Tesla as compared to other companies is that other companies have roughly on the order of 100 or 1,000 vehicles from which to curate datasets, whereas Tesla has 160,000+ vehicles enrolled in the FSD beta program and millions of other vehicles on the road. Without the bottleneck of human labellers, Tesla can leverage self-supervised learning at an unprecedented scale.

Mobileye also has a large fleet. They have millions of cars that are equipped with their ADAS. They have been able to use those millions of cars to crowdsource map data to build their HD maps of nearly every road in the US and Europe. I imagine they could also use that data for ML self-supervised learning too.
 
Warren works at Waymo so he is biased but I do feel like Waymo is at an inflection point. I think we are seeing the scaling get faster now. It is encouraging if Waymo is noticing that their deployments are getting easier.

It's exciting to see them scale rapidly, but someone has to be keeping an eye on financials. They are reporting billions in losses over the last few years. Investment is still coming in, which is great, but the company has to turn a profit sometime. Either fare increases, licensing deals for their technology, or partnerships.
 
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Operating at small scale in different cities give them the opportunity to not only test and validate their software but also work out the issues and learn how to operate in different regions.
Yes, they will learn more things. If there's one thing Waymo excels at, it's testing and learning. But learning is not a business.

There is a literal 4 hours long video of waymo driving around without a safety personnel above.
I've always praised Waymo's technology. But they're squandering their once-massive tech lead because they can't figure out the business side.

But now when they do scale, you complain that they are scaling horizontally instead of vertically.
No, this is not scaling. At least not in the business sense. Sub-scale operations in LA will lose just as much money as in San Francisco and Phoenix.

The fact that Waymo is adding LA is proof they are making real progress.
"Making progress" isn't the issue. Creating a business is.

Again, that is a very negative slant. If you read the blog, the ride was very good. There were only a couple issues. Obviously, AVs are not perfect.
No product is perfect. Your business model must work despite occasional product failure. If 1 in 1000 mail-order gizmos are DOA you do a money-back guarantee and increase the cost to the other 999 by a nickel. Easy. Waymo could do something similar if ride failures were rare and uncorrelated, e.g. Conegate. But a San Francisco robotaxi service that shuts down in fog is not viable. Customers won't say "dang, a fog just rolled in, guess I'll put my frozen food back and sleep here in aisle 5 tonight". And it's not viable to have 20,000 fully trained safety drivers on call, ready to drop whatever they're doing and jump into action every time fog hits.

It's the same with Phoenix and rain. 98% uptime is generally manageable, but not if it means you shut down completely the other 2%. That's the kind of "fatal flaw" Warren Craddock spoke about in his thread the other day.

The fog/rain/etc. issue has a technical solution. Maybe it'll come in Gen6 or Gen7. But some of their other flaws don't. At least not any time soon.

I'd be much more excited if they opened up a much different type of service vs. repeating the same sub-scale experiment in more cities. The Phoenix Sky Harbor thing is one small example of that type of iteration. They need about 100 more.

I have no such doubts about trucking. It's a great business, IMHO, if they can get regulatory approval. I half-joke robotaxis are just a way to build a safety record to help them get past trucking regulators.
 
It's exciting to see them scale rapidly, but someone has to be keeping an eye on financials. They are reporting billions in losses over the last few years. Investment is still coming in, which is great, but the company has to turn a profit sometime. Either fare increases, licensing deals for their technology, or partnerships.

Definitely. Waymo recently hired a new CFO. Hopefully, they have a financial plan for making a profit as quickly as possible.
 
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Yes, they will learn more things. If there's one thing Waymo excels at, it's testing and learning. But learning is not a business.
Business is operating an autonomous ride hailing fleet which they are. It is a literal new frontier in mobility as a service that nobody has figured out how to do. Waymo is the only company right now doing so at a smaller scale in the US.

I've always praised Waymo's technology. But they're squandering their once-massive tech lead because they can't figure out the business side.
Maybe because it is a difficult thing to solve? Nobody has figured out the business side. You can't just put thousands of vehicles on the road and hope for the best. The market is big enough to accommodate many players in the field. Has all the issues cruise has encountered with their car blocking traffic and needing to be manually recalled not taught us anything?

No, this is not scaling. At least not in the business sense. Sub-scale operations in LA will lose just as much money as in San Francisco and Phoenix.
It is scaling. They are setting up logistics and services in various regions that is literally scaling. What you want them to do which they are not ready to do is expand their operational domain statewide.
 
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What’s interesting about Tesla as compared to other companies is that other companies have roughly on the order of 100 or 1,000 vehicles from which to curate datasets, whereas Tesla has 160,000+ vehicles enrolled in the FSD beta program and millions of other vehicles on the road. Without the bottleneck of human labellers, Tesla can leverage self-supervised learning at an unprecedented scale.
Its actually way more than that. Its quite easy to focus on Tesla in detail and gloss over what others are doing.

Here's a list of cars for example with supercomputers that have sometimes an order of magnitude better compute than Tesla HW3 and certainly Tesla's HW2, way better sensors (hence more quality data), more storage to hold more data and also 5G in alot of cases:

This is not an exhaustive list, there are alot of models missing.
  • 2020 Xpeng P7 - Nvidia Xavier 30 TOPs (12x cameras, 5x radars, 12x ultrasonics)
  • 2021 Xpeng P5 - Nvidia Xavier 30 TOPs (2x Lidars, 12x cameras, 5x radars, 12x ultrasonics)) Production 2021
  • 2022 Xpeng G9 - 508 TOPS 2x Nvidia Orin-X Chips (2x Lidars, 12x cameras, 5x radars, 12x ultrasonics) Production 2022
  • 2022 Nio ET7 - 1,016 TOPS 4x NVIDIA DRIVE Orin-X chips (11x (7x 8MP/ 4x 3MP) camera, 1 high res lidar, 5x radars, 12 ultrasonics) Production 2022
  • 2022 Nio ET5 - 1,016 TOPS 4x NVIDIA DRIVE Orin-X chips(11x (7x 8MP/ 4x 3MP) camera, 1 high res lidar, 5x radars, 12 ultrasonics) Production 2022
  • 2022 Nio ES7 - 1,016 TOPS 4x NVIDIA DRIVE Orin-X chips(11x (7x 8MP/ 4x 3MP) camera, 1 high res lidar, 5x radars, 12 ultrasonics) Production 2022
  • 2021 Zeekr 001 - 32 TOPS 2x EyeQ5 (11x (7x 8MP/ 4x 3MP) camera, 1x radars, 12 ultrasonics) Production October 2021
  • 2023 Zeekr 009 - 32 TOPS 2x EyeQ5 (11x (7x 8MP/ 4x 3MP) camera, 1x radars, 12 ultrasonics) Production 2023
  • 2022 WEY Mocha Lidar - Snapdragon Ride 360 TOPs (2x lidars, 12x high-def cameras, 5x radars, 12x ultrasonics)
  • 2023 Volvo EX90 - 254 TOPS Nvidia Orin-X chip (8x cameras, 1 lidar, 5 radars, 16 ultrasonics)
  • 2021 BAIC Arcfox Alpha S Huawei MDC810 400 TOPS (3x lidars, 6x radars, 13x ultrasonics, 12x high def cameras)
  • 2021 Lucid Air Nvidia Drive 30+ TOPS (14x cameras, 5x radar unit, ultrasonic sensors)
  • 2022 WM Motors M7 - 1,016 TOPS 4x NVIDIA DRIVE Orin-X chips (3x LiDARs, 5x radars, 12x ultrasonic, 7x high-res cams, 4x surround-view cams)
  • 2022 Li Auto L9 - 508 TOPS 2x Nvidia Orin-X Chips (1x Hesai 128 line lidar, 11 cams (6x 8-megapixel cams/ 5x 2-megapixel cams), 1x radar and 12 ultrasonics)
  • 2022 Hozon Neta S - 200 TOPS Huawei MDC (2x Lidars, 13x cameras, 12x ultrasonics, 5x radar)
  • 2022 GAC Aion LX Plus - 200 TOPS (3x LiDARs, 6x radars, 12x ultrasonic, 8x high-def cams, 4x panoramic-view cams)
  • 2022 Avita 11 - Huawei MDC810 400 TOPS (3x lidars, 6x radars, 13x ultrasonics, 12x high def cameras)

I don’t think Mobileye has the ability to upload raw sensor data, e.g. video clips, from the millions of cars carrying Mobileye chips.
While Mobileye doesn't collect raw pictures/videos from the over 2 million cars with EyeQ4 that are sending NN output and other car telematics for mapping.

They are on the other hand collecting raw pictures/videos from their SuperVision (2x EyeQ5) fleet on the Zeekr 001. There are over 50,000 Zeekr 001 delivered. With goals to deliver 70k by the end of the year, 140k to 200k by end of 2023.

That's a huge fleet and the system will be released OTA mere weeks from now.

The same is the case for Xpeng who have a fleet of ~100k P7 and P5 that they collect data from.
Also with NIO who has a fleet of 5k-7k fleet of NIO ET7 and ES7 with ET5 (with pre-order of over 200k) on the way.
Then you have Huawei with around ~1k fleet of Arcfox Alpha S HI version with Avita 11 entering deliveries which is poised to sell alot more (and have hardware as standard).
 
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Why keep adding cities instead of scaling up in one city? Answer: they can't afford to scale up. So this is the only way they can show "progress" to investors.

In other news, Waymo recently gave a "driverless" ride to a San Francisco journalist. It didn't go great. First, it wasn't driverless. Maybe because of fog? The chaperone didn't really say. And Waymo says Gen5 can handle fog. There were also phantom braking events and the car got so confused at a drop-off point the safety driver had to put it into manual mode for a few blocks.

You want to keep adding cities because you don't want your system to just be good in one city, but to experience all the intricacies of different cities. Adding different cities is also a litmus test on your ability to scale your software, infrastructure and logistics.

I will give you one thing. Google has a huge problem in failing to market new technological innovation in a brand new categories. We have examples in google fiber, project tango, google glass, DayDream, google stadia, etc.

But google is also great at copying, look at Android, Google Home, ARCore, Pixel, PixelBook (although now canceled) for example.

What I believe they should copy is partially how Cruise is handling deployments. Launching services at night (10pm - 6am) where there is less traffic and craziness is brilliant.

This will allow Cruise for example to scale faster and get to 50 cities quite easily. It will also challenge Cruise to progressively build up their infrastructure and logistics to handle 50 cities.

Ofcourse this requires that the foundational tech required is done. Aka the system can handle fundamental tasks at safety levels greater than a human and an ML factory to improve. For example: navigating around, stopping for and responding to pedestrians/cyclists. Stop for and responding to any and all static objects. And for general driving, it just has to be good to a certain performance miles that when you put artificial limits on it (10pm-6AM), that it boosts safety above human drivers.

For example a system that is developed to be a L4 highway system that can drive 90 mph, perform lane changes, handle on-ramp/off-ramp, lane merges, construction, freeway exit, traffic jam, accidents scene, all the DDT on the divided highway. But the current safety performance is 10,000 miles before a safety disengagement is required.

But by artificially limiting the system to a L3 single lane, requiring a vehicle in front and under 37 mph in clear weather, it automatically boosts the safety performance of the system to 100,000 miles (10x) for example.

The same is the case by Cruise developing the system to handle all driving, all times of day, all weather. But artificially limiting it during initial deployments.

Since I believe Waymo clearly has the superior tech, this is something i believe they CAN copy.

Theoretically, you could get to 50 cities in 5 years. The limit then becomes your logistics and infrastructure (safety drivers, warehouse, cleaning cars, charging cars, charging station, maintenance engineers, roadside assistance, etc).

Hiring more safety drivers is not the roadblock. For example, if Cruise/Waymo wanted a fleet of 1,000 cars in 50 cities.
10 cars used for autonomous driving, while 10 (20 during the day) cars used with safety drivers.
If they decided to hire 1000 safety drivers.

At 15 dollars per hour they would be spending $31,200,000 a year.
At 20 dollars per hour they would be spending $62,400,000 a year.

This is cheap (and won't happen all at once) compared to the 500 mill Cruise is spending every quarter. Tacking 8 mil to that with the high reward is really nothing.

If you noticed, Waymo put no deadline to their start of test to launch in LA. While SF put a 3 month deadline to their start of test and launch in Texas. Now if Cruise can launch in 5 or more cities per year...
 
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In the hypothetical (ahem) I get to record a video in an I-PACE next week, is there anything anyone wants to see specifically (I already found a three point turn spot)

Try to find a difficult route that has unprotected turns, busy traffic etc... Thanks.

I hope you get to make a video in the I-Pace. I know it's been a long wait. And your videos are great. You place the camera in a good spot to show what is happening.
 
Try to find a difficult route that has unprotected turns, busy traffic etc... Thanks.

I hope you get to make a video in the I-Pace. I know it's been a long wait. And your videos are great. You place the camera in a good spot to show what is happening.
As always; although there's only so much I can do without prior access to view the available pickup/dropoff locations