DuoRivian
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- Joined
- Sep 3, 2023
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- Location
- California
- Vehicles
- Rivian R1T and an R1S
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- IT
Good summary and it makes perfect sense to do their own chip and cut out the middle man (Nvidia who have a 70% profit margin). Simplifies supply chain too as they can deal direct with the chip manufacturer.If you go to the following video and specifically to the section about vision versus LIDAR (29:45) RJ Scaringe answers most of these questions.
Here are some quotes from RJ Scaringe:
"a high performance long range LIDAR you can buy for a couple hundred bucks"
"The most expensive part in a self-driving system is the brain. ... It's the inference platform plus the entire compute platform necessary support that. So, all the associated memory ... and all that consolidated onto a big PCBA, the cooling systems to support that that's far more expensive than the perception stack."
"radars are tens of dollars, cameras are tens of dollars, lidars are hundreds of dollars."
"[LIDAR is] incredibly useful for training your cameras."
"you just have to go El Camino or page and you'll see a bunch of Telsas drive by ... with LIDARS mounted on them and it's part of their ... ground truth fleet for training their models"
"in fact any of the incremental costs that would have been there on its own is offset by the fact that we brought inference in house and reduced the cost of our inference platform so dramatically from [the R1S that] uses an Nvidia inference platform."
"I say all this because in the infinite long-term ... you could make the case that once the models are very very robust you could have less cameras or you may be able to get away with less radar. It's not clear yet if that's the case for covering all these corner cases. I'd say it's unknown but ... in our case it's very clearly accelerates the rate of progress for training the model and it very clearly allows us to deliver level four features"
My takeaways are:
The cameras, radars and LIDAR are for training the model; it is unknown what will be needed for level four self-driving (the inference stack as opposed to the training wheel for the model). I would assume radar would be needed for poor visibility but LIDAR may not be needed.
The RAP1 chip is four times as powerful as the Nvidia chip (800 TOPS versus 200 TOPS) so there might be some reason to wait for it but I do not see any reason to wait for LIDAR which may not even be needed for level four driving (which is still along ways off). But I think the difference would just be the speed at which the inference stack responds to the sensors.
The inference stack with the RAP1 chip is going to cost less than the Nvidia inference stack even with the additional LIDAR sensor so it will save Rivian some money. I do not think they will charge more for the LIDAR and RAP1 chip.
If you are worried about obsolescence I would worry about solid state batteries. I think they are far enough off that they are not a concern but they may be widely available by around 2030. So if you wait for LIDAR then you may then find yourself waiting for solid state batteries.
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