![seti_park's tweet photo. DATA PIPELINE AND DEEP LEARNING SYSTEM FOR AUTONOMOUS DRIVING
@Tesla's US20250225391A1 patent addresses the fundamental problem in sensor data conversion where "compression and down-sampling that can reduce the signal fidelity" ([0002]).
The fundamental bottleneck persists when sensors capture 12-bit, 16-bit, 20-bit, or 32-bit data that must be converted to fit 8-bit neural network requirements, causing inevitable signal fidelity loss ([0018], [0021], [0002]). Traditional conversion methods simply compress high bit-depth raw data, losing critical information in the process.
Tesla transcends this limitation through decomposing captured images into plurality of component images: feature data component via high-pass filter ([0040]), global data component via low-pass filter ([0043]), each provided to different layers of the neural network ([0035]). This approach enables each component to "fully utilize the image resolution of their respective components for their intended purposes" ([0012]).
[FIG. 3: Parallel component extraction paths 311 and 321 for feature and global data]
[FIG. 6: System architecture with image pre-processor 603 and deep learning network 605]](https://pbs.twimg.com/media/Gxr0x-vawAQnt88.jpg)
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My best guess of robotaxi registration growth based on data so far from TX DMV
+10: all 2026 Model Y LR AWD, Fremont:
7SAYGDEE3TF583974
7SAYGDEE5TF598900
7SAYGDEE6TF583824
7SAYGDEE7TF598624
7SAYGDEE6TF584892
7SAYGDEE7TF599207
7SAYGDEE8TF596574
7SAYGDEE4TF607442
7SAYGDEE5TF600189
7SAYGDEE0TF554416
Total: 69.
๐จ Tesla Robotaxi, LLC added 10 vehicles to its TX DMV registry.
All Fremont-built 2026 Tesla Model Y Long Range AWD.
Registered fleet: 69
$TSLA #Robotaxi
+1: 2026 Model Y LR AWD ยท VIN 7SAYGDEE4TF598984 ยท Fremont
Total: 59.
๐จ Tesla Robotaxi, LLC added 3 vehicles to its TX DMV registry.
All Fremont-built 2026 Tesla Model Y Long Range AWD.
Registered fleet: 54
$TSLA #Robotaxi
+4: all 2026 Model Y LR AWD, Fremont:
7SAYGDEE1TF588512
7SAYGDEE1TF601839
7SAYGDEEXTF559980
7SAYGDEE3TF581349
Total: 58.
๐จ BREAKING: @Tesla Cybercab spotted in Seattle area โ one of the first on WA highways!
Golden @robotaxi was seen driving on I-5, then over the floating 520 bridge and continued east, while I went north onto I-405.
The autonomous future is already driving WA highways!
@SawyerMerritt @wholemars @Kristennetten @DavidMoss @herbertong
@austinreforms Not yet but I can
๐จ Tesla Robotaxi, LLC fleet update (TX DMV AV8313426653583):
Added: 4 2026 Tesla Model Y
Registered fleet now 51 vehicles
@Tslachan Itโs 51 now. My account will automatically post updates.
๐จ Tesla Robotaxi, LLC fleet update (TX DMV AV8313426653583):
Added: 4 2026 Tesla Model Y
Registered fleet now 51 vehicles
๐จ Tesla Robotaxi, LLC fleet update (TX DMV AV8313426653583):
Added: 2 2026 Tesla Model Y
Registered fleet now 47 vehicles
๐จ Tesla Robotaxi, LLC fleet update (TX DMV AV8313426653583):
Added: 1 2026 Tesla Model Y
Registered fleet now 45 vehicles
๐จ Tesla Robotaxi, LLC fleet update (TX DMV AV8313426653583):
Added: 1 2026 Tesla Model Y
Registered fleet now 44 vehicles
๐จ Tesla Robotaxi, LLC fleet update (TX DMV AV8313426653583):
Added: 1 2026 Tesla Model Y
Registered fleet now 43 vehicles
Interestingly this vehicle is NOT part of the 42 vehicle fleet registered in TxDMV @tesla2moon @DavidMoss. Tesla has 30 days to register afaik so the VIN list may lag reality.
Honestly even though I only saw 7 today, the reports are 30 in ATX. Will search for more later.
๐จ Now tracking the Tesla Robotaxi, LLC AV fleet (TX DMV AV8313426653583).
Fleet now 42 vehicles
The EU government:
@jeremyjudkins_ Sorry for your loss โ Don is proud of you ๐ฏ๏ธ๐
DATA PIPELINE AND DEEP LEARNING SYSTEM FOR AUTONOMOUS DRIVING
@Tesla's US20250225391A1 patent addresses the fundamental problem in sensor data conversion where "compression and down-sampling that can reduce the signal fidelity" ([0002]).
The fundamental bottleneck persists when sensors capture 12-bit, 16-bit, 20-bit, or 32-bit data that must be converted to fit 8-bit neural network requirements, causing inevitable signal fidelity loss ([0018], [0021], [0002]). Traditional conversion methods simply compress high bit-depth raw data, losing critical information in the process.
Tesla transcends this limitation through decomposing captured images into plurality of component images: feature data component via high-pass filter ([0040]), global data component via low-pass filter ([0043]), each provided to different layers of the neural network ([0035]). This approach enables each component to "fully utilize the image resolution of their respective components for their intended purposes" ([0012]).
[FIG. 3: Parallel component extraction paths 311 and 321 for feature and global data]
[FIG. 6: System architecture with image pre-processor 603 and deep learning network 605]
![seti_park's tweet photo. DATA PIPELINE AND DEEP LEARNING SYSTEM FOR AUTONOMOUS DRIVING
@Tesla's US20250225391A1 patent addresses the fundamental problem in sensor data conversion where "compression and down-sampling that can reduce the signal fidelity" ([0002]).
The fundamental bottleneck persists when sensors capture 12-bit, 16-bit, 20-bit, or 32-bit data that must be converted to fit 8-bit neural network requirements, causing inevitable signal fidelity loss ([0018], [0021], [0002]). Traditional conversion methods simply compress high bit-depth raw data, losing critical information in the process.
Tesla transcends this limitation through decomposing captured images into plurality of component images: feature data component via high-pass filter ([0040]), global data component via low-pass filter ([0043]), each provided to different layers of the neural network ([0035]). This approach enables each component to "fully utilize the image resolution of their respective components for their intended purposes" ([0012]).
[FIG. 3: Parallel component extraction paths 311 and 321 for feature and global data]
[FIG. 6: System architecture with image pre-processor 603 and deep learning network 605]](https://pbs.twimg.com/media/Gxr0x-6bwAA5lSr.jpg)
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