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Tesla is patenting a clever way to train Autopilot with augmented camera images
Tesla is currently tackling what could only be described as its biggest challenge to date. In his Master Plan, Part Deux, CEO Elon Musk envisioned a fleet of zero-emissions vehicles that are capable of driving on their own. Tesla has made steps towards this goal with improvements and refinements to its Autopilot and Full Self-Driving suites, but a lot of work remains to be done.
As noted by Tesla during its Autonomy Day presentation last year, attaining Full Self-Driving is largely a matter of training the neural networks used by the company. Tesla adopts what could be described as a somewhat organic approach for autonomy, with the company using a system that is centered on cameras and artificial intelligence — the equivalent of a human primarily using the eyes and brain to drive.
Tesla’s camera-centric approach may be quite controversial due to Elon Musk’s strong stance against LiDAR, but it is gaining ground, with other autonomous vehicle companies such as MobilEye developing FSD systems that rely primarily on visual data and a trained neural network. This approach does come with its challenges, as training neural networks requires tons of data. Tesla emphasized this point as much during its Autonomy Day presentation.
With this in mind, it is pertinent for the electric car maker to train its neural networks in a way that is as efficient as possible with zero compromises. To help accomplish this, Tesla seems to be looking into the utilization of augmented data, as described in a recently published patent titled “Systems and Methods for Training Machine Models with Augmented Data.”
Teslas are equipped with a suite of cameras that provide 360-degree visual coverage for the vehicle. In the patent’s description, Tesla noted that images used for neural network training are usually captured by various sensors, which, at times, have different characteristics. An example of this may lie in a Tesla’s three forward-facing cameras, each of which has a different field of view and range as the other two.
Tesla’s recent patent describes a system that allows the company to process these images in an optimized manner. Part of how this is done is through augmentation, which opens the doors to flexible and widespread neural network training, even when it involves vehicles equipped with differently-specced cameras. The electric car maker describes this process as such:
“Augmentation may provide generalization and greater robustness to the model prediction, particularly when images are clouded, occluded, or otherwise do not provide clear views of the detectable objects. These approaches may be particularly useful for object detection and in autonomous vehicles. This approach may also be beneficial for other situations in which the same camera configurations may be deployed to many devices. Since these devices may have a consistent set of sensors in a consistent orientation, the training data may be collected with a given configuration, a model may be trained with augmented data from the collected training data, and the trained model may be deployed to devices having the same configuration.”
Among the most notable aspects of Tesla’s recent patent is the use of “cutouts,” which allow Tesla’s neural networks to be trained using an optimized set of images. This was something that was discussed by former Tesla Autopilot engineer Eshak Mir in a Third Row Podcast interview, where he hinted at a system adopted in the electric car maker’s ongoing Autopilot rewrite that helped lay out “all the camera images” from a vehicle “into one view.” Such a process has the potential to help Tesla with 3D labeling, especially since the images used for neural network training are stitched together. Tesla’s patent seems to reference a system that is very similar to that described by the former Autopilot engineer.
“As a further example, the images may be augmented with a“cutout” function that removes a portion of the original image. The removed portion of the image may then be replaced with other image content, such as a specified color, blur, noise, or from another image. The number, size, region, and replacement content for cutouts may be varied and may be based on the label of the image (e.g., the region of interest in the image, or a bounding box for an object).”
Tesla is aiming to release a feature-complete version of its Full Self-Driving suite as soon as possible. Elon Musk remains optimistic about this, despite the company missing its initial timeline that was set at the end of 2019. That being said, Elon Musk did mention previously that Tesla is working on a foundational rewrite of Autopilot. In a tweet early last month, Musk stated that an essential part of the rewrite involves work on Autopilot’s core foundation code and 3D labeling. Once done, the CEO indicated that additional functionalities could be rolled out quickly. This recent patent, if any, seems to give a glimpse at how these improvements are being done.
Investor's Corner
Tesla has a Cybercab problem nobody saw coming
Tesla’s Cybercab line now builds robotaxis faster than Tesla can deploy them, Lars Moravy says.
Tesla’s Cybercab line in Texas can now build the two seat robotaxi faster than the company can put it into service, according to Lars Moravy, Tesla’s Vice President of Vehicle Engineering. “We don’t run every day because the line can run so fast. It’s currently outpacing our deployments,” Moravy said in a new Cybercab deep dive from Munro Live. “So, it’s a bit of a race right now. Who’s going to go faster? Right now the manufacturing line’s ahead.”
Tesla’s official Robotaxi account shared a Cybercab analysis video on X on Monday, calling it a “Cybercab engineering deep dive.” Moravy’s comments go further than what he told analysts on the Q3 earnings call last week, when he said the Cybercab line’s listed capacity of more than 125,000 units a year undersells what it can actually run.
The tour shows where that speed comes from. Tesla’s unboxed process splits the Cybercab into front, rear, side and center modules that are built and tested on their own before they meet on a short final line. That final line has just 25 fully automated stations, which Tesla’s team estimated is a fifth to a tenth of what a Model Y needs. The full line is about 78% automated, the framing line is already at 100%, and the layout uses roughly 50% less floor space per vehicle. The stated goal is “100% automation in 10 second cycle time,” or one finished Cybercab every 10 seconds.
Tesla also deleted the paint shop. Only the side modules are coated, and the exterior panels get their color inside the molding machine. Engineers said the car is designed to run at least 500,000 miles, which matters for a vehicle that will spend most of its day carrying riders.
The battery got the same treatment. The Cybercab uses an 88S6P pack of 4680 cells in a steel enclosure, a first for Tesla, with each module potted separately so a single failed module can be swapped instead of the whole pack. Wireless battery management boards replace a wiring harness, and new contactors are fast enough to drop the pyro fuse entirely. Tesla’s battery director graded the pack an A minus, compared with a D for the Model Y structural pack and a B minus for Cybertruck. Up front, the drive unit runs an 11:1 gear ratio versus roughly 9:1 on most Teslas, since the Cybercab does not need a high top speed.
Cybercab engineering deep dive — Robotaxi (@robotaxi) October 26, 2026
The deployment side is now the bottleneck Moravy described. Tesla had more than 50 Cybercabs working the Formula 1 weekend in Austin, and Texas records show 463 Cybercabs registered. Tesla said on its earnings call that Cybercab service will launch in Dallas next month, and Moravy said the company is aiming to get Cybercabs to customers by the end of 2026. Every unit built so far has gone into Tesla’s own fleet since the line moved into volume production in April.
Munro Live also asked when it could get a Cybercab to tear down. Tesla’s answer: “Not yet.”
SpaceX’s Raptor engine family has steadily increased sea-level thrust while simultaneously reducing mass, turning iterative engineering gains into substantially higher performance for the Super Heavy booster. CEO Elon Musk shed more light on how powerful Raptor 4 will be, and it’s pretty crazy.
Raptor 1 delivered 185 metric tons of thrust at roughly 2,080 kilograms, while Raptor 2 raised that to 230 tons while cutting mass to about 1,630 kilograms. SpaceX data for Raptor 3 lists a target of 280 tons at 1,525 kilograms (with further reductions in associated vehicle hardware), while operational flight ratings have been cited near 250 tons.
This morning, Elon Musk stated that Raptor 4 is expected to reach 300 tons of sea-level thrust at roughly the same mass as Raptor 3. Across these versions, thrust has risen by more than 60 percent from the first engines while engine mass has fallen, producing a sharp improvement in thrust-to-weight ratio. This is a true testament to how great SpaceX engineering has been:
🚀 Elon says Raptor 4 will make 300 tons of thrust at sea level at roughly the same mass as Raptor 3.
How far Raptor has come:
Raptor 1: 185 tf, 2,080 kg
Raptor 2: 230 tf, 1,630 kg
Raptor 3: 250 tf, 1,525 kg
Raptor 4: 300 tf33 on Super Heavy = ~9,900 tf, nearly 3x Saturn V https://t.co/HQDswZhyvL
— TESLARATI (@Teslarati) October 26, 2026
With 33 engines on SuperHeavy, the jump from earlier versions to a 300-ton Raptor 4 would produce roughly 9,900 metric tons of total liftoff thrust, nearly three times the Saturn V’s approximately 3,400 metric tons from its five F-1 engines.
This is not just a larger number on a data sheet, either, as sea-level thrust is the force available the moment the vehicle leaves the pad. This is when it is heaviest and still deep in Earth’s gravity well and dense atmosphere.
Higher levels of sea-level thrust directly improves the initial thrust-to-weight ratio of the full stack. A higher ratio produces faster acceleration off the pad, which shortens the time the rocket has to spend fighting gravity, and gravity losses during the first minute or two of the flight are a major drain on total available delta-v. Every second shaved off of that early climb preserves more propellant energy for reaching orbital velocity or for carrying extra payload.
Because Super Heavy’s engine count is constrained by vehicle diameter and packing density, increasing thrust per engine is the most straightforward way to raise total liftoff performance without redesigning the booster structure or design.
The same-mass target for Raptor 4 is equally important. Holding engine mass roughly constant while raising thrust improves the engine’s own thrust-to-weight ratio and reduces the structural and propellant mass fraction that must be carried solely to support the engines.
Earlier Raptor iterations also simplified or eliminated extensive vehicle-side hardware such as heat shields. These gains compound: more force for less installed mass leaves additional capacity for propellant, payload, or operational margins such as engine-out capability.
For Starship’s architecture, the practical effects are large. Greater liftoff thrust supports heavier vehicles, larger propellant loads, and higher payloads to low Earth orbit, figures SpaceX has targeted in the 100-plus-ton reusable range. That capacity underpins orbital refueling, lunar missions, and eventual Mars cargo and crew flights.
Continued growth in sea-level thrust therefore tightens the gap between theoretical rocket performance and the high flight rates and reusability needed to make large-scale space transport practical.
Tesla has expanded its Tesla Assisted Driving (TAD) suite to its tenth European country, as Latvia joined a growing list of European Union member states authorizing the company’s advanced driver assistance technology.
Tesla Europe announced this morning that the system is now approved in Latvia, with a software rollout to begin soon.
The approval and rollout follow recognition of a provisional European type approval originally granted by the Netherlands’ RDW authority on April 10.
🚨 Latvia has just received approval for Tesla Full Self-Driving (Supervised)
This is the 10th country in the EU to gain approval:
1. Netherlands — 10 April 2026 (original RDW approval)
2. Lithuania — 20 May 2026
3. Estonia — 29 May 2026
4. Denmark — 9 June 2026
5. Belgium — 10 June 2026
6. Slovenia — 7 September 2026
7. Czechia — 21 September 2026
8. Croatia — 29 September 2026
9. Slovakia — 16 October 2026— TESLARATI (@Teslarati) October 26, 2026
Latvia’s Road Traffic Safety Directorate (CSDD) evaluated the Dutch documentation, coordinated with the other national authorities, and reviewed updates from Tesla. These included tightened limits on speed offsets, automatic resets of any manual speed adjustments after each trip, and stronger driver monitoring.
In Europe, the feature is marketed as Tesla Assisted Driving after a recent rule change that labeled the name “Full Self-Driving” as misleading.
The system remains an SAE Level 2 driver assistance tool, not unsupervised autonomy. It can control speed, steering, braking, lane changes, and intersections while following a navigation route. Drivers need not keep their hands on the wheel at all times, yet they must continuously monitor the road and be prepared to intervene immediately.
The sequence of EU recognitions began with the Netherlands in April and continued through Lithuania, Estonia, Denmark, Belgium, Slovenia, Czechia, Croatia, and Slovakia. Most have relied on mutual recognition of the single Dutch provisional approval rather than independent national testing.
These national clearances do not yet constitute bloc-wide authorization. An EU-wide decision requires a qualified majority in the Technical Committee on Motor Vehicles: at least 15 of 27 member states representing 65 percent of the EU population. Larger markets, like Germany, France, and Italy, remain under review, and a vote is expected this December.
However, for Latvian Tesla owners with compatible vehicles and software, the imminent rollout expands access to the supervised system while underscoring that attentive human oversight remains essential.
Tesla has a Cybercab problem nobody saw coming
How SpaceX Is raising Raptor’s sea-level thrust while holding engine mass steady
