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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.
Tesla just trademarked ‘MEGAPOD’ with the United States Patent and Trademark Office (USPTO), its latest move in what seems to be a hint that the company is incredibly focused on its AI efforts and storage needs as compute increases.
The application carries serial number 99893717 and lists the applicant as Tesla, Inc., located at 1 Tesla Road, Austin, Texas 78725.
The filing remains in ‘live pending’ status, and it is a new application waiting for assignment to an examining attorney. It has not yet been published or registered.
Tesla just trademarked MEGAPOD
Summary:
“Modular data center hardware systems for artificial intelligence computing, comprised of computer servers, computer hardware for artificial intelligence processing, computer networking hardware, electrical power distribution units, and… pic.twitter.com/3l85DsKadl— Robin (@xdNiBoR) June 19, 2026
According to the official goods and services description in the application, Tesla describes ‘MEGAPOD’ as:
“Modular data center hardware systems for artificial intelligence computing, comprised of computer servers, computer hardware for artificial intelligence processing, computer networking hardware, electrical power distribution units, and cooling systems, sold as a unit; self-contained modular computing hardware systems for artificial intelligence workloads; integrated computer hardware platforms for artificial intelligence computing, namely, enclosures containing computer hardware, power distribution hardware, and cooling hardware, sold as a unit; downloadable software for monitoring, managing, optimizing, and regulating modular artificial intelligence computing hardware systems.”
This description specifies complete, self-contained modular units that integrate servers and specialized AI processing hardware with networking components, power distribution, and cooling systems. It also includes associated downloadable software for oversight and optimization of these systems. The language emphasizes hardware sold “as a unit” and enclosures that combine the necessary elements for AI computing workloads.
Tesla has an established history of developing and commercializing modular hardware systems. Its Megapack product line, for example, consists of utility-scale battery energy storage systems designed as containerized units for grid applications. The MEGAPOD filing follows a similar pattern of protecting a name for modular, integrated hardware platforms, this time focused on artificial intelligence computing infrastructure.
This could be an early move, especially as Tesla did not have trademark rights to the word ‘Cybercab,’ the name of its self-driving, ride-hailing-focused vehicle.
Trademark applications of this type allow companies to secure priority rights to a name for defined categories of goods and services. The USPTO examines applications for compliance with legal requirements, including distinctiveness and absence of conflicts with prior marks. If the application proceeds successfully through examination, publication, and any opposition period, it could result in a federal trademark registration providing nationwide protection. This is what Tesla’s obvious intention is with ‘MEGAPOD.’
Public reports and analysis suggest MEGAPOD could represent modular, container-style AI computing pods designed for easy deployment. These would bundle servers, AI accelerators, power systems, and cooling into self-contained units suitable for distributed AI workloads. This approach aligns with Tesla’s announced AI compute strategy.
In March 2026, Elon Musk outlined plans for “Digital Optimus” (also referred to as Macrohard), a joint Tesla-xAI project for AI agents capable of handling complex digital tasks. The plans include running these agents on Tesla’s AI4 hardware in parked vehicles as well as dedicated compute units installed at Supercharger stations, which collectively offer substantial unused electrical capacity.
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A modular hardware platform like the one described in the ‘MEGAPOD’ filing would support scalable, rapid deployment of such distributed compute resources. It could complement Tesla’s other AI infrastructure efforts, including the Dojo supercomputer used for training models and the development of AI systems for autonomous driving and robotics, by enabling edge or regional AI inference without reliance on traditional centralized data centers.
Investor's Corner
SpaceX is launching a secret spacecraft that could change how things are made in space
SpaceX’s secret disk-shaped Starfall capsule is targeting a market no reentry vehicle has cracked.
SpaceX is targeting Tuesday, June 23 for the first flight of Starfall, a reentry capsule the company has developed almost entirely in private. The Falcon 9 launch window opens at 6:43 a.m. ET from Space Launch Complex 40 at Cape Canaveral Space Force Station, with a backup window available the same time on June 24. SpaceX has made no public announcement about the vehicle, only providing launch details. Everything known about it has come through FAA and FCC regulatory filings.
What makes Starfall different starts with its shape. Rather than the traditional cone used by Dragon and every other cargo return capsule in operation, Starfall is a flat disk that measures roughly 10.2 feet (3.1 meters) wide and just 2.5 feet (0.75 meters) tall, and weighing 4,630 pounds (2,100 kg) and capable of returning up to 2,200 pounds (1,000 kilograms) of payload from orbit. The disk geometry maximizes structural efficiency and payload volume relative to mass, and the heat shield mechanically jettisons just before splashdown, allowing recovery teams to retrieve both the capsule and the shield separately from the Pacific Ocean.
The difference with Starfall from existing competitors, such as Varda Space Industries, which has largely built the orbital manufacturing market and returns heavy payloads per flight is that Starfall’s specification is roughly 30 times more per mission, and is designed to be mass-produced and launched on either Falcon 9 or Starship. That combination of volume and launch access is something no standalone startup can replicate, and it puts SpaceX in direct competition with the companies that currently pay it to reach orbit.
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The intended market is orbital manufacturing: pharmaceuticals, protein crystals, semiconductors, and advanced optical fiber that physically cannot be produced in the presence of gravity. FAA documents describe Starfall’s long-term purpose as building a “self-sustaining commercial in-space manufacturing market” and as a potential successor to the industrial capabilities of the International Space Station, which is set to retire in the late 2020s. Military rapid global cargo delivery is a parallel application under active discussion with the Pentagon.
The reason some industries seek manufacturing in space comes down to gravity. On Earth, gravity causes materials to settle, separate, and deform during production. In microgravity, those constraints disappear.
SpaceX’s already controls launch access, which means it currently functions as the landlord for every competitor in the orbital manufacturing return space. Starfall converts that landlord position into vertical ownership, and it would no longer just carry other companies’ capsules to orbit, but rather operate the capsule, own the return logistics, and capture the service revenue directly. Viewed alongside Starlink, Colossus, and the xAI merger, Starfall fits a consistent pattern: SpaceX identifying infrastructure layers that others depend on and moving to own them outright. Orbital manufacturing return is the next layer on that list.
If Tuesday’s reentry, parachute sequence, and recovery demonstration goes as planned, the second FAA-approved test flight follows. A successful pair of demos would position SpaceX to begin offering Starfall as a commercial service, likely first to pharmaceutical and materials science customers before scaling toward the military and broader manufacturing segments.
The Tesla Semi was spotted mounted with ground truth validation equipment as the company nears its looming launch. The Semi is Tesla’s Class 8 all-electric truck, and has been utilized in its earlier stages by many companies like PepsiCo. and Frito-Lay, who have been using it in a pilot program.
The Semi was spotted in Sunnyvale, California, and sports a typical ground truth validation unit that Tesla routinely uses on its vehicles. Ground truth validation is essentially the process of training supervised algorithms to ensure they can perform reliably. Tesla typically performs this on vehicles that are being released soon:
Spotted the new semi adorned with ground truthing equipment. Haven’t seen anyone post this so figured I’d share.
The future is autonomous!!@SawyerMerritt @wholemars pic.twitter.com/qkPDHPUQZ6
— Danny (@dannywinner1) June 21, 2026
The Semi being spotted with this type of validation rig is important because it means the company is working on solidifying a Full Self-Driving model for its commercial vehicle offering. This would be a massive development for not only Tesla but also the logistics industry as a whole.
There are strict regulations on driving hours for commercial truck drivers, and autonomy is a way to potentially combat these issues. FSD is already a widely effective way that owners of typical passenger vehicles take stress out of travel. Even launching a semi-autonomous platform for truck drivers to use to increase safety, reduce fatigue, and increase productivity would be a huge development.
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The Semi has already proven to be an ideal solution for companies that use commercial logistics. It has increased efficiency and reduced operating costs for many companies that have been able to use it in pilot programs.
There are expected to be some bumps along the way. Tesla saw some challenges with FSD on the Cybertruck, as it had never had a vehicle with cameras at that height, so some of the features with FSD were not immediately available. Just a week ago, Tesla launched Actually Smart Summon (ASS) for Cybertruck, nearly three years after the vehicle was first delivered to customers.
Tesla just trademarked MEGAPOD: here’s what it is
SpaceX is launching a secret spacecraft that could change how things are made in space
