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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.
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Tesla Full Self-Driving release in the EU gets delayed
Tesla Full Self-Driving’s release in Europe is set to be delayed by at least a few months.
The European Union will not vote on Tesla’s Full Self-Driving (Supervised) on October 6. The draft agenda for the 119th meeting of the Technical Committee on Motor Vehicles lists only a 25-minute “continuation of discussions” on the Netherlands’ Article 39 request, not a decision. The next scheduled TCMV session is in December, which is now the earliest date a bloc-wide vote could occur.
Tesla Europe had pointed to October 6 as a possible EU-wide vote after the Dutch vehicle authority RDW granted the first European type approval on April 10.
That approval, under UN Regulation 171 plus an Article 39 exemption in EU Regulation 2018/858, is the legal file other member states have been recognizing one by one. The same committee has already discussed the request twice without voting.
Elon Musk’s reply to the delay was a single word: “Sigh.”
Sigh
— Elon Musk (@elonmusk) September 25, 2026
Seven EU countries have now cleared FSD Supervised on their own roads: the Netherlands, Lithuania, Estonia, Denmark, Belgium, Slovenia, and Czechia. Those seven states represent about 53 million people, or roughly 12 percent of the EU population. An EU-wide authorization still needs a qualified majority: at least 15 of 27 member states representing 65 percent of the bloc’s population, about 292 million people.
Germany, France, Italy, and Spain remain the decisive markets. France has already rejected the current system; several other governments have flagged speed-limit compliance as the main sticking point.
The safety case Tesla is putting in front of those governments is now public. On September 1, Tesla Europe said FSD Supervised was in use by more than 70,000 customers, covering over 1 million kilometers a day, and was 4.1 times less likely to be involved in a crash than manual driving across 100 million kilometers on EU public roads.
An earlier mid-year cut of the same fleet data, covering 65 million kilometers in five approved countries, put the collision advantage at 5.2 times, with zero highway collisions over 41.9 million kilometers. Tesla also reported far fewer automatic emergency braking events, harsh accelerations, and hard swerves than in comparable manual Tesla driving. Those figures are company-reported, not independently audited.
Tesla Full Self-Driving is taking over Europe: fourth country gets FSD approval
The public-health backdrop is harder to dispute. European countries recorded about 19,400 road deaths in 2025, or roughly 53 a day, most of them attributed to human error. FSD Supervised is not unsupervised autonomy; the driver remains legally responsible. But the software is already legal and in daily use across seven member states.
Until TCMV votes, the rest of the EU remains a patchwork: available in Prague and Amsterdam, locked behind review in Paris and Berlin. December is now the next chance to close that gap.
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SpaceX readies Starship Flight 14 for a historic journey into uncharted territory
SpaceX finished Starship’s Flight 14 rehearsal, clearing the way for its first orbital flight Monday.
SpaceX has cleared one of the last hurdles before Starship’s first trip to orbit. The company posted on X Thursday afternoon that its launch rehearsal for Flight 14 was complete, keeping the mission on track for Monday, September 28. The launch window opens at 7:15 a.m. CT at Starbase, Texas, and runs for 75 minutes.
A wet dress rehearsal is essentially launch day without the launch. Crews fill Booster 21 and Ship 41 with thousands of tons of extremely cold propellant, run the countdown nearly to ignition, then drain everything back out. It lets engineers catch leaks or equipment problems before anything leaves the pad. SpaceX still needs a launch license from the FAA before the stack, which stands 407 feet tall, can fly.
Flight 14 matters because of where it is going. All 13 previous Starship flights followed a suborbital path, which works like throwing a ball extremely high and far: the vehicle reaches space, but it is always on a course that brings it back down within about an hour. This time, Ship 41 will perform a short engine firing called an orbital insertion burn roughly 25 minutes after liftoff, giving it enough speed to keep falling around Earth instead of back into it. SpaceX plans about six laps at an altitude near 275 kilometers (171 miles) over nearly 10 hours, as Teslarati detailed when the mission was first announced.
Launch rehearsal complete ahead of Starship Flight 14 pic.twitter.com/h5LBYyBqi4
— SpaceX (@SpaceX) September 24, 2026
Getting into orbit also means Starship has to prove it can get back out. The ship must relight a single Raptor engine in space to slow down for reentry. SpaceX says it will only attempt the orbital insertion burn after flight controllers confirm the hardware needed for that return burn has enough backup, and its flight plan includes health checks that could shorten the mission to two or five orbits.
Flight 14 is also the first to put working satellites into service. Flight 13 carried 20 Starlink V3 satellites in July, but they came back down with the ship because that mission never reached orbit. This time, 26 V3 satellites are meant to stay up and join the constellation within a few weeks. Together they add about 26 terabits per second of network capacity, which SpaceX says is roughly 10 times what a single Falcon 9 launch of older V2 Mini satellites adds. Three of them carry cameras that will photograph Starship’s heat shield in orbit to check for tile damage before reentry.
The hardware has changed too. Ship 41 flies with extra fasteners on tiles in the most vulnerable areas, fixes for gaps where superheated plasma slipped behind tiles, and curved tiles designed to reduce heating between them. Two tiles recovered from Ship 40 will fly again, the first reuse of any part of a Starship heat shield. Booster 21 carries better engine filtering and new relight software after ice clogged three center engines on the previous booster, leaving only eight of 13 engines to restart for its landing burn.
Ship 41 is targeting a splashdown in the Pacific Ocean west of Chile, a new recovery zone after several Indian Ocean landings, while Booster 21 aims for the Gulf. Neither will be caught by the tower on this flight. Elon Musk said in August that a ship catch was likely “in a few months.”
Elon Musk
Google just picked SpaceX for its first step into orbital AI
Google will launch its first Project Suncatcher AI satellite on SpaceX’s Transporter-18 rideshare next week.
Google is about to put its own AI chips into orbit for the first time, and it is paying SpaceX to get them there.
The company said Thursday that the first in-orbit test of Project Suncatcher, its research effort to find out whether space can host large-scale AI computing, will fly next week on SpaceX’s Transporter-18 rideshare mission.
The satellite, called MVP, is about the size of a refrigerator and carries four of Google’s Tensor Processing Units, the same chips Google runs in its ground data centers. Google originally planned to launch two custom satellites in 2027, but chose to move faster by integrating its chips into a satellite.
MVP’s solar panels supply about one kilowatt of power, and Google will run Gemini models on the TPUs only in bursts of roughly 15 minutes before the chips shut down so the radiators can shed heat. In a blog post, Google said its Trillium TPUs survived vibration testing that mimicked sustained launch loads of up to 10g, with individual components seeing 50 to 100g, and handled a radiation dose greater than a five year mission would deliver.
SpaceX and Google mull massive partnership on Musk’s orbital data dream: report
Next week’s flight, slated for October 1, follows a relationship that became public in May, when Teslarati reported that Google was in talks with SpaceX for a launch deal tied to orbital data centers. Google also holds a stake of roughly 6% in SpaceX.
The two companies are chasing the same idea from very different starting points. SpaceX’s own orbital compute program is built around the AI1 satellite, a roughly 70 meter structure derived from Starlink V3 hardware that is designed for 150 kW of peak compute, about 150 times the power MVP will draw. Elon Musk has brushed off concerns about crowding orbit with those satellites, and SpaceX is building its Gigasat factory in Bastrop, Texas, to produce them, targeting an annualized rate of about 1 GW of space compute by the end of 2027.
Musk also posted on X on Thursday that “the amount of compute in space will obviously round up to 100% of all compute.”
Google has been more cautious in public. Its research estimates that launch prices need to fall below about $200 per kilogram before an orbital data center can compete with a ground facility on energy cost, a threshold the company believes could be reached around the mid 2030s. The Suncatcher team has said it expects the effort to remain a project rather than a product for years, which leaves the first real test of its hardware riding on a rocket from the company with the most aggressive timeline in the field.