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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 FSD (Supervised) fleet passes 8.4 billion cumulative miles
Tesla’s Full Self-Driving (Supervised) system has now surpassed 8.4 billion cumulative miles.
The figure appears on Tesla’s official safety page, which tracks performance data for FSD (Supervised) and other safety technologies.
Tesla has long emphasized that large-scale real-world data is central to improving its neural network-based approach to autonomy. Each mile driven with FSD (Supervised) engaged contributes additional edge cases and scenario training for the system.
The milestone also brings Tesla closer to a benchmark previously outlined by CEO Elon Musk. Musk has stated that roughly 10 billion miles of training data may be needed to achieve safe unsupervised self-driving at scale, citing the “long tail” of rare but complex driving situations that must be learned through experience.
The growth curve of FSD Supervised’s cumulative miles over the past five years has been notable.
As noted in data shared by Tesla watcher Sawyer Merritt, annual FSD (Supervised) miles have increased from roughly 6 million in 2021 to 80 million in 2022, 670 million in 2023, 2.25 billion in 2024, and 4.25 billion in 2025. In just the first 50 days of 2026, Tesla owners logged another 1 billion miles.
At the current pace, the fleet is trending towards hitting about 10 billion FSD Supervised miles this year. The increase has been driven by Tesla’s growing vehicle fleet, periodic free trials, and expanding Robotaxi operations, among others.
With the fleet now past 8.4 billion cumulative miles, Tesla’s supervised system is approaching that threshold, even as regulatory approval for fully unsupervised deployment remains subject to further validation and oversight.
Tesla’s Full Self-Driving (Supervised) system has now surpassed 8.4 billion cumulative miles.
The figure appears on Tesla’s official safety page, which tracks performance data for FSD (Supervised) and other safety technologies.
Tesla has long emphasized that large-scale real-world data is central to improving its neural network-based approach to autonomy. Each mile driven with FSD (Supervised) engaged contributes additional edge cases and scenario training for the system.
The milestone also brings Tesla closer to a benchmark previously outlined by CEO Elon Musk. Musk has stated that roughly 10 billion miles of training data may be needed to achieve safe unsupervised self-driving at scale, citing the “long tail” of rare but complex driving situations that must be learned through experience.
The growth curve of FSD Supervised’s cumulative miles over the past five years has been notable.
As noted in data shared by Tesla watcher Sawyer Merritt, annual FSD (Supervised) miles have increased from roughly 6 million in 2021 to 80 million in 2022, 670 million in 2023, 2.25 billion in 2024, and 4.25 billion in 2025. In just the first 50 days of 2026, Tesla owners logged another 1 billion miles.
At the current pace, the fleet is trending towards hitting about 10 billion FSD Supervised miles this year. The increase has been driven by Tesla’s growing vehicle fleet, periodic free trials, and expanding Robotaxi operations, among others.
With the fleet now past 8.4 billion cumulative miles, Tesla’s supervised system is approaching that threshold, even as regulatory approval for fully unsupervised deployment remains subject to further validation and oversight.
Elon Musk
Elon Musk fires back after Wikipedia co-founder claims neutrality and dubs Grokipedia “ridiculous”
Musk’s response to Wales’ comments, which were posted on social media platform X, was short and direct: “Famous last words.”
Elon Musk fired back at Wikipedia co-founder Jimmy Wales after the longtime online encyclopedia leader dismissed xAI’s new AI-powered alternative, Grokipedia, as a “ridiculous” idea that is bound to fail.
Musk’s response to Wales’ comments, which were posted on social media platform X, was short and direct: “Famous last words.”
Wales made the comments while answering questions about Wikipedia’s neutrality. According to Wales, Wikipedia prides itself on neutrality.
“One of our core values at Wikipedia is neutrality. A neutral point of view is non-negotiable. It’s in the community, unquestioned… The idea that we’ve become somehow ‘Wokepidea’ is just not true,” Wales said.
When asked about potential competition from Grokipedia, Wales downplayed the situation. “There is no competition. I don’t know if anyone uses Grokipedia. I think it is a ridiculous idea that will never work,” Wales wrote.
After Grokipedia went live, Larry Sanger, also a co-founder of Wikipedia, wrote on X that his initial impression of the AI-powered Wikipedia alternative was “very OK.”
“My initial impression, looking at my own article and poking around here and there, is that Grokipedia is very OK. The jury’s still out as to whether it’s actually better than Wikipedia. But at this point I would have to say ‘maybe!’” Sanger stated.
Musk responded to Sanger’s assessment by saying it was “accurate.” In a separate post, he added that even in its V0.1 form, Grokipedia was already better than Wikipedia.
During a past appearance on the Tucker Carlson Show, Sanger argued that Wikipedia has drifted from its original vision, citing concerns about how its “Reliable sources/Perennial sources” framework categorizes publications by perceived credibility. As per Sanger, Wikipedia’s “Reliable sources/Perennial sources” list leans heavily left, with conservative publications getting effectively blacklisted in favor of their more liberal counterparts.
As of writing, Grokipedia has reportedly surpassed 80% of English Wikipedia’s article count.
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Tesla Sweden appeals after grid company refuses to restore existing Supercharger due to union strike
The charging site was previously functioning before it was temporarily disconnected in April last year for electrical safety reasons.
Tesla Sweden is seeking regulatory intervention after a Swedish power grid company refused to reconnect an already operational Supercharger station in Åre due to ongoing union sympathy actions.
The charging site was previously functioning before it was temporarily disconnected in April last year for electrical safety reasons. A temporary construction power cabinet supplying the station had fallen over, described by Tesla as occurring “under unclear circumstances.” The power was then cut at the request of Tesla’s installation contractor to allow safe repair work.
While the safety issue was resolved, the station has not been brought back online. Stefan Sedin, CEO of Jämtkraft elnät, told Dagens Arbete (DA) that power will not be restored to the existing Supercharger station as long as the electric vehicle maker’s union issues are ongoing.
“One of our installers noticed that the construction power had been backed up and was on the ground. We asked Tesla to fix the system, and their installation company in turn asked us to cut the power so that they could do the work safely.
“When everything was restored, the question arose: ‘Wait a minute, can we reconnect the station to the electricity grid? Or what does the notice actually say?’ We consulted with our employer organization, who were clear that as long as sympathy measures are in place, we cannot reconnect this facility,” Sedin said.
The union’s sympathy actions, which began in March 2024, apply to work involving “planning, preparation, new connections, grid expansion, service, maintenance and repairs” of Tesla’s charging infrastructure in Sweden.
Tesla Sweden has argued that reconnecting an existing facility is not equivalent to establishing a new grid connection. In a filing to the Swedish Energy Market Inspectorate, the company stated that reconnecting the installation “is therefore not covered by the sympathy measures and cannot therefore constitute a reason for not reconnecting the facility to the electricity grid.”
Sedin, for his part, noted that Tesla’s issue with the Supercharger is quite unique. And while Jämtkraft elnät itself has no issue with Tesla, its actions are based on the unions’ sympathy measures against the electric vehicle maker.
“This is absolutely the first time that I have been involved in matters relating to union conflicts or sympathy measures. That is why we have relied entirely on the assessment of our employer organization. This is not something that we have made any decisions about ourselves at all.
“It is not that Jämtkraft elnät has a conflict with Tesla, but our actions are based on these sympathy measures. Should it turn out that we have made an incorrect assessment, we will correct ourselves. It is no more difficult than that for us,” the executive said.
Tesla FSD (Supervised) fleet passes 8.4 billion cumulative miles
Elon Musk fires back after Wikipedia co-founder claims neutrality and dubs Grokipedia “ridiculous”
