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Tesla is patenting a clever way to train Autopilot with augmented camera images

Tesla Autopilot construction zone lane (Credit: YouTube/Cf Tesla)

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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.”

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A block diagram of an environment for computer model training. (Credit: Patentscope.wipo.int)

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).”

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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.

Simon is an experienced automotive reporter with a passion for electric cars and clean energy. Fascinated by the world envisioned by Elon Musk, he hopes to make it to Mars (at least as a tourist) someday. For stories or tips--or even to just say a simple hello--send a message to his email, simon@teslarati.com or his handle on X, @ResidentSponge.

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The secret behind Tesla’s Cybercab Gold goes well beyond just the color

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Tesla has spent years trying to engineer its way out of the automotive paint shop, one of the most expensive, space-consuming, and environmentally costly steps in vehicle manufacturing. With the Cybercab, Tesla confirmed on X this week that a new reaction injection molding process will embed color directly into the panel itself during production.

“Our new reaction injection molding (RIM) process shrinks Cybercab paint cycles from hours to minutes. This cuts those parts’ manufacturing and supply chain emissions by 35% and eliminating 100% of paint volatile organic compounds (VOCs) emitted in traditional paint methods.” noted Tesla.

While the RIM process isn’t necessarily new and has existed since the 1960s, what makes Tesla’s application notable is how it is being used specifically for exterior body panels that traditionally required a separate paint process after forming.

Tesla Cybercab stands to gain from new Trump autonomy rules

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Tesla’s RIM approach integrates the color directly into the panel material during the molding process itself. The pigment is part of the polymer mix injected into the mold, meaning the panel comes out of the mold already colored, with no separate paint application required. The clear coat or protective layer can be applied at the mold stage or through a much faster post-process than traditional multi-stage painting. Tesla claims this compresses what was a multi-hour paint cycle into minutes per panel.

Tesla’s obsession with killing the paint shop is one of the most consistent threads running through the company’s manufacturing philosophy going back years. As far back as 2018, Musk was trimming paint color options to simplify production, tweeting at the time: “Moving 2 of 7 Tesla colors off menu on Wednesday to simplify manufacturing.” Two years later, in a 2020 Automotive News interview, Musk laid out his broader vision, saying he believed Tesla factories could one day be 1,000 times more efficient than conventional plants, and pointing to the paint shop as one of the biggest sources of waste, cost, and complexity. The Cybertruck was the most extreme expression of that thinking. Tesla chose an unpainted stainless steel exterior partly because it would eliminate the need for a $200 million paint facility at Gigafactory Texas. The stainless approach proved harder and more expensive than anticipated, but the underlying ambition never changed. The Cybercab is what happens when that same ambition meets a manufacturing process that delivers on it.

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Tesla app update makes Robotaxi ownership make a lot more sense

Tesla’s app now shows a live indicator when your car is actively driving itself.

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A recent Tesla app update, released last week  (4.58.5), gives visibility on whether a vehicle is navigating in its semi-autonomous mode or being drive by a human driver. The updated app now displays a live “Self-Driving” indicator in bright blue text directly beneath the vehicle’s speed readout whenever Full Self-Driving is actively engaged, along with the signature glowing blue navigation path that FSD users see on the main touchscreen. It is a small visual update with meaningful implications for how Tesla owners monitor their vehicles remotely.

The feature was first spotted in the wild by X user Jordan Camina, who shared video of a Hardware 3 Model S displaying the new animation through the app while driving. That detail is significant because it confirms the update is not limited to newer HW4 vehicles. It works across hardware generations, and Tesla confirmed it will eventually support all vehicles regardless of chip platform once both the app and vehicle software are updated. The vehicle side requires software version 2026.20.6.1, which has reached nearly 40% of the fleet so far, as monitored by NotaTeslaApp.

The feature makes the most practical sense when viewed through the lens of Tesla’s expanding robotaxi operation. In a robotaxi context, the owner of a vehicle generating ride revenue has a direct financial and safety interest in knowing whether their car is operating under autonomous control at any given moment. The app’s new FSD indicator gives fleet owners exactly that visibility, the same way a logistics company monitors whether a delivery driver is following the planned route. It also carries implications for Tesla’s insurance model. Tesla’s own insurance product prices premiums in part based on FSD engagement rates, and real-time visibility into when FSD is active creates a feedback loop that could eventually tie directly into policy pricing. For individual owners who have opted their personal vehicles into the robotaxi network, the update effectively turns the Tesla app into a fleet management dashboard, one that tells you whether your car is earning money, whether it is driving itself to do it, and whether everything is operating the way it should from wherever you happen to be.

Tesla expands Robotaxi to Florida, marking its third state for autonomy

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As Teslarati has reported, Tesla launched unsupervised robotaxi rides in Miami this summer, a milestone that makes a remote FSD status indicator significantly more practical than a cosmetic feature. When a vehicle is operating as a robotaxi without a driver present, the owner or fleet operator needs a reliable way to confirm autonomy is engaged. The app now provides exactly that.

As noted by NotATeslaApp, The update also arrived alongside a hint buried in the same app version that Tesla plans to use the cabin camera to verify driver identity before FSD can be activated. Pairing identity verification with a live autonomy status indicator points toward the infrastructure Tesla is building for a fleet of driverless vehicles that owners can monitor the way you would track a package delivery.

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California snubs Tesla in its newly passed EV incentive that favors Rivian and Lucid

California passed a $135 million EV incentive that rewards Rivian and Lucid while sidelining Tesla

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California just drew a line in the EV incentive sand to put Tesla on the wrong side of it. The state recently passed a $135 million program offering first-time electric vehicle buyers a direct incentive with no application required, but the rules were written in a way that leaves Tesla at a structural disadvantage compared to Rivian and Lucid.

The program caps eligible vehicles at $50,000 for new EVs and $25,000 for used ones. That pricing threshold rules out a significant portion of Tesla’s lineup, though some lower-priced Model 3 and Model Y configurations would still qualify. California-based automakers are exempt from the price cap entirely, regardless of what their vehicles cost. Rivian, headquartered in Irvine, and Lucid, based in the San Francisco Bay Area, both benefit from that exemption. Rivian’s R2 starts at roughly $45,000 but has versions above the cap. Lucid’s Air and Gravity start at $70,990 and $79,990 respectively, well above any threshold a non-California company would face.

California hits Tesla Cybercab and Robotaxi driverless cars with new law

Tesla built its reputation and a significant portion of its early market share in California, where EV adoption has consistently led the nation. The company operates its original factory in Fremont, California, and the state was home to Tesla’s headquarters for most of its existence. That changed in 2021 when Tesla moved its corporate headquarters to Austin, Texas. Since then, the relationship between the company and California Governor Gavin Newsom has been openly adversarial, with Musk and Newsom trading public criticism on multiple occasions.

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California’s EV incentive landscape has shifted repeatedly in recent years, and Tesla has previously lost eligibility for state-level programs as its vehicles exceeded income-adjusted price thresholds. The federal $7,500 EV tax credit, which Tesla models have qualified for and lost depending on policy cycles, is no longer available after it expired without renewal, making state-level programs more meaningful to buyers than they have been in years.

The practical impact for buyers is more nuanced than the headline suggests. California residents purchasing a Tesla under $50,000 for the first time can still access the incentive. But the exemption written for California-based manufacturers is a structural advantage that rewards where a company plants its headquarters flag rather than where it builds its products, and Tesla moved that flag to Texas.

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