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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 has expanded the operational geofence for its driverless Robotaxi service in Austin, Texas, marking the first such increase in some time. The updated Service Area for Robotaxi in Austin now spans about 288 square miles and is roughly 9 percent larger than the previous boundary.
This incremental growth adds approximately 24 square miles of coverage, bringing the prior zone of roughly 264 square miles into a broader footprint that better serves northern suburbs.
The expansion extends the geofence northward toward Pflugerville along the US 183 corridor, incorporating additional neighborhoods north of the Domain and areas such as Mesa Park. These additions include higher-end residential and commercial districts that previously sat just outside the allowed operating zone.
Riders can now request unsupervised trips that begin or end in these newly included locations, provided the entire route remains inside the digital boundary:
Tesla has just expanded its Robotaxi service area in Austin, Texas for the first time in 10 months.
The new service area is roughly 9% larger than the old one, and is about 288 square miles in total. pic.twitter.com/i4oBM1KfWS
— Sawyer Merritt (@SawyerMerritt) August 31, 2026
Tesla first launched public Robotaxi operations in Austin in mid-2025 with a modest initial zone of about 20 square miles. Subsequent enlargements in 2025 and early 2026 steadily grew the map until it covered much of the metropolitan area.
After the last major update roughly ten months earlier, the company held the boundary steady while it collected additional miles and refined the FSD suite.
The modest nine percent increase still matters for daily utility. Longer trips become possible, more residents gain access, and the fleet can accumulate more diverse real-world data across new road types and traffic patterns. Observers note that the added territory aligns with existing Tesla service infrastructure, which could support more efficient vehicle staging in the North end of Austin.
Although the geofence has grown, Tesla continues to operate a relatively small unsupervised fleet in the city. The company has emphasized safety and software readiness over rapid geographic scaling. This latest map update signals that Tesla remains committed to expanding Robotaxi availability in its home market as it prepares for further software improvements and potential Cybercab deployments.
The 288-square-mile zone now gives Austin riders one of the larger driverless service areas currently available in the United States.
Tesla’s 2026 Summer Update turns Grok from a talking companion into a true in-car controller, with considerably expanded capabilities. Where the assistant once answered questions and handled navigation, it can now operate hardware and software through ordinary speech, including several requests at the same time.
Grok has become incredibly robust over the past few quarters, and this new ability that Tesla has included with its Summer Update is perhaps the clearest sign of just how capable it has become.
You can issue many commands to Grok at once, and each will be taken care of: anything from headlight control to climate adjustments to mirror folding can now all be taken care of with a simple sentence spoken toward Grok:
This is cool, you can now give Grok a ton of commands at once in your Tesla and it’ll do them all together.
The commands I gave:
• Fold mirrors
• Turn on wipers to fastest setting
• Change temp to 65°
• Open self-driving app
• Open gloveboxComes with Tesla’s 2026 Summer… pic.twitter.com/drZxoHrknv
— Sawyer Merritt (@SawyerMerritt) August 26, 2026
Here’s another example I used in my Model Y:
🚨 Using Grok to control the cabin temperature and turn on headlights in 2026 Model Y on FSD v14.3.8 and 2026.26.6.5 pic.twitter.com/7clgQkGABk
— TESLARATI (@Teslarati) August 27, 2026
Tesla’s official Release Notes list the core powers of Grok’s new capabilities: Grok can place phone calls from a paired phone, search a streaming service and queue music, adjust climate, and search the Controls panel. Drivers can also ask it questions about the vehicle or the latest features included in a recent software update.
It can also take care of driving settings, dynamics, and other relevant preferences that deal with how the car operates and handles.
Tesla Summer Update begins rolling out: a look at the new features
It’s also more of a use case than a novelty feature. Grok was great for learning about something that was being pondered while Full Self-Driving was taking care of the major automotive responsibilities, but it did have some useful functionality when it came to navigation.
Another less-noted improvement is the swift transition that Grok has from thinking of its answer to giving you one. In the past, it would take a few seconds for Grok to truly come up with a valuable response. It now seems that it is improving, at least slightly.
Tesla Full Self-Driving is set to get a major fix with pothole avoidance, a feature that CEO Elon Musk said recently was “coming soon.” The feature has been included within the FSD v14 release notes for some time as an upcoming improvement.
Potholes come in all shapes and sizes, but one thing is universal about them, and that is the fact that every driver wants to avoid them. Driving into or over a pothole can cause tire and wheel damage, alignment issues, and even, in some more severe instances, injury to occupants or major damage to a vehicle in despair.
Tesla Cybercab uses a unique strategy for picking up the right rider
Musk said late last night in a post that pothole avoidance while utilizing the Full Self-Driving suite is “coming soon.”
Coming soon
— Elon Musk (@elonmusk) August 31, 2026
Full Self-Driving, even in its most recent and robust forms, still has its shortcomings. While the suite has performed exceptionally well with avoiding on-road obstacles like cardboard boxes, horse droppings, car parts, and other debris, it still seems to struggle with recognizing certain things.
Potholes and other abrupt changes in a road’s layout are things that FSD still tends to struggle with. Perhaps the suite’s 3D modeling is still in need of some additional data or some sort of script that will help it to avoid a potentially destructive pothole or bump in the road that would be easily recognizable by the human eye.
Musk has mentioned pothole avoidance for some time, with the initial idea coming in April 2019 when an owner first requested the feature. Musk said that Tesla would “definitely” develop pothole avoidance.
In August 2020, Musk said Tesla was in the process of labeling bumps and potholes so cars can either slow down or steer around them altogether.
Tesla to utilize micro maps for pothole-detection in future update
Avoiding major disturbances in the road is a necessary feature for Full Self-Driving to have, especially as Tesla is working toward a fully autonomous program that will eventually allow every occupant in the vehicle to sleep, work, or enjoy leisure time while traveling.
That truly begins this week with the launch of Cybercab on Thursday.
Tesla expands driverless Robotaxi geofence in Austin
Elon Musk’s Grok can basically control anything in your Tesla now
