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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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News
Tesla is improving Giga Berlin’s free “Giga Train” service for employees
With this initiative, Tesla aims to boost the number of Gigafactory Berlin employees commuting by rail while keeping the shuttle free for all riders.
Tesla will expand its factory shuttle service in Germany beginning January 4, adding direct rail trips from Berlin Ostbahnhof to Giga Berlin-Brandenburg in Grünheide.
With this initiative, Tesla aims to boost the number of Gigafactory Berlin employees commuting by rail while keeping the shuttle free for all riders.
New shuttle route
As noted in a report from rbb24, the updated service, which will start January 4, will run between the Berlin Ostbahnhof East Station and the Erkner Station at the Gigafactory Berlin complex. Tesla stated that the timetable mirrors shift changes for the facility’s employees, and similar to before, the service will be completely free. The train will offer six direct trips per day as well.
“The service includes six daily trips, which also cover our shift times. The trains will run between Berlin Ostbahnhof (with a stop at Ostkreuz) and Erkner station to the Gigafactory,” Tesla Germany stated.
Even with construction continuing at Fangschleuse and Köpenick stations, the company said the route has been optimized to maintain a predictable 35-minute travel time. The update follows earlier phases of Tesla’s “Giga Train” program, which initially connected Erkner to the factory grounds before expanding to Berlin-Lichtenberg.
-->Tesla pushes for majority rail commuting
Tesla began production at Grünheide in March 2022, and the factory’s workforce has since grown to around 11,500 employees, with an estimated 60% commuting from Berlin. The facility produces the Model Y, Tesla’s best-selling vehicle, for both Germany and other territories.
The company has repeatedly emphasized its goal of having more than half its staff use public transportation rather than cars, positioning the shuttle as a key part of that initiative. In keeping with the factory’s sustainability focus, Tesla continues to allow even non-employees to ride the shuttle free of charge, making it a broader mobility option for the area.
News
Tesla Model 3 and Model Y dominate China’s real-world efficiency tests
The Tesla Model 3 posted 20.8 kWh/100 km while the Model Y followed closely at 21.8 kWh/100 km.
Tesla’s Model 3 and Model Y once again led the field in a new real-world energy-consumption test conducted by China’s Autohome, outperforming numerous rival electric vehicles in controlled conditions.
The results, which placed both Teslas in the top two spots, prompted Xiaomi CEO Lei Jun to acknowledge Tesla’s efficiency advantage while noting that his company’s vehicles will continue refining its own models to close the gap.
Tesla secures top efficiency results
Autohome’s evaluation placed all vehicles under identical conditions, such as a full 375-kg load, cabin temperature fixed at 24°C on automatic climate control, and a steady cruising speed of 120 km/h. In this environment, the Tesla Model 3 posted 20.8 kWh/100 km while the Model Y followed closely at 21.8 kWh/100 km, as noted in a Sina News report.
These figures positioned Tesla’s vehicles firmly at the top of the ranking and highlighted their continued leadership in long-range efficiency. The test also highlighted how drivetrain optimization, software management, and aerodynamic profiles remain key differentiators in high-speed, cold-weather scenarios where many electric cars struggle to maintain low consumption.
Xiaomi’s Lei Jun pledges to continue learning from Tesla
Following the results, Xiaomi CEO Lei Jun noted that the Xiaomi SU7 actually performed well overall but naturally consumed more energy due to its larger C-segment footprint and higher specification. He reiterated that factors such as size and weight contributed to the difference in real-world consumption compared to Tesla. Still, the executive noted that Xiaomi will continue to learn from the veteran EV maker.
“The Xiaomi SU7’s energy consumption performance is also very good; you can take a closer look. The fact that its test results are weaker than Tesla’s is partly due to objective reasons: the Xiaomi SU7 is a C-segment car, larger and with higher specifications, making it heavier and naturally increasing energy consumption. Of course, we will continue to learn from Tesla and further optimize its energy consumption performance!” Lei Jun wrote in a post on Weibo.
-->Lei Jun has repeatedly described Tesla as the global benchmark for EV efficiency, previously stating that Xiaomi may require three to five years to match its leadership. He has also been very supportive of FSD, even testing the system in the United States.
Elon Musk
Elon Musk reveals what will make Optimus’ ridiculous production targets feasible
Musk recent post suggests that Tesla has a plan to attain Optimus’ production goals.
Elon Musk subtly teased Tesla’s strategy to achieve Optimus’ insane production volume targets. The CEO has shared his predictions about Optimus’ volume, and they are so ambitious that one would mistake them for science fiction.
Musk’s recent post on X, however, suggests that Tesla has a plan to attain Optimus’ production goals.
The highest volume product
Elon Musk has been pretty clear about the idea of Optimus being Tesla’s highest-volume product. During the Tesla 2025 Annual Shareholder Meeting, Musk stated that the humanoid robot will see “the fastest production ramp of any product of any large complex manufactured product ever,” starting with a one-million-per-year line at the Fremont Factory.
Following this, Musk stated that Giga Texas will receive a 10 million-per-year unit Optimus line. But even at this level, the Optimus ramp is just beginning, as the production of the humanoid robot will only accelerate from there. At some point, the CEO stated that a Mars location could even have a 100 million-unit-per-year production line, resulting in up to a billion Optimus robots being produced per year.
Self-replication is key
During the weekend, Musk posted a short message that hinted at Tesla’s Optimus strategy. “Optimus will be the Von Neumann probe,” the CEO wrote in his post. This short comment suggests that Tesla will not be relying on traditional production systems to make Optimus. The company probably won’t even hire humans to produce the humanoid robot at one point. Instead, Optimus robots could simply produce other Optimus robots, allowing them to self-replicate.
-->The Von Neumann is a hypothetical self-replicating spacecraft proposed by the mathematician and physicist John von Neumann in the 1940s–1950s. The hypothetical machine in the concept would be able to travel to a new star system or location, land, mine, and extract raw materials from planets, asteroids, and moons as needed, use those materials to manufacture copies of itself, and launch the new copies toward other star systems.
If Optimus could pull off this ambitious target, the humanoid robot would indeed be the highest volume product ever created. It could, as Musk predicted, really change the world.
Tesla is improving Giga Berlin’s free “Giga Train” service for employees
Tesla Model 3 and Model Y dominate China’s real-world efficiency tests
