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Tesla's new data pipeline and deep learning patent paves way for quicker autonomous driving improvements

A Tesla Model 3 navigates around heavy traffic. (Credit: Scott Kubo/YouTube)

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Tesla’s Neural Net continues to improve and become more advanced on a daily basis, but it appears that the electric car maker is making sure that it will evolve at an even faster rate in the future. A recent patent, for example, would allow Tesla’s autonomous driving systems to work more efficiently, thanks to a new data pipeline focused on optimized image processing.

Tesla’s patent for “Data Pipeline and Deep Learning System for Autonomous Driving” was published on December 26. The idea behind the patent is to revolutionize and improve upon past deep learning systems that have been used for autonomous driving vehicles. In the past, these systems have used “captured sensor data” to retrieve information.

Tesla recognizes the need for new sensors when data becomes more complex. According to the electric car maker’s patent, there is “a need for a customized data pipeline that can maximize the signal information from the captured sensor data and provide a higher level of signal information to the deep learning network for deep learning analysis.”

(Credit: Tesla)

The system described in this patent would capture an image using any of the sensors or cameras on the vehicle. In this case, this would describe a high dynamic range camera, camera sensor, radar sensor, or ultrasonic sensor. The image would then be broken down through a “high-pass” or ‘lo-pass” filter and a series of processors would then decipher what the image means.

The flowchart below describes what the process of the vehicle learning the information would look like. “Receive Sensor Data” is the first portion of this process. Then, data will be broken down and pre-processed for the system to then begin its “Deep Learning Analysis.” The results will then be passed along to the vehicle’s Artificial Intelligence Processor to be utilized during vehicle control.

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Credit: U.S. Patent Office/Tesla

In another process, the series of information that is retrieved from these images will be compared to data compiled from other Tesla users on a global scale. This will alleviate concerns that drivers may have that the system could perform the wrong process when driving autonomously. The aim of the patent is to create a safe driving experience and improve upon the already solid performance of Tesla’s autonomous driving software, and do so in a process that is more efficient than before.

By using this process, Tesla is able to maintain as much resolution as possible from the images captured by its vehicles’ cameras and sensors. This then allows the Neural Network to more efficiently learn from the data packets that it is receiving. This allows the Neural Network to work with better images in a more efficient manner as well, which opens the doors to faster autonomous driving improvements. These efficiencies would work very well with the additional horsepower offered by Tesla’s Hardware 3 computer, which is specifically designed for full self-driving with built-in redundancies.

Building upon the foundation that Tesla has already laid down in terms of its Full Self Driving suite, the recent patent suggests that the company is now attempting to narrow down on the finer points of its software’s performance. The addition of this patent will not only create a safer driving experience for owners of Tesla vehicles but will bring the quickly approaching future of fully-autonomous vehicles even closer to completion.

The full text of Tesla’s new data pipeline and deep learning patent could be viewed here.

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Joey has been a journalist covering electric mobility at TESLARATI since August 2019. In his spare time, Joey is playing golf, watching MMA, or cheering on any of his favorite sports teams, including the Baltimore Ravens and Orioles, Miami Heat, Washington Capitals, and Penn State Nittany Lions. You can get in touch with joey at joey@teslarati.com. He is also on X @KlenderJoey. If you're looking for great Tesla accessories, check out shop.teslarati.com

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Ford is charging for a basic EV feature on the Mustang Mach-E

When ordering a new Ford Mustang Mach-E, you’ll now be hit with an additional fee for one basic EV feature: the frunk.

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Credit: Ford Motor Company

Ford is charging an additional fee for a basic EV feature on its Mustang Mach-E, its most popular electric vehicle offering.

Ford has shuttered its initial Model e program, but is venturing into a more controlled and refined effort, and it is abandoning the F-150 Lightning in favor of a new pickup that is currently under design, but appears to have some favorable features.

However, ordering a new Mustang Mach-E now comes with an additional fee for one basic EV feature: the frunk.

The frunk is the front trunk, and due to the lack of a large engine in the front of an electric vehicle, OEMs are able to offer additional storage space under the hood. There’s one problem, though, and that is that companies appear to be recognizing that they can remove it for free while offering the function for a fee.

Ford is charging $495 for the frunk.

Interestingly, the frunk size varies by vehicle, but the Mustang Mach-E features a 4.7 to 4.8 cubic-foot-sized frunk, which measures approximately 9 inches deep, 26 inches wide, and 14 inches high.

When the vehicle was first released, Ford marketed the frunk as the ultimate tailgating feature, showing it off as a perfect place to store and serve cold shrimp cocktail.

Ford Mach-E frunk is perfect for chowders and chicken wings, and we’re not even joking

It appears the decision to charge for what is a simple advantage of an EV is not going over well, as even Ford loyal customers say the frunk is a “basic expectation” of an EV. Without it, it seems as if fans feel the company is nickel-and-diming its customers.

It will be pretty interesting to see the Mach-E without a frunk, and while it should not be enough to turn people away from potentially buying the vehicle, it seems the decision to add an additional charge to include one will definitely annoy some customers.

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Tesla to improve one of its best features, coding shows

According to the update, Tesla will work on improving the headlights when coming into contact with highly reflective objects, including road signs, traffic signs, and street lights. Additionally, pixel-level dimming will happen in two stages, whereas it currently performs with just one, meaning on or off.

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Credit: @jojje167 on X

Tesla is looking to upgrade its Matrix Headlights, a unique and high-tech feature that is available on several of its vehicles. The headlights aim to maximize visibility for Tesla drivers while being considerate of oncoming traffic.

The Matrix Headlights Tesla offers utilize dimming of individual light pixels to ensure that visibility stays high for those behind the wheel, while also being considerate of other cars by decreasing the brightness in areas where other cars are traveling.

Here’s what they look like in action:

As you can see, the Matrix headlight system intentionally dims the area where oncoming cars would be impacted by high beams. This keeps visibility at a maximum for everyone on the road, including those who could be hit with bright lights in their eyes.

There are still a handful of complaints from owners, however, but Tesla appears to be looking to resolve these with the coming updates in a Software Version that is currently labeled 2026.2.xxx. The coding was spotted by X user BERKANT:

According to the update, Tesla will work on improving the headlights when coming into contact with highly reflective objects, including road signs, traffic signs, and street lights. Additionally, pixel-level dimming will happen in two stages, whereas it currently performs with just one, meaning on or off.

Finally, the new system will prevent the high beams from glaring back at the driver. The system is made to dim when it recognizes oncoming cars, but not necessarily objects that could produce glaring issues back at the driver.

Tesla’s revolutionary Matrix headlights are coming to the U.S.

This upgrade is software-focused, so there will not need to be any physical changes or upgrades made to Tesla vehicles that utilize the Matrix headlights currently.

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xAI’s Grok approved for Pentagon classified systems: report

Under the agreement, Grok can be deployed in systems handling classified intelligence analysis, weapons development, and battlefield operations. 

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Credit: xAI

Elon Musk’s xAI has signed an agreement with the United States Department of Defense (DoD) to allow Grok to be used in classified military systems.

Previously, Anthropic’s Claude had been the only AI system approved for the most sensitive military work, but a dispute over usage safeguards has reportedly prompted the Pentagon to broaden its options, as noted in a report from Axios.

Under the agreement, Grok can be deployed in systems handling classified intelligence analysis, weapons development, and battlefield operations. 

The publication reported that xAI agreed to the Pentagon’s requirement that its technology be usable for “all lawful purposes,” a standard Anthropic has reportedly resisted due to alleged ethical restrictions tied to mass surveillance and autonomous weapons use.

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Defense Secretary Pete Hegseth is scheduled to meet with Anthropic CEO Dario Amodei in what sources expect to be a tense meeting, with the publication hinting that the Pentagon could designate Anthropic a “supply chain risk” if the company does not lift its safeguards. 

Axios stated that replacing Claude fully might be technically challenging even if xAI or other alternative AI systems take its place. That being said, other AI systems are already in use by the DoD. 

Grok already operates in the Pentagon’s unclassified systems alongside Google’s Gemini and OpenAI’s ChatGPT. Google is reportedly close to an agreement that will result in Gemini being used for classified use, while OpenAI’s progress toward classified deployment is described as slower but still feasible. 

The publication noted that the Pentagon continues talks with several AI companies as it prepares for potential changes in classified AI sourcing.

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