News
Tesla patents virtualization and machine learning software to improve FSD
Tesla has applied for a set of patents that are set to significantly improve virtualization, recognition, and Full Self Driving overall.
Tesla has worked tirelessly to improve full self-driving technology in the first two months of the year. Most recently, Tesla pushed its most significant improvement to employees, v11.3. Still, with new patented technology, the software is set to continue to improve dramatically this year. The two patents, focusing on virtualization and machine learning, appeared in the U.S. Patent Office database late last week.
The first patent, “Vision-Based Machine Learning Model for Autonomous Driving with Adjustable Virtual Camera,” is likely simply a reworking of a previous system but changed to fit Tesla’s new visual-only autonomous driving system. The second patent, “Vision-Based Machine Learning Model for Aggregation of Static Objects and Systems for Autonomous Driving,” focuses more on improving the virtualization seen on screen while in the vehicle.
The first patent’s abstract describes a system that looks similar to the one already available in Tesla vehicles but has been adapted to remove non-visual sensors. However, it does include an added “adjustable virtual camera,” potentially indicating that Tesla is working to give drivers more control of looking out of their car with the camera system or improved virtualization interaction.
“Systems and methods for a vision-based machine learning model for autonomous driving with adjustable virtual camera. An example method includes obtaining images from a multitude of image sensors positioned about a vehicle. Features associated with the images are determined, with the features being output based on a forward pass through a first portion of a machine learning model. The features are projected into a vector space associated with a virtual camera at a particular height. The projected features are aggregated with other projected features associated with prior images.”
The second, significantly more extensive patent is described in its abstract, focusing on the “aggregation of static objects” by the vehicle:
“Systems and methods for a vision-based machine learning model for aggregation of static objects and systems for autonomous driving. An example method includes obtaining images from image sensors positioned about a vehicle. Features associated with the images are determined, with the features being output based on a forward pass through a machine learning model. The features are projected into a vector space associated with a birds-eye view based on the machine learning model.”
If anything, the new patents from Tesla show just how dedicated it is to its visual-only system. Nowhere in either of the patents does the automaker address other sensor inputs, which seems to line up with recent discoveries showing upcoming vehicles without ultrasonic sensors.
Further, with Tesla’s increasing focus on making vehicles more AI capable, implementing improved machine learning also matches its design goals.
It remains unclear whether these improvements have been implemented in upcoming software versions or have already been placed in cars via recent software updates. Still, they nonetheless indicate that the company is making continuous progress in its pursuit.
As more and more automakers enter the autonomous driving competition, Tesla’s lead becomes ever more apparent. And while many have mocked the company for its dedication to AI over just vehicles, that investment is proving to be a fantastic one. Hopefully, it will result in an increasingly better Tesla driving experience in the coming years.
What do you think of the article? Do you have any comments, questions, or concerns? Shoot me an email at william@teslarati.com. You can also reach me on Twitter @WilliamWritin. If you have news tips, email us at tips@teslarati.com!
Elon Musk
Tesla’s Elon Musk: 10 billion miles needed for safe Unsupervised FSD
As per the CEO, roughly 10 billion miles of training data are required due to reality’s “super long tail of complexity.”
Tesla CEO Elon Musk has provided an updated estimate for the training data needed to achieve truly safe unsupervised Full Self-Driving (FSD).
As per the CEO, roughly 10 billion miles of training data are required due to reality’s “super long tail of complexity.”
10 billion miles of training data
Musk comment came as a reply to Apple and Rivian alum Paul Beisel, who posted an analysis on X about the gap between tech demonstrations and real-world products. In his post, Beisel highlighted Tesla’s data-driven lead in autonomy, and he also argued that it would not be easy for rivals to become a legitimate competitor to FSD quickly.
“The notion that someone can ‘catch up’ to this problem primarily through simulation and limited on-road exposure strikes me as deeply naive. This is not a demo problem. It is a scale, data, and iteration problem— and Tesla is already far, far down that road while others are just getting started,” Beisel wrote.
Musk responded to Beisel’s post, stating that “Roughly 10 billion miles of training data is needed to achieve safe unsupervised self-driving. Reality has a super long tail of complexity.” This is quite interesting considering that in his Master Plan Part Deux, Elon Musk estimated that worldwide regulatory approval for autonomous driving would require around 6 billion miles.
FSD’s total training miles
As 2025 came to a close, Tesla community members observed that FSD was already nearing 7 billion miles driven, with over 2.5 billion miles being from inner city roads. The 7-billion-mile mark was passed just a few days later. This suggests that Tesla is likely the company today with the most training data for its autonomous driving program.
The difficulties of achieving autonomy were referenced by Elon Musk recently, when he commented on Nvidia’s Alpamayo program. As per Musk, “they will find that it’s easy to get to 99% and then super hard to solve the long tail of the distribution.” These sentiments were echoed by Tesla VP for AI software Ashok Elluswamy, who also noted on X that “the long tail is sooo long, that most people can’t grasp it.”
News
Tesla earns top honors at MotorTrend’s SDV Innovator Awards
MotorTrend’s SDV Awards were presented during CES 2026 in Las Vegas.
Tesla emerged as one of the most recognized automakers at MotorTrend’s 2026 Software-Defined Vehicle (SDV) Innovator Awards.
As could be seen in a press release from the publication, two key Tesla employees were honored for their work on AI, autonomy, and vehicle software. MotorTrend’s SDV Awards were presented during CES 2026 in Las Vegas.
Tesla leaders and engineers recognized
The fourth annual SDV Innovator Awards celebrate pioneers and experts who are pushing the automotive industry deeper into software-driven development. Among the most notable honorees for this year was Ashok Elluswamy, Tesla’s Vice President of AI Software, who received a Pioneer Award for his role in advancing artificial intelligence and autonomy across the company’s vehicle lineup.
Tesla also secured recognition in the Expert category, with Lawson Fulton, a staff Autopilot machine learning engineer, honored for his contributions to Tesla’s driver-assistance and autonomous systems.
Tesla’s software-first strategy
While automakers like General Motors, Ford, and Rivian also received recognition, Tesla’s multiple awards stood out given the company’s outsized role in popularizing software-defined vehicles over the past decade. From frequent OTA updates to its data-driven approach to autonomy, Tesla has consistently treated vehicles as evolving software platforms rather than static products.
This has made Tesla’s vehicles very unique in their respective sectors, as they are arguably the only cars that objectively get better over time. This is especially true for vehicles that are loaded with the company’s Full Self-Driving system, which are getting progressively more intelligent and autonomous over time. The majority of Tesla’s updates to its vehicles are free as well, which is very much appreciated by customers worldwide.
Elon Musk
Judge clears path for Elon Musk’s OpenAI lawsuit to go before a jury
The decision maintains Musk’s claims that OpenAI’s shift toward a for-profit structure violated early assurances made to him as a co-founder.
A U.S. judge has ruled that Elon Musk’s lawsuit accusing OpenAI of abandoning its founding nonprofit mission can proceed to a jury trial.
The decision maintains Musk’s claims that OpenAI’s shift toward a for-profit structure violated early assurances made to him as a co-founder. These claims are directly opposed by OpenAI.
Judge says disputed facts warrant a trial
At a hearing in Oakland, U.S. District Judge Yvonne Gonzalez Rogers stated that there was “plenty of evidence” suggesting that OpenAI leaders had promised that the organization’s original nonprofit structure would be maintained. She ruled that those disputed facts should be evaluated by a jury at a trial in March rather than decided by the court at this stage, as noted in a Reuters report.
Musk helped co-found OpenAI in 2015 but left the organization in 2018. In his lawsuit, he argued that he contributed roughly $38 million, or about 60% of OpenAI’s early funding, based on assurances that the company would remain a nonprofit dedicated to the public benefit. He is seeking unspecified monetary damages tied to what he describes as “ill-gotten gains.”
OpenAI, however, has repeatedly rejected Musk’s allegations. The company has stated that Musk’s claims were baseless and part of a pattern of harassment.
Rivalries and Microsoft ties
The case unfolds against the backdrop of intensifying competition in generative artificial intelligence. Musk now runs xAI, whose Grok chatbot competes directly with OpenAI’s flagship ChatGPT. OpenAI has argued that Musk is a frustrated commercial rival who is simply attempting to slow down a market leader.
The lawsuit also names Microsoft as a defendant, citing its multibillion-dollar partnerships with OpenAI. Microsoft has urged the court to dismiss the claims against it, arguing there is no evidence it aided or abetted any alleged misconduct. Lawyers for OpenAI have also pushed for the case to be thrown out, claiming that Musk failed to show sufficient factual basis for claims such as fraud and breach of contract.
Judge Gonzalez Rogers, however, declined to end the case at this stage, noting that a jury would also need to consider whether Musk filed the lawsuit within the applicable statute of limitations. Still, the dispute between Elon Musk and OpenAI is now headed for a high-profile jury trial in the coming months.
Tesla’s Elon Musk: 10 billion miles needed for safe Unsupervised FSD
Tesla earns top honors at MotorTrend’s SDV Innovator Awards
