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Tesla FSD Beta V11.3 starts shipping to employees (Release Notes)

Credit: Drive in EV/Twitter

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The release notes for Tesla FSD Beta V11.3 have been shared online. Observers from the electric vehicle community suggest that Tesla Full Self-Driving Beta 11.3 is rolling out to the company’s employee FSD Beta testers, at least for now. 

The following are Tesla’s FSD Beta V11.3 release notes: 

  • Enabled FSD Beta on highway. This unifies the vision and planning stack on and off-highway and replaces the legacy highway stack, which is over four years old. The legacy highway stack still relies on several single-camera and single-frame networks, and was setup to handle simple lane-specific maneuvers. FSD Beta’s multi-camera video networks and next-gen planner, that allows for more complex agent interactions with less reliance on lanes, make way for adding more intelligent behaviors, smoother control and better decision making.
  • Added voice drive-notes. After an intervention, you can now send Tesla an anonymous voice message describing your experience to help improve Autopilot.
  • Expanded Automatic Emergency Braking (AEB) to handle vehicles that cross ego’s path. This includes cases where other vehicles run their red light or turn across ego’s path, stealing the right-of-way.
  • Replay of previous collisions of this type suggests that 49% of the events would be mitigated by the new behavior. This improvement is now active in both manual driving and autopilot operation.
  • Improved autopilot reaction time to red light runners and stop sign runners by 500ms, by increased reliance on object’s instantaneous kinematics along with trajectory estimates.
  • Added a long-range highway lanes network to enable earlier response to blocked lanes and high curvature.
  • Reduced goal pose prediction error for candidate trajectory neural network by 40% and reduced runtime by 3X. This was achieved by improving the dataset using heavier and more robust offline optimization, increasing the size of this improved dataset by 4X, and implementing a better architecture and feature space.
  • Improved occupancy network detections by oversampling on 180K challenging videos including rain reflections, road debris, and high curvature.
  • Improved recall for close-by cut-in cases by 20% by adding 40k autolabeled fleet clips of this scenario to the dataset. Also improved handling of cut-in cases by improved modeling of their motion into ego’s lane, leveraging the same for smoother lateral and longitudinal control for cut-in objects.
  • Added “lane guidance module and perceptual loss to the Road Edges and Lines network, improving the absolute recall of lines by 6% and the absolute recall of road edges by 7%.
  • Improved overall geometry and stability of lane predictions by updating the “lane guidance” module representation with information relevant to predicting crossing and oncoming lanes.
  • Improved handling through high speed and high curvature scenarios by offsetting towards inner lane lines. 
  • Improved lane changes, including: earlier detection and handling for simultaneous lane changes, better gap selection when approaching deadlines, better integration between speed-based and nav-based lane change decisions and more differentiation between the FSD driving profiles with respect to speed lane changes.
  • Improved longitudinal control response smoothness when following lead vehicles by better modeling the possible effect of lead vehicles’ brake lights on their future speed profiles.
  • Improved detection of rare objects by 18% and reduced the depth error to large trucks by 9%, primarily from migrating to more densely supervised autolabeled datasets.
  • Improved semantic detections for school busses by 12% and vehicles transitioning from stationary-to-driving by 15%. This was achieved by improving dataset label accuracy and increasing dataset size by 5%.
  • Improved decision making at crosswalks by leveraging neural network based ego trajectory estimation in place of approximated kinematic models.
  • Improved reliability and smoothness of merge control, by deprecating legacy merge region tasks in favor of merge topologies derived from vector lanes.
  • Unlocked longer fleet telemetry clips (by up to 26%) by balancing compressed IPC buffers and optimized write scheduling across twin SOCs.

Several longtime FSD Beta testers have pointed out some key improvements that would likely be very appreciated by users in V11.3. These include the systems’ improved handling through high speed and high curvature scenarios, as well as improvements to Automatic Emergency Braking (AEB). With the improvements in place, FSD Beta V11.3 would behave closer to a proper human driver. 

Comments from longtime Tesla FSD Beta testers also suggest that V11.3 is still only being released for company employees for now. Considering Tesla’s past updates, it would not be surprising if the greater FSD Beta fleet gets the V11.3 update in the coming week or so. This is, of course, unless V11.3 ends up going the way of FSD Beta V11, which was released to employees in November but not to the greater fleet of FSD Beta testers. 

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Maria--aka "M"-- is an experienced writer and book editor. She's written about several topics including health, tech, and politics. As a book editor, she's worked with authors who write Sci-Fi, Romance, and Dark Fantasy. M loves hearing from TESLARATI readers. If you have any tips or article ideas, contact her at maria@teslarati.com or via X, @Writer_01001101.

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Elon Musk

Elon Musk shuts down talk of TSMC taking over Terafab

Musk says Tesla and SpaceX will build and run Terafab, with TSMC limited to renting.

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SpaceX Terafab rendering

Elon Musk has drawn a firm line around who will be in charge of Terafab, the giant chip factory Tesla and SpaceX are planning in Texas.

Musk replied to a post on X arguing that Taiwan Semiconductor Manufacturing Company (TSMC) would most likely end up owning and operating the plant. “No, we will build and run the fab. Let there be ZERO doubt about that,” Musk wrote. “Maybe TSMC subleases part of the Terafab if they want, but nothing more than that.”

In plain terms, a sublease means TSMC could rent a section of the complex to make chips, similar to a tenant renting one floor of an office tower. The building, the equipment decisions and the daily operation would stay with Tesla and SpaceX.

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The comment shuts down speculation that started last week. On October 2, tech journalist Tim Culpan reported that TSMC was exploring ways to help run Terafab’s factories. Musk responded the next day that it was “just discussions, but something may come of it,” as Teslarati reported at the time. That left room for a scenario where the world’s largest contract chipmaker took the wheel. Musk’s latest post closes that door.

Elon Musk teases TSMC as potential Terafab partner

Some background helps explain why this matters. Tesla designs its own AI chips today but pays outside companies like TSMC and Samsung to manufacture them. Musk unveiled Terafab in March as a joint project between Tesla, SpaceX and xAI, arguing that existing suppliers cannot expand fast enough to meet his companies’ future demand. The goal is to produce enough chips each year to supply one terawatt of computing power, roughly 50 times what the entire global AI chip industry produces now.

Those chips are meant for Tesla’s Optimus humanoid robots, the Cybercab and Full Self-Driving computers, along with chips for SpaceX’s planned data centers in orbit. Owning the factory means Musk’s companies would not have to compete with every other chip customer for time on someone else’s production lines.

Intel is still part of the picture. The company signed on in April to help design, build and package chips for the project, and CEO Lip-Bu Tan told Bloomberg this week that Intel will keep working on Terafab despite the TSMC chatter.

The project moved from concept to construction planning over the summer. In August, SpaceX confirmed the Grimes County site about an hour from Houston, sent the county a $10 million payment under its tax abatement deal and said civil work would begin shortly. The first phase carries a $16.8 billion price tag, and total spending across all phases could reach as much as $119 billion.

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TSMC chairman C.C. Wei has said a new fab typically takes two to three years to build and another one to two years to reach full output. Tesla and SpaceX have never run one, which is why TSMC’s expertise drew so much attention. Musk’s answer suggests he would rather learn that process in house than hand control of a project this central to Tesla’s robotics and autonomy plans to an outside company.

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Elon Musk

Trump to hand Elon Musk a top honor that traces back to JFK

Trump will award Elon Musk the National Medal of Science at Thursday’s White House summit.

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elon musk and donald trump in front of a tesla cybertruck at the white house

Elon Musk is set to receive the highest honor the U.S. government gives to scientists and engineers.

President Donald Trump will present Musk with the National Medal of Science on Thursday at the White House’s Science: A New Golden Age Summit, Fox News Digital first reported on Wednesday. Google cofounder Sergey Brin, Nvidia CEO Jensen Huang and AMD CEO Lisa Su will receive the same medal, while Dell Technologies CEO Michael Dell and Microsoft CEO Satya Nadella will receive the National Medal of Technology and Innovation. A White House official later confirmed the list to Reuters.

“The Trump administration is grateful for the contributions of these incredible leaders in science and technology. These recipients are helping ensure America keeps leading the world in innovation,” White House spokesperson Liz Huston told Fox News.

It will be the first time Trump has presented either medal in his two terms. Congress created the National Medal of Science in 1959, and the National Science Foundation, which administers it, says 529 scientists and engineers have received it since. A presidential committee reviews nominees, but the president makes the final call.

Thursday’s group of medalists run or founded companies, and three of them sit at the center of the Super Intelligence hardware race that Musk competes in. Huang’s Nvidia supplies the GB300 chips filling SpaceX’s Colossus 2 cluster, while Su’s AMD is Nvidia’s biggest rival in data center GPUs.

Worth noting that Trump’s uncle, MIT physicist John G. Trump, received the National Medal of Science from President Ronald Reagan for his work on ionizing radiation and its uses in medicine and industry.

The Pentagon taps Elon Musk to design the battlefield of the future

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For Musk, the medal is the latest sign of how far his relationship with Trump has come since their 2025 split over the “Big Beautiful Bill” and his exit from DOGE. Last week, he sat at Trump’s left during a White House lunch where AI executives signed a voluntary safety accord, and Defense Secretary Pete Hegseth named him to help lead the Pentagon’s Project Meridian study on the future of warfare. Musk has also adopted the administration’s new vocabulary, saying on Sunday that SpaceXAI will be renamed SpaceXSI after Trump ordered federal agencies to replace “artificial intelligence” with “super intelligence.”

Musk has collected science honors before, including the Stephen Hawking Medal for Science Communication in 2019.

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Lifestyle

Tesla FSD changed its mind mid-intersection, and it may have saved a life

Tesla shares dashcam footage of FSD Supervised stopping mid intersection to avoid a T-bone crash.

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Credit: @BLKMDL3/X

Tesla is putting another Full Self-Driving save in front of its 24.8 million followers on X.

On Tuesday morning, Tesla’s main account shared a dashcam clip with the caption “FSD Supervised preventing T-bone crash.” The footage came from an owner posting as TheNewGrid, who described what happened at a stop sign: “I looked at the car coming to the stop sign figured they would stop, my car went, then came to a stop mid intersection as they flew by. Had I been manually driving this would have resulted in a crash.”

The sequence is the notable part. FSD had already started crossing when the other driver ran the stop sign. Instead of pressing on, the car braked hard in the middle of the intersection and let the crossing vehicle pass in front of it. By the owner’s own account, they had made the same assumption the software initially made, that the other car would stop, and would not have corrected in time.

The clip is the latest in a run of safety posts Tesla has amplified over the past several days. On Saturday, the company shared a video from Selling Sunset star Jason Oppenheim, who sold his Bentley for a Model Y and said he was buying Teslas with FSD for 10 of his employees. Ashok Elluswamy, who leads Tesla AI, followed up by writing that Tesla self-driving “reacts to other people cutting into your path with super-human response times.” On Monday, a Cybertruck owner posted footage of FSD moving across three lanes from a red light to clear a path for an ambulance approaching from behind.

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This recent clip also lands a few weeks after Tesla began shipping Automatic Collision Evasion with FSD v14.3.9, a feature that can activate FSD on the driver’s behalf when a frontal collision is imminent or the driver appears distracted. Elluswamy said in September that “even earlier prediction of hazards, even faster reaction time and overall significantly better safety and collision avoidance” are coming with v15, the release Tesla has tied to round the clock Robotaxi operation.

The safety messaging matters beyond social media. Tesla has said FSD Supervised was 4.1 times less likely to crash than manual driving across 100 million kilometers on European roads, and it has been putting those figures in front of regulators. Eight EU countries have now approved FSD Supervised, with Croatia the most recent, but the EU’s bloc-wide vote originally set for October 6 has been pushed to December at the earliest.

FSD Supervised is still a Level 2 system, and the driver remains responsible at all times. Even heavy users find reasons to step in. Teslarati’s Joey Klender, who uses FSD for about 76 percent of his driving, laid out five recurring issues on Tuesday that still prompt him to intervene. Clips like this one show the other column of that ledger: moments where the software caught a mistake a human was about to make.

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