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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. 

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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. 

The Teslarati team would appreciate hearing from you. If you have any tips, contact me at maria@teslarati.com or via Twitter @Writer_01001101.

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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USDOT Secretary visits Tesla Giga Texas, hints at national autonomous vehicle standards

The Transportation Secretary also toured the factory’s production lines and spoke with CEO Elon Musk.

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Credit: Elon Musk/X

United States Department of Transportation (USDOT) Secretary Sean Duffy recently visited Tesla’s Gigafactory Texas complex, where he toured the factory’s production lines and spoke with CEO Elon Musk. In a video posted following his Giga Texas visit, Duffy noted that he believes there should be a national standard for autonomous vehicles in the United States.

Duffy’s Giga Texas Visit

As could be seen in videos of his Giga Texas visit, the Transportation Secretary seemed to appreciate the work Tesla has been doing to put the United States in the forefront of innovation. “Tesla is one of the many companies helping our country reach new heights. USDOT will be right there all the way to make sure Americans stay safe,” Duffy wrote in a post on X. 

He also praised Tesla for its autonomous vehicle program, highlighting that “We need American companies to keep innovating so we can outcompete the rest of the world.”

National Standard

While speaking with Tesla CEO Elon Musk, the Transportation Secretary stated that other autonomous ride-hailing companies have been lobbying for a national standard for self-driving cars. Musk shared the sentiment, stating that “It’d be wonderful for the United States to have a national set of rules for autonomous driving as opposed to 50 independent sets of rules on a state-by-state rules basis.”

Duffy agreed with the CEO’s point, stating that, “You can’t have 50 different rules for 50 different states. You need one standard.” He also noted that the Transportation Department has asked autonomous vehicle companies to submit data. By doing so, the USDOT could develop a standard for the entire United States, allowing self-driving cars to operate in a manner that is natural and safe.

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Tesla posts Optimus’ most impressive video demonstration yet

The humanoid robot was able to complete all the tasks through a single neural network.

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Credit: Tesla Optimus/X

When Elon Musk spoke with CNBC’s David Faber in an interview at Giga Texas, he reiterated the idea that Optimus will be one of Tesla’s biggest products. Seemingly to highlight the CEO’s point, the official Tesla Optimus account on social media platform X shared what could very well be the most impressive demonstration of the humanoid robot’s capabilities to date.

Optimus’ Newest Demonstration

In its recent video demonstration, the Tesla Optimus team featured the humanoid robot performing a variety of tasks. These include household chores such as throwing the trash, using a broom and a vacuum cleaner, tearing a paper towel, stirring a pot of food, opening a cabinet, and closing a curtain, among others. The video also featured Optimus picking up a Model X fore link and placing it on a dolly.

What was most notable in the Tesla Optimus team’s demonstration was the fact that the humanoid robot was able to complete all the tasks through a single neural network. The robot’s actions were also learned directly from Optimus being fed data from first-person videos of humans performing similar tasks. This system should pave the way for Optimus to learn and refine new skills quickly and reliably.

Tesla VP for Optimus Shares Insight

In a follow-up post on X, Tesla Vice President of Optimus (Tesla Bot) Milan Kovac stated that one of the team’s goals is to have Optimus learn straight from internet videos of humans performing tasks, including footage captured in third person or by random cameras.

“We recently had a significant breakthrough along that journey, and can now transfer a big chunk of the learning directly from human videos to the bots (1st person views for now). This allows us to bootstrap new tasks much faster compared to teleoperated bot data alone (heavier operationally).

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“Many new skills are emerging through this process, are called for via natural language (voice/text), and are run by a single neural network on the bot (multi-tasking). Next: expand to 3rd person video transfer (aka random internet), and push reliability via self-play (RL) in the real-, and/or synthetic- (sim / world models) world,” Kovac wrote in his post on X.

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Starship Flight 9 nears as SpaceX’s Starbase becomes a Texan City

SpaceX’s launch site is officially incorporated as Starbase, TX. Starship Flight 9 could launch on May 27, 2025. 

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(Credit: Jenny Hautmann/Wikimedia Commons)

SpaceX’s Starbase is officially incorporated as a city in Texas, aligning with preparations for Starship Flight 9. The newly formed city in Cameron County serves as the heart of SpaceX’s Starship program.

Starbase City spans 1.5 square miles, encompassing SpaceX’s launch facility and company-owned land. A near-unanimous vote by residents, who were mostly SpaceX employees, led to its incorporation. SpaceX’s Vice President of Test and Launch, Bobby Peden, was elected mayor of Starbase. The new Texas city also has two SpaceX employees as commissioners. All Starbase officials will serve two-year terms unless extended to four by voters.

As the new city takes shape, SpaceX is preparing for the Starship Flight 9 launch, which is tentatively scheduled for May 27, 2025, at 6:30 PM CDT from Starbase, Texas.

SpaceX secured Federal Aviation Administration (FAA) approval for up to 25 annual Starship and Super Heavy launches from the site. However, the FAA emphasized that “there are other licensing requirements still to be completed,” including policy, safety, and environmental reviews.

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On May 15, the FAA noted SpaceX updated its launch license for Flight 9, but added: “SpaceX may not launch until the FAA either closes the Starship Flight 8 mishap investigation or makes a return to flight determination. The FAA is reviewing the mishap report SpaceX submitted on May 14.”

Proposed Texas legislation could empower Starbase officials to close local highways and restrict Boca Chica Beach access during launches. Cameron County Judge Eddie Trevino, Jr., opposes the Texas legislation, insisting beach access remain under county control. This tension highlights the balance between SpaceX’s ambitions and local interests.

Starbase’s incorporation strengthens SpaceX’s operational base as it gears up for Starship Flight 9, a critical step in its mission to revolutionize space travel. With growing infrastructure and regulatory hurdles in focus, Starbase is poised to become a cornerstone of SpaceX’s vision, blending community development with cutting-edge aerospace innovation.

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