Connect with us
tesla-fsd-beta-v-11-3-release-date tesla-fsd-beta-v-11-3-release-date

News

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

Credit: Drive in EV/Twitter

Published

on

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.

Advertisement

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. 

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

Advertisement

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.

Advertisement
Comments

News

Tesla reveals first vehicle model to receive Starlink integration

Published

on

Tesla has evidently revealed which of its vehicle models will be the first to receive Starlink integration: the Cybercab.

Tesla’s Santana Row showroom now has a full-fledged display of the Cybercab, with an extensive bit of information hung around an exhibit that seems to reveal the vehicle’s newest feature: an integrated Starlink antenna that will enable secure and reliable internet access during trips.

Credit: @Starscream_SJC | X

Cybercab is geared toward autonomous ride-hailing for one or two passengers. The production units rolling off the lines at Gigafactory Texas are built without steering wheels or pedals, meaning when public rides begin, passengers will not need to interact with a human being or control the vehicle in any way outside of what appears on the center screen for their entertainment during the ride.

Advertisement

Along the display, Tesla wrote this message about Cybercab:

“Cybercab is built for autonomy. It has no steering wheel, no side mirrors, and no pedals. It goes where you tell it to go and how you want it to, so you can relax along the way. It is hyper aware and responsive to your surroundings, monitoring other drivers, responding to emergency vehicles, utilizing its expertise in the rarest scenarios to help keep you safe.”

Tesla has been teasing a potential Starlink integration for quite some time now. In December, the company hinted at potential Starlink internet terminal integration within its vehicles in a patent that described a vehicle roof assembly with integrated radio frequency (RF) transparency.

Tesla hints at Starlink integration with recent patent

Advertisement

The company wrote in its patent application that a new roof design built with materials that differ from the standard metallic or glass elements used in today’s cars would allow it to integrate modern vehicular technologies, in particular, ones that require radio frequency transmission and reception.

Tesla suggested high-strength polymer blends, like Polycarbonate, Acrylonitrile Butadiene Styrene, or Acrylonitrile Styrene Acrylate.

This is the first time we’ve seen Tesla officially confirm the Starlink integration into the Cybercab. It’s not much of a surprise considering the company’s intention behind the Cybercab, which is to make travel autonomous.

Productivity will now be at a maximum during a work-related commute, while the center screen could be utilized for Netflix or potentially even live TV for those who are heading to dinner or to a fun activity.

Advertisement
Continue Reading

News

SpaceX adjusts Starship Flight 13 test launch target date once again

Published

on

Credit: SpaceX

SpaceX has updated its target for the thirteenth integrated flight test of Starship, aiming for as early as Thursday, July 23. The 90-minute launch window opens at 5:45 p.m. CT from the company’s Starbase facility in South Texas.

The target flight was initially rescheduled for today, but SpaceX pushed it back again.

This latest adjustment follows an aborted attempt earlier in the week and reflects the iterative, rapid-development approach that has defined the Starship program. With the vehicle already stacked and ground teams making final preparations, the mission represents another step toward proving the full reusability of the world’s most powerful rocket system.

The original launch attempt on July 16 was scrubbed at T-0 when several Raptor engines on the Super Heavy booster failed to ignite properly. The automatic abort system triggered just as the engines began their startup sequence, preventing liftoff.

SpaceX CEO Elon Musk confirmed that some engines did not start as expected, prompting the decision to replace two Raptors on Booster 20 to ensure reliability. The issue occurred despite a successful full-duration static fire earlier, highlighting the complexities of coordinating 33 engines under flight conditions.

This cautious approach underscores SpaceX’s commitment to safety amid an aggressive test cadence.

Advertisement

SpaceX comes with a slew of changes for Starship Flight 13

Flight 13 builds directly on the lessons from Flight 12 in May 2026. The Super Heavy booster’s primary goals include a successful liftoff, ascent, stage separation, boostback burn, and controlled splashdown in the Gulf of America.

Hardware and software modifications address the off-nominal flip and boostback burn problems from the prior flight, where propellant slosh and engine relight issues led to an uncontrolled impact.

For the Starship upper stage, objectives include deploying 20 operational Starlink V3 satellites, the first real payload of this type, performing a single Raptor engine relight in space, and executing a controlled entry, descent, and splashdown in the Indian Ocean. Propulsion upgrades aim to improve engine-out capability after one vacuum Raptor was lost on Flight 12.

Advertisement

Additional test elements focus on heat shield performance. Six satellites carry cameras to image the tiles during flight, while white-painted tiles and upgraded attachments on flaps and the aft skirt will gather data for future reusability.

The FAA completed its mishap investigation into Flight 12 earlier this month, clearing the regulatory path.

This suborbital mission, the second with V3 vehicles, advances Starship toward operational missions, including potential crewed flights and support for NASA’s Artemis program. Success would mark significant progress in rapid reusability and satellite deployment from the massive system.

Advertisement
Continue Reading

News

Elon Musk debunks $52 billion SpaceX-NVIDIA GPU deal

Published

on

Credit: SpaceX

Elon Musk dismissed reports claiming SpaceX had placed a massive order for NVIDIA GPUs worth $52 billion. The denial came hours after Taiwanese media, citing unnamed industry sources, reported that SpaceX planned to acquire approximately 13,000 AI server racks, equating to roughly 1 million GB300 GPUs, from Foxconn.

Each rack was estimated at around $4 million, with deliveries potentially starting in late 2025.

The story suggested this would mark SpaceX’s first major foray into Foxconn-manufactured NVIDIA hardware, breaking from suppliers like Supermicro and Dell. Musk responded bluntly on X:

Despite the denial, the rumored scale aligns with SpaceX’s explosive growth in AI infrastructure. NVIDIA’s GB300 (successor to the GB200 NVL) racks deliver unprecedented performance for large-scale training and inference. A $52 billion commitment would dwarf most corporate AI budgets and provide the compute muscle needed for frontier models.

SpaceX already operates gigawatt-scale terrestrial clusters like Colossus in Memphis, Tennessee, and has monetized them aggressively through leasing deals.

SpaceX’s newest Starmind will make earth data centers obsolete

Advertisement

Major customers include Anthropic (paying ~$1.25 billion monthly for 220,000+ GPUs), Google (~$920 million monthly for 110,000 GPUs), and Reflection AI. These arrangements are projected to generate tens of billions in annual revenue, far outpacing traditional SpaceX businesses.

Such an investment would fuel internal AI efforts, particularly Grok models under the integrated SpaceXAI division, while supporting ambitious orbital data center plans. SpaceX envisions launching thousands of AI-optimized satellites powered by solar energy and cooled in space, bypassing terrestrial power and land constraints.

This “Starmind” constellation could position the company as a leader in space-based computing.

SpaceX as an Emerging AI Powerhouse

Once primarily known for reusable rockets and Starlink satellite internet, SpaceX has transformed into a multifaceted AI player.

Advertisement

The 2026 acquisition of xAI integrated Grok development directly into the company. Starlink’s low-latency global network complements massive compute clusters, enabling efficient data flow for training and serving AI models.

Musk has long argued that AI scaling demands solutions beyond Earth, citing things like real estate and electricity limits on the ground.

While the Foxconn deal may not be in the cards, SpaceX’s trajectory is continuing on the path of blending aerospace engineering with hyperscale AI to dominate both launches and intelligence infrastructure.

Advertisement
Continue Reading