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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.

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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.

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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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Tesla Full Self-Driving shows stunning maneuver in Europe to silence skeptics

In a striking demonstration of autonomous driving prowess, Tesla’s Full Self-Driving (FSD) system recently showcased its capabilities on the narrow rural roads of the Netherlands. Captured in two in-car videos, the system encountered scenarios that would challenge even the most experienced human drivers.

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Credit: Tesla

Tesla Full Self-Driving, fresh on the heels of its approval for operation on European roads for the first time, showed off a stunning maneuver that will certainly silence any skeptics on the continent.

Fresh off its approval in the Netherlands, Full Self-Driving is working toward a significant expansion into more parts of Europe.

In a striking demonstration of autonomous driving prowess, Tesla’s Full Self-Driving (FSD) system recently showcased its capabilities on the narrow rural roads of the Netherlands. Captured in two in-car videos, the system encountered scenarios that would challenge even the most experienced human drivers.

In the first clip, a wide tractor occupied more than half the lane on a tight two-way road. Rather than braking abruptly or forcing a collision risk, FSD smoothly edged the vehicle onto the adjacent bike path—using the extra space with precision—before seamlessly returning to the lane once clear.

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The second clip was equally demanding: while overtaking a group of cyclists, an oncoming car approached at speed.

FSD maintained a safe, minimal buffer to the cyclists while timing the pass perfectly, avoiding any swerve or hesitation that could unsettle passengers or other road users.

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This maneuver highlights FSD’s advanced spatial reasoning and predictive planning. On roads often under three meters wide, with no room for error, the system calculated available clearance in real time, incorporated shoulder and path geometry, and executed a controlled deviation without compromising safety.

It treated the bike path as a legitimate extension of navigable space, something many drivers might hesitate to do, while respecting Dutch road norms and cyclist priority.

Such feats align closely with a growing library of impressive FSD maneuvers documented on camera worldwide.

In urban Amsterdam, for instance, FSD has navigated the world’s densest cyclist environments, weaving through hundreds of unpredictable bike movements on canal-side streets with tram tracks and pedestrians.

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One uncut drive showed it yielding smoothly at crossings, overtaking where needed, and even handling a near-perfect auto-park in a tight residential spot, demonstrating the same low-speed precision seen in the rural clips.

Teslas using FSD have tackled turbo roundabouts in the Netherlands, complex multi-lane circles notorious for geometry challenges, merging confidently while yielding to traffic. Similar clips depict smooth handling of construction zones, emergency vehicle pull-overs, and gated parking barriers, where the car stops precisely, waits for clearance, and proceeds without driver input.

Collectively, these examples illustrate FSD’s evolution toward handling the unpredictable.

The rural Netherlands maneuvers aren’t isolated. Instead, they reflect a pattern of spatial awareness, cyclist deference, and traffic anticipation seen from city streets to highways.

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As FSD continues refining through real-world data, videos like this one are certainly building a compelling case for its readiness on Europe’s varied roads.

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Tesla utilizes its ‘Rave Cave’ for new awesome safety feature

Part of the massive interior overhaul of both the Model 3 “Highland” and Model Y “Juniper” was the addition of interior accent lighting to help bring out the mood of the vehicle, increase the customization of the interior, and to create a unique listening experience.

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Credit: Tesla | X

Tesla is utilizing its ‘Rave Cave’ for an awesome new safety feature that will arrive with the upcoming Spring Update for 2026.

Part of the massive interior overhaul of both the Model 3 “Highland” and Model Y “Juniper” was the addition of interior accent lighting to help bring out the mood of the vehicle, increase the customization of the interior, and to create a unique listening experience.

Tesla added a Sync Lights feature that will strobe the accent strips with the beat of the music.

It is one of the most unique and one of the coolest non-functional features of a Tesla, as it does not improve the driving of the vehicle, but makes it a cool and personal addition to the interior.

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However, Tesla is going to take it one step further, as the Rave Cave lights will now be used for blind spot recognition. This feature will be added as the Spring 2026 Update starts to roll out.

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Tesla writes:

“Accent lights now turn red when an object is in your blind spot and your turn signal is engaged, or when an approaching object is detected while parked.”

This neat new safety feature will now increase the likelihood of a driver, who is operating their Tesla manually, of seeing the blind spot warnings that are currently available on the A pillar and on the center touchscreen.

These new alerts will now warn drivers of cross traffic as they back out of a parking space with little to no visibility of what is coming. It is a great new addition that will only increase the safety of the vehicles, while also utilizing something that is already installed in these specific Model 3 and Model Y units.

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The Model 3 and Model Y were the central focus of the Spring 2026 Update, especially considering the fact that the Model S and Model X are basically gone, with only a few hundred units left. Additionally, Tesla included new Immersive Sound and Car Visualization for the Model 3 and Model Y specifically in this new update.

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Tesla parked 50+ Cybercabs outside its Texas Factory with some crash tested

Dozens of Tesla Cybercabs have been spotted at Giga Texas crash testing facility ahead of launch.

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Tesla Cybercab fleet spotted at Gigafactory Texas [Credit: Joe Tegtmeyer)
Tesla Cybercab fleet spotted at Gigafactory Texas on April 13, 2026 [Credit: Joe Tegtmeyer)

Drone footage captured by longtime Giga Texas observer Joe Tegtmeyer shows over 50 units of Tesla Cybercab at the Austin factory campus, including several units clustered by Tesla’s on-site crash testing facility.

The outbound lot at Gigafactory Texas sits just outside the factory exit and serves as the primary staging area where finished vehicles are held before being loaded onto transport carriers or dispatched for validation testing. On any given day, the lot holds a mix of Model Y and Cybertruck units alongside the growing Tesla Cybercab fleet, as can be seen in the drone footage captured by Joe Tegtmeyer.

Tesla Cybercab fleet spotted at Gigafactory Texas [Credit: Joe Tegtmeyer)

Tesla Cybercab fleet spotted at Gigafactory Texas on April 13, 2026 [Credit: Joe Tegtmeyer)

Roughly 50 Cybercab units are visible across the campus, parked in tight organized rows. Most of the units visible still carry steering wheels and pedals, temporary additions Tesla included to satisfy current safety regulations while the vehicles accumulate real-world data ahead of full regulatory approval for a steering wheel-free design.

Tesla Cybercab fleet spotted at Gigafactory Texas [Credit: Joe Tegtmeyer)

Tesla Cybercab fleet spotted at Gigafactory Texas [Credit: Joe Tegtmeyer)

Tesla operates dedicated Crash Labs at both its Giga Texas and Fremont facilities that are purpose-built for controlled structural crash tests. Historically, automakers begin intensive crash testing roughly one to two months before volume production kicks off. The Cybertruck followed almost exactly that pattern. The Cybercab appears to be on the same track facility that we first saw back in October 2025.

Tesla Cybercab crash test units spotted at Gigafactory Texas [Credit: Joe Tegtmeyer)

Tesla Cybercab crash test units spotted at Gigafactory Texas [Credit: Joe Tegtmeyer)

The first production Cybercab rolled off the Giga Texas line on February 17, 2026. Volume production is now targeted for April. Musk previously wrote on X that “the early production rate will be agonizingly slow, but eventually end up being insanely fast,” and separately stated Tesla is targeting at least 2 million Cybercab units per year. Commercial robotaxi service in Austin is targeted for late 2026.

 

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