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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 expands ridesharing service in California to new hotspot

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Credit: Tesla

Tesla has extended its Bay Area ride-hailing service to include pickups and drop-offs at San Francisco International Airport (SFO). The update, shared via the company’s official channels on July 21, allows users in the region to request rides directly to and from one of California’s busiest airports.

The expansion builds on Tesla’s secured limousine permit for SFO operations. Public records show the permit became effective March 20, 2026, and remains active through January 31, 2027. Tesla vehicles operating the service now display authorized limousine permits issued by the City and County of San Francisco.

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Tesla’s ride-hailing program in California relies on Model Y vehicles equipped with Full Self-Driving (Supervised) technology. Human safety drivers remain present in compliance with state regulations, distinguishing the service from fully driverless operations.

The Bay Area geofence covers a broad area spanning north of San Francisco to south of San Jose, offering extensive connectivity across the region.

UPDATE: Elon Musk reveals why Tesla didn’t say ‘Robotaxi’ upon California launch

This SFO addition follows earlier progress at other Bay Area airports. Tesla previously expanded service to San Jose Mineta International Airport (SJC) in late 2025. The company had engaged with SFO, SJC, and Oakland International Airport officials as early as September 2025 to secure necessary approvals for passenger transport.

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The service provides a new option for travelers seeking electric, app-based transportation integrated with Tesla’s ecosystem. Rides are booked through Tesla’s dedicated ride-hailing application, which handles matching, routing, and payments. Pricing follows standard ride-hailing models, with potential adjustments based on distance, time, and demand.

Tesla’s California ride-hailing program launched in July 2025 with an initial invite-only rollout in the Bay Area. It started alongside operations in Austin, Texas, marking the company’s second major U.S. market.

The Bay Area remains a primary focus in California, with service centered on high-demand corridors connecting residential, commercial, and now major transportation hubs. This latest airport integration represents a practical step in Tesla’s broader mobility ambitions within the state.

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Tesla reveals 2026 Summer Update with crazy fixes to Nav and more

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Credit: Tesla

Tesla has officially revealed its 2026 Summer Update, which comes with a variety of crazy new features, including Navigation fixes that owners have been wanting for months.

Tesla routinely releases a larger update with the Spring, Summer, Fall, and Winter updates, where it ships a variety of new features, bug fixes, and other additions to customer cars.

The 2026 Spring Update featured things like “Hey Grok” voice assistance, a redesigned self-driving app, Unreal Engine visual upgrades, and more.

Tesla’s Summer Release has about ten new features; we’ll show you each and detail them below:

New Grok Voice Commands

“Grok can now make phone calls, search and play music, adjust climate, open the glovebox, and answer questions about your Tesla.”

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Self-Driving Stats in Mobile App

“View and share self-driving stats from the mobile app.”

Caraoke With Scoring

“Caraoke now scores your singing while in Park. High scores are saved to your Tesla profile.”

Automatic Navigation

“Automatic Navigation now adapts to your routine.

In addition to Home, Work, and upcoming calendar events, your vehicle can now suggest and route to places you visit regularly – like a school drop-off on the way to work, or the gym on the way home.”

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Preferred Routes

“For a more personalized experience, navigation now prioritizes routes that you’ve taken before”

Set Arrival Energy from Mobile App

“Set your desired Arrival Energy from your phone.”

Send Custom Wraps from Mobile App

“Skip the USB drive and upload a custom wrap of your car from the mobile app. Instructions for creating a custom wrap here: https://github.com/teslamotors/custom-wraps.”

Rear Display Lock

“Kids can watch content on the rear screen, but only the front row can control it through the rear screen app.”

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Other Improvements

  • Find Superchargers by name when searching for a destination
  • Add Apple Music songs to queue from search and artist page
  • Set your preferred zoom level for the Self-Driving visualization
  • Intro animations for new Model 3 and Y
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Tesla’s reason for Starlink integration on Cybercab might surprise you

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Credit: Tesla

Tesla’s reason for Starlink integration on Cybercab might surprise you, as the company’s Head of AI, Ashok Elluswamy, finally shed some light on the reason they are putting a satellite internet terminal on its ride-hailing-geared vehicle.

On Monday, Tesla officially confirmed that it would integrate Starlink V5 terminals into Cybercab vehicles, something many Tesla fans had figured the company would do, as the vehicle is primarily geared toward giving rides without any passenger intervention.

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The ability to access the internet would allow riders to work or play in the car with their devices. It seemed like a more-than-reasonable feature to add to the Cybercab, which made its way off the production lines for the first time earlier this year.

Tesla reveals first vehicle model to receive Starlink integration

However, the move is not for the rider, as Elluswamy confirmed on Monday night. Instead, it’s actually for Tesla to be able to have a constant connection to the cars in the Robotaxi fleet so it can troubleshoot issues, contact riders, or resolve other issues.

Elluswamy said:

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“It is still not required for safe operation of the vehicle. Connectivity is primarily meant for navigation, customer service and, in general, fleet management.”

Many initially assumed the option of constant connectivity would be enabled on the Cybercab for passenger entertainment or work. With the Cybercab, passengers won’t be doing anything but enjoying the ride, so it seemed more than logical that they would be hanging out with Starlink internet access as an amenity.

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However, Tesla’s primary concern with Robotaxi is safety, and nailing these first unsupervised rides is a crucial step to setting a good narrative on how effective driverless transportation can be.

Being able to get in touch with passengers or a vehicle if something is wrong is a crucial part of the overall experience, and preventative measures are being taken by Tesla to ensure a smooth process, even in the worst-case.

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