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.
Here are the V11.3 release notes again if you haven't seen them. Very happy to see improvements in rain reflections as that was rare, but could give some insane errors #FSDBeta @elonmusk pic.twitter.com/ZIOcIhmUMd
— Dirty Tesla (@DirtyTesLa) February 20, 2023
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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Tesla Cybercab is using a unique strategy for picking up the correct rider, which is a crucial part of ride-hailing to ensure people end up in the right place and are charged the correct price.
Cybercab will utilize an RGB strip in its front light bar that will illuminate in a variety of different colors to mark itself.
This identifying mark will also appear in the Robotaxi app, giving riders in the same location a notable distinction in an effort to avoid any confusion regarding who should get in each vehicle.
🚨 Tesla is using different light bar colors to help riders understand which vehicle is theirs
Pretty cool strategy pic.twitter.com/eGMpEMeRpc
— TESLARATI (@Teslarati) August 27, 2026
Other ride-hailing services use similar strategies: Lyft and Uber rides are recognizable through driver identity, vehicle type and color, as well as license plate. Waymo will display the rider’s initials on top of the vehicle, letting them know that the specific vehicle for them has arrived.
Tesla’s strategy is unique and interesting, but there are some flaws. Cybercab’s main purpose is aimed toward being an autonomous ride for all, including those who have disabilities like being blind or even color blind.
Tesla will likely have something in the pipeline for those who cannot see colors or have limited vision. There will definitely be multiple ways to identify which vehicle is the one that “you” specifically ordered.
Cybercab is set to start giving public rides next Thursday, September 3, in Austin, as it announced a dedicated event last week and invited many members of the Tesla community.
Additionally, members of the public will be invited as well. Tesla has been offering employee rides in Cybercab for nearly two months.
Tesla has said goodbye to one customization option for its vehicles: the wrap service it launched several years ago.
After launching an in-house wrap service in August 2020 for the first time in China. In the U.S., it launched in October 2023. Tesla continued to expand the program and adjust it with better pricing and fewer options for the Cybertruck.
By December 2023, it was giving owners of the Model 3, Model Y, and the Cybertruck the opportunity to give their vehicle a fresh look with a vinyl wrap.
Tesla revamps in-house vinyl wrap service with better pricing
It was only available in five locations: Costa Mesa, Oceanside, Santa Clara, West Covina, all in California, and Seattle, Washington.
However, Tesla made some big adjustments to its shop, and the wrap service is officially gone:
Tesla has made a LOT of changes to its online shop recently — price increases, price cuts, new options, and several products being removed entirely.
Some of the biggest increases are wheel covers. 👀
📈 PRICE INCREASES
• 2017-2023 | Model 3 Aero Wheel Cover — Style: Refresh…— Phoenix Self-Driving 🐦🔥 (@PhoenixFSD) August 25, 2026
Wraps are very popular across the Tesla lineup, especially since the company offers relatively few colors. Many choose to wrap their Teslas with interesting colors, patterns, or even finishes, turning their cars from glossy to satin or matte.
However, Tesla’s wrap service was so limited geographically that it never really had a chance to get off the ground or compete with local shops. Every area in the United States is now overflowing with detailing shops, mobile detailers, and other automotive specialists, many of whom perform wrap services.
Tesla’s service was confined to the Pacific time zone and only spanned across two states. It was never going to be something Tesla was a major competitor in, nor was it going to disrupt the wrapping industry. Now that the program has ended, it seems pretty ideal to believe it was always going to be a short-term thing.
Along with the wrap service, Tesla removed several other products, but nothing too crazy. The Model 3 Door Pocket and Cupholder Liners, the Model S 19″ Magnetite Wheel and Winter Tire Package, Model X/Y Ski/Snowboard Carrier for Hitch Rack, Tesla’s Electric Summer Party Tee, and the Electric Summer Tee were the other items the company totally eliminated from its online shop.
News
Tesla Robotaxi fleet gets a brain upgrade ahead of Cybercab launch event
Tesla’s Robotaxi service now runs longer hours nationwide as its unsupervised fleet quietly grows larger.
Tesla’s Robotaxi service just got easier to catch, with the company’s official Robotaxi account noting that rides are now available from 6 a.m. to 10 p.m., seven days a week, across its operating footprint. The account also said its unsupervised fleet is “a lot bigger” than before, though without specifics. The bigger change is what Tesla says upgraded intelligence in vehicle distribution and routing is what’s actually cutting wait times, not a new Full Self-Driving version.
While Tesla did not name the team behind the upgrade, the language points to its AI and fleet software group rather than the driving stack itself. Vehicle distribution and routing in Robotaxi has functioned mostly as a dispatch problem with the software deciding which idle car goes to which rider, and how far it has to travel to get there. “Upgraded intelligence” suggests a smarter version of that dispatch logic, likely using demand forecasting to position idle cars near where riders are about to request them rather than reacting once a request comes in. Tesla’s AI division has built similar prediction systems for other parts of the business, including the neural networks that power FSD itself, so applying that same approach to fleet logistics would be a natural extension rather than a new discipline for the team.
Robotaxi now runs 6am to 10pm, 7 days/week
Unsupervised fleet is a lot bigger
Also, upgraded intelligence in vehicle distribution & routing means you wait less pic.twitter.com/UFZ4bqg2dZ
— Tesla Robotaxi (@robotaxi) August 26, 2026
Tesla is also about a week away from a separate robotaxi milestone. The company plans to launch Cybercab, its purpose built two seat robotaxi with no steering wheel or pedals, in Austin on September 3. Cybercab has been giving employees rides on public and private roads for weeks, and the September event is expected to fold those vehicles into the existing Robotaxi fleet within days of the launch.
Thank you so much @Tesla for inviting us to the Cybercab launch in Austin! 🤠 pic.twitter.com/F1rAR8zd5O
— TESLARATI (@Teslarati) August 22, 2026
Austin previously ran Robotaxi from 6 a.m. to 2 a.m. as of last September, a schedule set before the service expanded into Dallas, Houston, Miami, Tampa, Orlando and the Bay Area. Wednesday’s post did not specify whether that extended overnight window still applies in Austin specifically or whether 6 a.m. to 10 p.m. is now the standard across every market. Tesla’s post, visible on its official Robotaxi account, framed the change simply as fewer riders waiting around for a car.
Whether the wider hours hold once Cybercab enters the fleet next week is the next thing worth watching. Tesla has tended to expand Robotaxi in increments, first geofence, then hours, then fleet size, and each step so far has arrived without much advance notice.
Tesla Cybercab uses a unique strategy for picking up the right rider
Tesla ends in-house wrap service that always seemed like a short-term program
