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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Elon Musk
SpaceX’s newest Starmind will make earth data centers obsolete
Elon Musk confirmed Starmind as SpaceX’s AI satellite constellation name, targeting one million orbital compute nodes.
Elon Musk confirmed that Starmind will be the official name of SpaceX’s planned AI satellite constellation, following a trademark filing by xAI that surfaced earlier this week. Starmind is what’s being described to the FCC as a constellation of up to one million AI satellites
It’s worth noting that SpaceX’s Starlink communication satellite and Starmind are built on the same orbital infrastructure concept but serve entirely different purposes. Starlink is a connectivity network, with satellites receiving and relaying data between points on Earth, and functioning as a high-speed internet backbone in space. The satellites themselves do not process or think, and move information from one place to another, the same function a fiber cable performs underground.
SpaceX just forced Verizon, AT&T and T-Mobile to team up for the first time in history
Starmind, on the other hand, is something completely different, and tather than moving data, its satellites would compute data through artificial intelligence and directly in orbit using onboard processors powered by large solar arrays. Where a Starlink satellite is essentially a very fast pipe, a Starmind satellite is a server. The practical implication is that Starmind would allow AI models to run inference, process queries, and generate outputs from space, then beam results down to users anywhere on Earth within milliseconds, and without the data ever needing to travel to a terrestrial data center.
Starship will be able to carry 30 to 50 AI1 satellites per launch, delivering the equivalent of dozens of server racks per flight, with no land acquisition, no power grid approval, and no cooling infrastructure required on the ground.
SpaceX is pursuing this new technology as terrestrial data centers are running into hard limits such as lack of physical space, community opposition, and power and water consumption at a scale that is increasingly difficult to permit. Space has unlimited solar power, natural vacuum cooling, and no zoning boards. Musk said in a June 8 video presentation that he expects space to become the lowest-cost location to deploy AI compute within two to three years. Two AI1 prototypes are scheduled to launch in early 2027, with volume production targeted for the end of that year at a new facility called Gigasat.
The real world applications Starmind enables extend well beyond powering Grok. A constellation of orbiting AI processors could run inference workloads for any paying customer, anywhere on Earth, with latency measured in milliseconds rather than the seconds associated with ground-based cloud routing across continents. Starmind, if it scales as described, would make SpaceX the landlord of AI compute the same way Starlink made it the landlord of satellite internet.
Tesla is pushing back against the unfair reporting of accidents involving its vehicles. Many media outlets were quick to jump to conclusions about a fatal accident involving a Tesla in Katy, Texas, that happened recently.
The driver of the vehicle, which slammed into a brick house and killed a woman inside, stated the car was operating on Autopilot. Tesla CEO Elon Musk and Head of AI Ashok Elluswamy both challenged that claim, with Elluswamy revealing last night that the system was overridden by the driver, who pressed the accelerator pedal “all the way to 100%.”
Tesla finally clarifies fatal Texas crash, confirms driver manually overrode acceleration
The car reached a speed of 73 MPH during the crash, Elluswamy detailed, and stated that the accelerator pedal was even pressed after the crash.
The story has been spread throughout the media with either incomplete or incorrect reporting, with some stories still not updated nearly 24 hours after Musk and Elluswamy posted answers about the crash on X.
The reporting has been a thorn in the side of Tesla for several years. Vehicle accidents involving Teslas are usually reported with the manufacturer’s name in the headline, while other companies are free of criticism when their cars are involved in accidents.
Here’s an example of that:
So you don’t report the vehicle’s make when it isn’t a Tesla, but you do report it when it is a Tesla?
The vehicle in your post above is a Hyundai Ioniq 5 EV. pic.twitter.com/4WT3sZ2DHm
— Sawyer Merritt (@SawyerMerritt) February 17, 2026
Many media outlets stated the car was in “self-driving mode” or “Autopilot mode” when the car crashed. The truth is, now that Tesla has chimed in, that the driver had manually overriden the system by pressing the accelerator. Elluswamy commented on the unfair reporting:
“This blatantly irresponsible reporting does more harm to people than they realize.
Using Tesla self-driving is far safer than manual driving, and this was measured over 10B miles.
Planting such FUD in the minds of general public, who might not know the all the facts, might prevent them from using this technology that makes them safer.”
This blatantly irresponsible reporting does more harm to people than they realize.
Using Tesla self-driving is far safer than manual driving, and this was measured over 10B miles.
Planting such FUD in the minds of general public, who might not know the all the facts, might…
— Ashok Elluswamy (@aelluswamy) June 22, 2026
The damage these headlines do to Tesla and the self-driving car movement is unexplainable. Most people do not realize the safeguards that are in place with Tesla’s self-driving functions; many people who have used it know the car would never travel at that speed in a residential area, not even on the most aggressive “Mad Max” setting.
It is important to remember that Tesla Full Self-Driving is not autonomous, and the company never claimed it was. Drivers are still responsible for paying attention and remaining vigilant. They must be able to take over at all times.
Tesla Supercharging is getting yet another layer of gamification, as the company is rolling out a new competition that could win Free Supercharging miles.
Tesla is ramping up its efforts to make vehicle ownership more engaging through gamification. In June 2026, the company announced the 2026 Free Supercharging Competition, building on the Charging Passport feature introduced the previous year. This initiative turns Supercharging into a competitive, collectible adventure while offering substantial real-world incentives.
🚨 Tesla is taking its gamification of Supercharging a step further with the launch of the 2026 Free Supercharging Contest:
“In January 2027, Tesla will celebrate nine outstanding Supercharger users from 2026 by awarding them free Supercharging for their Tesla vehicle for as… pic.twitter.com/CPPYJLJwFD
— TESLARATI (@Teslarati) June 23, 2026
The Charging Passport, rolled out late last year, functions like a digital travel log or a year-in-review for Tesla owners. These types of things are used by many platforms, including Spotify and Apple Music, which show listeners what type of taste they had for the year.
Accessed in the Tesla App under the ‘Charging’ section, it displays a map of visited Superchargers, key stats, such as total energy charged (kWh), number of unique sites, total charging sessions, top charging day, and miles added. Owners earn collectible Charging Badges in categories, which include:
- Charging Milestones – for total energy, consecutive weeks of Supercharging, or unique sites visited
- Iconic Chargers – for Flagship Locations or stations near famous landmarks
- Special Events – limited-time badges for specific experiences. These badges appear within 24 hours of qualifying activity and provide a fun, shareable recap of an owner’s Supercharging journeys. Milestone progress resets annually, allowing fresh challenges each year
The 2026 contest elevates this gamification by rewarding top performers with lifetime free Supercharging. All Supercharging sessions from January 1 to December 31, 2026, count toward the competition. To participate, owners must enable “Share Charging Data with Tesla App” in vehicle settings and open the 2026 Charging Passport in the app at least once before January 1, 2027.
Nine winners will be selected — three per region (Americas, Asia-Pacific, and EMEA, with some countries excluded for regulatory reasons) — one in each of three categories:
- Longest Trip: Longest continuous streak of unique Supercharger locations where each new site is visited within 24 hours of the previous session’s start time
- Most Unique Supercharger Sites Visited: Highest number of distinct locations
- Most Energy Supercharged: Highest total in kWh charged at Superchargers
A unique site is defined as shown in the Tesla app or vehicle navigation. Repeat visits during a streak are allowed but do not extend the count. Ties are broken by total energy charged. Ineligible participants include vehicles already receiving free Supercharging, commercial-use vehicles (taxi, rideshare, delivery), Tesla employees and their immediate families, and residents of certain excluded countries.
Winners receive free Supercharging on the winning vehicle for as long as they own or lease it.
This contest is part of Tesla’s broader gamification strategy. The Safety Score has long rewarded safe driving habits with a numerical rating that can influence insurance rates or feature access. The referral program incentivizes owners with credits or free Supercharging months for successful referrals.
In-app statistics, streaks, and community features further encourage engagement. Older third-party apps even awarded “mayor” titles for frequenting specific Superchargers.
By combining digital badges, competitive leaderboards, and high-value rewards, Tesla boosts network utilization, gathers usage data, and fosters deeper owner loyalty. The 2026 Free Supercharging Competition invites enthusiasts to plan epic road trips while turning everyday charging into a rewarding pursuit. With the Passport already proving popular, expect heightened activity across the Supercharger network throughout the year.
SpaceX’s newest Starmind will make earth data centers obsolete
Tesla pushes back against unfair reporting of accidents
