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 is being sued by the family of the woman who was killed in a Texas crash involving a Model 3. The driver, who is also being sued, claimed the vehicle was operating on Autopilot mode, but Tesla executives have come out challenging that claim, stating that the driver of the vehicle overrode the system.
The lawsuit was filed by 76-year-old Martha Avila’s daughter and her husband, who allege a “design defect” involving a Tesla and a failure to warn. The suit alleges negligence against Tesla and the driver, Michael Butler.
Butler “stated he was operating with an automated driving assistance system engaged at the time of the crash,” the Harris County Sheriff’s Office said in a statement. He showed no signs of intoxication and was cooperative, the Sheriff’s Office said, according to NBC News.
Just after reports of the crash and numerous headlines that immediately blamed Tesla’s Autopilot suite, both Tesla CEO Elon Musk and Head of AI Ashok Elluswamy challenged that. Musk said the crash made “no sense” given that Tesla Autopilot and Full Self-Driving do not travel at the speeds the door cameras captured the car traveling at, which Tesla says was 73 MPH.
Tesla finally clarifies fatal Texas crash, confirms driver manually overrode acceleration
Elluswamy also revealed that Tesla data showed Butler overrode the system by pressing the accelerator to 100%, and that the pedal was compressed fully even after the car had crashed. Tesla has not released this data to the public, likely because it is communicating with agencies like the NHTSA on an investigation.
The suit uses a Washington Post analysis of government data that “identified at least 17 fatal incidents linked to Tesla Autopilot.”
This is far from the first time an accident has been blamed on Autopilot. A fatal crash in Texas was blamed on Autopilot several years ago, but when Tesla released data to the NTSB, which was investigating the crash, Autopilot was not available where the crash occurred, and Autosteer was never enabled, meaning the car was manually controlled at the time of the accident.
“Application of the accelerator pedal was found to be as high as 98.8 percent,” the NTSB said in their findings. The highest recorded speed in the five seconds leading up to the impact was 67 miles per hour. The area where the crash occurred is residential, and Texas State laws… pic.twitter.com/XGD97NHVZ2
— TESLARATI (@Teslarati) March 18, 2026
More information on the accident will be released as Tesla works with agencies to find the cause of the crash. From personal experience, it is hard to imagine Tesla Autopilot or FSD operating in this manner. It drives sometimes too cautiously in residential areas in parking lots, at least in my experience. Speeding happens, but at this rate in this type of area, it is hard to believe.
We look forward to more details being released with time.
The Insurance Institute for Highway Safety (IIHS) has awarded the 2025-2026 Tesla Cybertruck crew cab pickup its highest honor: Top Safety Pick+. This marks the Cybertruck as the only full-size pickup to achieve this distinction in recent evaluations.
The award applies specifically to vehicles built after April 2025, following structural upgrades including front underbody reinforcements and footwell modifications.
These changes enabled strong performance in updated crash tests. The Cybertruck earned “Good” ratings in the small overlap front (driver and passenger sides), updated moderate overlap front, and updated side tests—core requirements for the Top Safety Pick+ designation.
It also secured acceptable or good headlights across trims and a “Good” rating for its standard front crash prevention system in pedestrian scenarios, along with acceptable or good performance in vehicle-to-vehicle testing.
The Cybertruck avoided every single pedestrian collision, including:
- Daytime child crossing
- Nightitime adult crossing
- Night parallel adult
In IIHS pedestrian front crash prevention tests, @Cybertruck avoided every single collision – daytime, nighttime & different angles
It was also the only pickup to earn Top Safety Pick+ (highest award) in 2026https://t.co/BNPqT9TbsW pic.twitter.com/M6nwDisBFK
— Tesla (@Tesla) June 24, 2026
In the large pickup category, competitors such as the Toyota Tundra received only a standard Top Safety Pick, while the Ford F-150 and Ram 1500 did not qualify for either award. This positions the Cybertruck as a standout in occupant protection and crash avoidance among its peers.
Credit: IIHS
Ironically, the same vehicle celebrated for superior U.S. safety performance remains banned from public roads in the United Kingdom and much of Europe. Regulators there cite the Cybertruck’s sharp external edges and highly rigid stainless-steel construction as failing pedestrian-protection standards. European and UK rules require rounded surfaces on protruding parts to minimize injury risk in collisions with vulnerable road users.
Critics also point to the truck’s substantial weight and unyielding body structure, which some argue could transfer more force to other vehicles or pedestrians rather than absorbing it.
Tesla’s engineering philosophy underpins the Cybertruck’s strong IIHS results. The vehicle features a distinctive stainless-steel exoskeleton made from ultra-hard 30X cold-rolled stainless steel. This provides exceptional structural rigidity and a robust safety cage that resists deformation in side impacts and rollovers.
Engineers designed integrated load paths to channel crash forces away from the occupant compartment while allowing controlled energy absorption in key zones. Post-April 2025 refinements to the front underbody further optimized performance in overlap crashes.
Complementing the passive structure is Tesla’s advanced active safety suite, including the standard Collision Avoidance Assist system with automatic emergency braking. This contributed directly to the vehicle’s strong front crash prevention scores. The skateboard platform and low center of gravity also enhance stability and handling, reducing the likelihood of certain crashes.
The IIHS recognition highlights how Tesla’s combination of high-strength materials, structural innovation, and software-driven safety systems can deliver top-tier protection in rigorous testing. While global regulatory differences on design and pedestrian interaction continue to limit the Cybertruck’s availability outside North America, its U.S. safety credentials set a new benchmark for full-size pickups.
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 and driver sued by family of woman killed in Texas crash: what we know
Tesla Cybertruck is officially the safest pickup, IIHS says
