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.
The Teslarati team would appreciate hearing from you. If you have any tips, contact me at maria@teslarati.com or via Twitter @Writer_01001101.
Investor's Corner
NASA taps SpaceX to launch the telescope that could unlock new worlds
NASA’s Roman Space Telescope heads to orbit this August aboard SpaceX’s Falcon Heavy with massive scientific ambitions.
SpaceX is set to play a central role in one of NASA’s most anticipated science missions in years. The company’s Falcon Heavy rocket, currently the most powerful operational launch vehicle in the world, will carry the Nancy Grace Roman Space Telescope into orbit on August 30 from Kennedy Space Center in Florida. Roman is now in final preparations inside the Payload Hazardous Servicing Facility, where on June 26 technicians used a crane to lift the observatory into a specialized stand for fueling and pre-launch testing.
Roman is named after Nancy Grace Roman, NASA’s first chief of astronomy, whose career helped shape how the agency approaches space science.
NASA chose SpaceX Falcon Heavy because of Roman’s needs to reach a specific orbit far from Earth, well beyond where a standard Falcon 9 can deliver it. The Falcon Heavy, which first flew in 2018, has since become NASA’s go-to option for missions that need serious muscle without the cost and complexity of older launch systems.
Celebrating SpaceX’s Falcon Heavy Tesla Roadster launch, seven years later (Op-Ed)
Roman will carry a field of view at least 100 times wider than the Hubble Space Telescope, meaning it can photograph enormous swaths of the universe in a single shot rather than the narrow slices Hubble captures. That difference in scale is significant. While Hubble reshaped our understanding of the cosmos over 30 years, Roman is built to work faster and wider, surveying hundreds of millions of galaxies at once.
One of Roman’s most compelling capabilities is its potential to discover and photograph planets orbiting stars outside our solar system, and with enough precision to directly image planets that would otherwise be lost. That means scientists could study the atmosphere and surface characteristics of distant worlds rather than simply confirming they exist. Combined with Roman’s sweeping field of view, the telescope could detect thousands of exoplanets, and some of those planets may be in habitable zones where liquid water could exist. No telescope currently in operation has this level of power and capability. That capability alone could change what we know about other worlds, and perhaps finally answer the question: are we the only intelligent lifeforms in existence?
What Roman actually finds once it reaches orbit is an open question, and that is exactly what makes this launch worth watching.
News
Tesla confirms crucial detail of Miami Robotaxi launch
Tesla has confirmed a crucial detail of its Miami Robotaxi launch, stating that the fleet is operating on an Unsupervised basis, joining a few other cities where company employees do not watch over the vehicles from inside.
Tesla’s Head of AI, Ashok Elluswamy, confirmed the detail on X, answering a highly speculated question about the Robotaxi Service in Miami, which was launched on June 3:
Unsupervised
— Ashok Elluswamy (@aelluswamy) July 3, 2026
The first launch of Robotaxi in Florida, Miami presents a unique opportunity for Tesla as it is operating the Unsupervised Robotaxi ride-hailing service in a major tourist hotspot in the Sunshine State. It also signals the suite will expand to other cities soon; many have requested Orlando, a heavy tourist spot with Disney and other resorts nearby, get access to the program soon as well.
Miami is getting a conservative rollout as well, just as Tesla has done with other cities. The initial geofence covers a compact 10–14 square mile zone in western Miami-Dade County, primarily West Miami extending toward Doral and Sweetwater. It is bounded roughly by SR-826 (Palmetto Expressway) to the north and US-41 (Tamiami Trail) to the south, excluding downtown Miami, Miami Beach, the airport, and most of Coral Gables.
Tesla has also been pretty slim on other details. For example, Tesla has not disclosed the exact fleet size, but field reports and license plate tracking indicate just two unsupervised Model Y vehicles were active on launch day, increasing to three within 48 hours.
According to The Road to Autonomy, a nearby staging lot near Miami International Airport holds dozens of Cybercabs alongside additional Model Y units, suggesting capacity for rapid scaling as demand and data collection grow.
The confirmation of Robotaxi being Unsupervised carries immense weight. It establishes that Tesla’s Miami Robotaxi operations run without human safety drivers or remote supervision, relying entirely on the company’s Full Self-Driving technology. Miami becomes the second major U.S. city after Austin to offer unsupervised Robotaxi rides from day one.
The move reflects rapid progress in Tesla’s AI efforts. Neural networks trained on vast real-world data now handle complex urban environments, including South Florida’s heavy traffic, pedestrians, and rainy conditions. Industry observers see it as validation of Tesla’s vision-centric, data-driven approach versus traditional rule-based systems; a truly unorthodox approach in this day and age.
Challenges remain, including regulatory oversight, public trust, and scaling the fleet to match geofence ambitions. Miami’s small initial footprint and limited vehicles highlight a deliberate, measured expansion strategy focused on safety and data gathering.
Nevertheless, the unsupervised confirmation marks a pivotal milestone. It showcases technical readiness and advances Tesla’s vision of transforming vehicles into autonomous revenue generators while reshaping urban mobility. For Miami users, driverless transportation has moved from concept to reality.
News
Radiologist who drove Tesla off cliff has attempted murder charges dismissed
A California radiologist who drove his Tesla Model Y off a 250-foot cliff in an attempt to kill his family has had his charges dismissed after doctors say he is “doing well” in a mental health program.
Dharmesh Patel was charged with three counts of attempted murder in connection with a January 2023 crash where he drove his Tesla off a cliff, injuring his wife and two children, aged 7 and 4 at the time.
Patel drove the Tesla off Devil’s Slide in California, an area that is extremely rough to the point that investigators and rescuers expected the worst when arriving at the scene for the first time. Patel supposedly had schizoaffective disorder, according to Deputy District Attorney Dominique Davis.
Shockingly, Patel’s wife, who was in the vehicle, testified that she did not want her husband to be prosecuted, noting that their children missed their father and they wanted him to come back home. Patel’s attorney argued, “not everyone who commits a crime is a criminal.”
Doctor who took Tesla off cliff gets support from unlikely person
A three-day trial in Mental Health Diversion Court ruled in Patel’s favor, which kept him out of jail and instead on house arrest. He was admitted to a Mental Health Diversion Program, which he successfully completed, the Associated Press reported. San Mateo County District Attorney Steve Wagstaffe said the judge was “required by law” to dismiss the charges:
“If the person who’s given mental health diversion follows the treatment plan, there’s nothing that can be done, and at the end of the two years he gets it wiped out of his record.”
Wagstaffe said he has argued, along with other DAs in California, to have attempted murder removed from the list of charges eligible to be dismissed due to mental health diversion programs.
Patel had the charges officially dismissed on Monday; his wife waited for him as he left court and they departed the building together, according to Mercury News. Patel surrendered his California medical license in December.
The crash has been one of the best examples of Tesla’s incredible engineering, which has saved four lives in this particular instance. The car was totalled but kept the four human beings alive and safe, which is something that many referred to as “an absolute miracle.”