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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In a remarkable demonstration of how advanced vehicle technology can intersect with family care and rapid response, a Tesla Model Y equipped with Full Self-Driving (FSD) Supervised helped save a driver’s life during a severe heart attack. The incident, which occurred on November 15, 2025, highlights the life-saving potential of Tesla’s connected ecosystem.
John Brandt, 55, was driving his new 2026 Model Y Launch Edition on Interstate 20 from Atlanta toward Birmingham early that morning. He had recently received the FSD v14.1.3 update. Around 3:50 a.m., he began experiencing severe chest pain. Barely conscious and unable to safely control the vehicle, John managed to call his son, Jack Brandt.
FSD Supervised remained engaged, keeping the car steadily on course while John reached out for help.
As an authorized driver on his father’s Tesla account, Jack quickly sprang into action from his own phone. He located Tanner Medical Center in Carrollton, Georgia—a facility equipped for cardiac emergencies—via Google Maps and shared the destination directly through the Tesla app.
A Model Y driver started experiencing a medical emergency with chest pain mid-drive & called his son.
His son then remotely rerouted the car – which had FSD Supervised enabled – to the nearest hospital & let them know the vehicle was en route. ER staff were standing by on… pic.twitter.com/yi1tHISK9y
— Tesla North America (@tesla_na) June 16, 2026
The Model Y responded immediately, rerouting: it took the next exit, turned around on I-20, navigated local roads, and pulled directly up to the emergency room entrance. Jack also alerted hospital staff that a heart attack patient was en route in a Tesla.
Doctors diagnosed John with a massive STEMI heart attack, requiring immediate intervention on three blocked arteries. They later confirmed that without the swift reroute, John likely would not have survived—whether he had pulled over to wait for an ambulance or attempted to continue driving. He received life-saving treatment and is now recovering fully.
Tesla shared the story on X, including an interview video featuring John and Jack reflecting on the event. John described the terrifying onset of symptoms, while Jack detailed the ease of remote intervention thanks to the app’s features. Only authorized users with vehicle access can change navigation destinations, adding a layer of security and family coordination.
This case underscores Tesla’s emphasis on connectivity and supervised autonomy. Features like remote navigation allow loved ones to assist in real-time emergencies, while FSD handles complex driving tasks reliably. Tesla notes that FSD Supervised requires active driver supervision and is not fully autonomous; this was a specific incident, not a general emergency protocol.
The story has resonated widely, with many praising Tesla’s technology for bridging gaps in critical moments. Jack previously shared details on social media in February 2026, and Tesla’s recent post has amplified its reach. As vehicles become smarter and more connected, such integrations could redefine personal safety on the road—turning cars into proactive partners in health crises.
For Tesla owners, the incident serves as a powerful reminder to add trusted family members as authorized drivers and explore FSD capabilities. While no technology replaces professional medical care, this blend of AI-assisted driving and seamless app control proved invaluable. John’s survival stands as a testament to innovation that prioritizes human life.
In a bold declaration on X, xAI CEO Elon Musk announced that its model will be capable of creating full movies by the end of the year. Quoting an xAI post showcasing a stunning AI-generated trailer for Homer’s The Odyssey, Musk simply stated: “Full movies by the end of the year.”
The quoted video, created entirely with the newly released Grok Imagine Video 1.5, demonstrates the rapid strides in AI video generation. Crafted by creator David Thompson, the 2-minute-plus trailer reimagines the ancient epic in the style of a 1970s classical Hollywood blockbuster. It features 36 meticulously consistent shots that form a cohesive narrative world.
Full movies by the end of this year https://t.co/kkBrngWA0X
— Elon Musk (@elonmusk) June 17, 2026
Its realistic nature is truly mind-blowing, and it’s pretty amazing to think that it cool to think it could create an entire movie soon.
The trailer reimagines The Odyssey as a whole, and opens with a concept board outlining the vision: a retelling of the story using 35mm film aesthetics, classical framing, and other elements.
There are a handful of things that truly outline Grok’s capabilities:
- Scale and Physics: A bloodied Spartan helmet rests on a sandy battlefield amid smoke, marching armies, and flocks of birds. Horses gallop, chariots charge, and warriors clash with believable weight and motion.
- Emotional Depth and Dialogue: Close-ups capture intense expressions, as characters deliver lines like a warrior’s grief-stricken speech on a rocking ship.
- Cinematic Workflow: It’s hard to believe AI created this trailer, as editing and suspense are clearly detailed in this trailer
Now, why is this a big deal? AI has been a real threat to the way movies have been made over the past several decades. It’s no secret that the various AI platforms out there are becoming more capable, but Musk has said that he believes things would be “watchable” by the end of this year, and by the end of 2027, Grok would be able to create “really good” movies.
There are several issues that remain, most notably the ability to remain cohesive throughout the length of a film, energy requirements, copyright questions for training data, and artistic intent. Hollywood has created some of the greatest cinematic masterpieces over the past 100 years, but 2026 could be the year AI not only assists but also independently authors cinema.
Tesla is continuing to push the boundaries of vehicle dynamics, as its latest published patent, US12654505B2, or “Suspension Actuator System for a Vehicle,’ which has finally been pushed through.
The design, which is credited to inventors Brian Lee Doorlag, Avraham Kagan, and Justin Sill, introduces a sophisticated hybrid suspension design that blends active motor-driven control with strategic passive elements to deliver superior ride quality, energy efficiency, and resilience against road imperfections, especially potholes.
Suspension Actuator System for a Vehicle@Tesla‘s US20240383297A1 patent introduces an innovative suspension actuator system that transforms vehicle suspension control through an intelligent combination of active and passive control elements.
By implementing both series and… https://t.co/vRvlOu3Dql pic.twitter.com/2WriXgpOvr
— SETI Park (@seti_park) November 27, 2024
At the heart of the system is an active control element powered by an electric motor. This motor drives a belt connected to a ball nut assembly and threaded screw, which adjusts the effective length of the suspension strut in real time.
By extending or retracting, the actuator can lift or lower the wheel more accurately, which can end up countering road disturbances. Sensors, including accelerometers and wheel position monitors, feed data to a suspension control system that processes inputs and commands the motor instantly.
This active component doesn’t work alone. A low-rate air spring mounts in parallel with the actuator. Its primary role is to offset much of the vehicle’s static weight, dramatically reducing the power demand on the motor.
Without this, the active system would constantly fight gravity, draining energy and generating heat. The air spring handles steady-state loads efficiently, allowing the motor to focus on dynamic adjustments.
Complementing this is a series of passive control elements—a spring and an adaptive damper—placed between the actuator and the wheel. This setup filters high-frequency vibrations before they reach the active motor, preventing it from overworking on minor inputs. The adaptive damper, potentially magnetorheological or valve-controlled, further tunes damping electronically for optimal comfort and stability.
How It Differs from Traditional Suspensions
Traditional passive suspensions compromise between comfort and handling, while pure active systems can be power-hungry and complex. Tesla’s hybrid approach resolves this by delegating tasks: the parallel air spring manages weight and low-frequency body motions, the series elements absorb rapid vibrations, and the active actuator tackles larger, lower-frequency events.
The result is a smoother, more isolated cabin experience. High-frequency road noise and harshness diminish, while the vehicle maintains precise control during cornering or acceleration. Energy efficiency improves, too—lower motor loads mean reduced battery drain, potentially extending range in electric vehicles.
How It Mitigates Potholes Specifically
Potholes are a major challenge because they provide a sudden drop to the wheel plunge, jarring the body of the vehicle, risking damage. The patent explicitly addresses this. Upon detecting a pothole (via sensors or predictive mapping), the control system activates
the motor to retract the strut, effectively pulling the wheel upward to minimize downward excursion. The series spring/damper cushions the impact, while the parallel air spring maintains overall support.
This proactive “wheel retraction” prevents sharp jolts, preserving passenger comfort and protecting components. Integrated with Tesla’s road roughness mapping patents, the system could anticipate potholes from fleet data, enabling preemptive adjustments for even smoother navigation.
Future Implications for Tesla Vehicles
This technology builds on Tesla’s existing adaptive dampers and air suspension that is seen in Cybertruck, but advances toward fully active control. It could roll out to future models, including refreshed Cybertrucks or next-gen vehicles, enhancing both daily drivability and off-road capability. By minimizing power use and complexity, it aligns with Tesla’s goals of efficiency and scalability.
In summary, US12654505B2 exemplifies Tesla’s engineering philosophy: intelligent integration over brute force. This hybrid suspension promises quieter, more comfortable rides and robust pothole defense, potentially setting a new standard for automotive comfort. As Tesla iterates, drivers can look forward to roads feeling far less rough.
Tesla Full Self-Driving and App Connectivity save life in medical emergency
Elon Musk predicts Grok will start to challenge Hollywood by the end of 2026
