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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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.”
A common misconception of anti-EV proponents is that the batteries used in the vehicles are detrimental to the environment and that they cause more waste than they are worth. But a look at Tesla’s battery recycling efforts last year shows the company is doing more than ever to recover materials and give portions of the cells a second life.
Tesla reported a significant milestone in its sustainability efforts last year, with battery recycling volumes rising 20% compared to 2024. According to the company’s 2025 Impact Report, Tesla recycled over 14,000 metric tons of battery material through a combination of in-house processing at its Gigafactories and collaborations with third-party recycling partners.
Tesla: “In 2025, we recycled over 14,000 metric tons of battery material through a combination of in-house processing and through our network of recycling partners.”
That’s equivalent to 46,000 long-range battery packs, a +20% increase from 2024. pic.twitter.com/TC3Nz7Kaqf
— Sawyer Merritt (@SawyerMerritt) July 7, 2026
This amount of recovered material is equivalent to the resources needed to produce approximately 46,000 long-range battery packs. The increase reflects growing operational scale as Tesla’s global vehicle fleet expands and more batteries reach end-of-life or manufacturing scrap becomes available for processing.
Tesla and Battery Recycling
Battery recycling forms a core part of Tesla’s circular economy strategy. The company designs its batteries for longevity, often exceeding 200,000 miles of driving, and prioritizes repairs, remanufacturing, and second-life applications before full recycling.
Once packs are decommissioned, Tesla ensures 100% are recycled with no materials sent to landfills. This approach recovers critical metals including lithium, nickel, cobalt, and copper, which can be refined and reused in new battery production.
Tesla has advanced hydrometallurgical recycling processes capable of achieving recovery rates up to 98% for key battery metals. These methods are more efficient and environmentally friendly than traditional pyrometallurgical techniques, reducing energy use and enabling higher-purity materials suitable for direct reintegration into battery manufacturing.
Tesla co-founder JB Straubel confirms Redwood’s battery recycling operations are already profitable
In-house capabilities are supplemented by a network of specialized partners, creating a robust system that handles both production scrap and end-of-life packs.
The environmental and economic benefits are substantial. Recycling reduces reliance on virgin mining, lowers the carbon footprint associated with raw material extraction and processing, and helps stabilize supply chains for critical minerals amid rising global EV demand. As millions of Tesla vehicles age, the volume of recyclable material is expected to grow significantly in the coming years.
This 20% year-over-year growth demonstrates the effectiveness of Tesla’s investments in recycling infrastructure and technology. It positions the company as a leader in addressing one of the automotive industry’s major sustainability challenges. Continued innovation in battery design for easier disassembly and higher recyclability will further enhance these efforts.
Overall, Tesla’s progress in 2025 highlights how scaling recycling operations supports both environmental goals and long-term business resilience in the transition to electric mobility. As the EV market matures, such closed-loop systems will become increasingly vital for sustainable growth.
Tesla has spent years trying to engineer its way out of the automotive paint shop, one of the most expensive, space-consuming, and environmentally costly steps in vehicle manufacturing. With the Cybercab, Tesla confirmed on X this week that a new reaction injection molding process will embed color directly into the panel itself during production.
“Our new reaction injection molding (RIM) process shrinks Cybercab paint cycles from hours to minutes. This cuts those parts’ manufacturing and supply chain emissions by 35% and eliminating 100% of paint volatile organic compounds (VOCs) emitted in traditional paint methods.” noted Tesla.
While the RIM process isn’t necessarily new and has existed since the 1960s, what makes Tesla’s application notable is how it is being used specifically for exterior body panels that traditionally required a separate paint process after forming.
Tesla’s RIM approach integrates the color directly into the panel material during the molding process itself. The pigment is part of the polymer mix injected into the mold, meaning the panel comes out of the mold already colored, with no separate paint application required. The clear coat or protective layer can be applied at the mold stage or through a much faster post-process than traditional multi-stage painting. Tesla claims this compresses what was a multi-hour paint cycle into minutes per panel.
Tesla’s obsession with killing the paint shop is one of the most consistent threads running through the company’s manufacturing philosophy going back years. As far back as 2018, Musk was trimming paint color options to simplify production, tweeting at the time: “Moving 2 of 7 Tesla colors off menu on Wednesday to simplify manufacturing.” Two years later, in a 2020 Automotive News interview, Musk laid out his broader vision, saying he believed Tesla factories could one day be 1,000 times more efficient than conventional plants, and pointing to the paint shop as one of the biggest sources of waste, cost, and complexity. The Cybertruck was the most extreme expression of that thinking. Tesla chose an unpainted stainless steel exterior partly because it would eliminate the need for a $200 million paint facility at Gigafactory Texas. The stainless approach proved harder and more expensive than anticipated, but the underlying ambition never changed. The Cybercab is what happens when that same ambition meets a manufacturing process that delivers on it.
Radiologist who drove Tesla off cliff has attempted murder charges dismissed
Tesla battery recycling efforts increased 20 percent last year
