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Tesla slowly rolls out FSD Beta v11 for wide release

Credit: Whole Mars Blog | Twitter

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Tesla appears to have started slowly rolling out v11 of the Full Self-Driving Beta program this morning, just a few days after CEO Elon Musk stated the company would begin releasing it to more vehicles.

Tesla’s FSD Beta v11.3.1 started to roll out to employees late last year and a select few of the automaker’s long-standing testers a few weeks ago.

The controlled rollout allows Tesla to monitor its behaviors and tendencies in a highly safe way, as it can determine issues or bugs in a small sample size and fix them before a wider release begins.

On March 14, Musk stated that “ V11 starts going wide this weekend,” and we’ve heard it before. However, it appears the automaker is happy with the early reviews and is starting to release it to more vehicles.

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v11.3.2 is the newest version of the Beta and is being rolled out with Tesla Software Update 2022.45.11. According to statistics from TeslaScope, more vehicles are being updated with the new FSD Beta v11, and more drivers on the r/TeslaMotors subreddit are beginning to report that they have received the update.

Drivers who have experienced the early editions of this rollout have reported that there have been several improvements to highway driving, and inner-city street navigation has also been refined and feels more accurate than ever before, which is undoubtedly a step in the right direction.

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The full release notes for the FSD Beta are available below via TeslaScope:

  • 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.
  • Improved recall for close-by cut-in cases by 15%, particularly for large trucks and high-yaw rate scenarios, through an additional 30k auto-labeled clips mined from the fleet. Additionally, expanded and tuned dedicated speed control for cut-in objects.
  • Improved the position of ego in wide lanes, by biasing in the direction of the upcoming turn to allow other cars to maneuver around ego.
  • Improved handling during scenarios with high curvature or large trucks by offsetting in lane to maintain safe distances to other vehicles on the road and increase comfort.
  • Improved behavior for path blockage lane changes in dense traffic. Ego will now maintain more headway in blocked lanes to hedge for possible gaps in dense traffic.
  • Improved lane changes in dense traffic scenarios by allowing higher acceleration during the alignment phase. This results in more natural gap selection to overtake adjacent lane vehicles very close to ego.
  • Made turns smoother by improving the detection consistency between lanes, lines and road edge predictions. This was accomplished by integrating the latest version of the lane-guidance module into the road edge and lines network.
  • Improved accuracy for detecting other vehicles’ moving semantics. Improved precision by 23% for cases where other vehicles transition to driving and reduced error by 12% for cases where Autopilot incorrectly detects its lead vehicle as parked. These were achieved by increasing video context in the network, adding more data of these scenarios, and increasing the loss penalty for control- relevant vehicles.
  • Extended maximum trajectory optimization horizon, resulting in smoother control for high curvature roads and far away vehicles when driving at highway speeds.
  • Improved driving behavior next to row of parked cars in narrow lanes, preferring to offset and staying within lane instead of unnecessarily lane changing away or slowing down.
  • Improved back-to-back lane change maneuvers through better fusion between vision-based localization and coarse map lane counts.
  • Added text blurbs in the user interface to communicate upcoming maneuvers that FSD Beta plans to make. Also improved the visualization of upcoming slowdowns along the vehicle’s path. Chevrons render at varying opacity and speed to indicate the slowdown intensity, and a solid line appears at locations where the car will come to a stop.
  • Improved the recall and precision of object detection, notably reducing the position error of semi-trucks by 10%, increasing the recall and precision of crossing vehicles over 100m away by 3% and 7%, respectively, and increasing the recall of motorbikes by 5%. This was accomplished by implementing additional quality checks in our two million video clip autolabeled dataset.
  • Reduced false offsetting around objects in wide lanes and near intersections by improving object kinematics modeling in low speed scenarios.

I’d love to hear from you! If you have any comments, concerns, or questions, please email me at joey@teslarati.com. You can also reach me on Twitter @KlenderJoey, or if you have news tips, you can email us at tips@teslarati.com.

Joey has been a journalist covering electric mobility at TESLARATI since August 2019. In his spare time, Joey is playing golf, watching MMA, or cheering on any of his favorite sports teams, including the Baltimore Ravens and Orioles, Miami Heat, Washington Capitals, and Penn State Nittany Lions. You can get in touch with joey at joey@teslarati.com. He is also on X @KlenderJoey. If you're looking for great Tesla accessories, check out shop.teslarati.com

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Elon Musk secretly acquires $1B energy company to power the AI future

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Gage Skidmore, CC BY-SA 4.0 , via Wikimedia Commons

Elon Musk flew under the radar with his recent purchase of a $1 billion energy company, according to Federal Trade Commission (FTC) documents.

Transaction number 202612350 listed Tesla and SpaceX frontman Elon Musk as the acquiring party and CF APR Super Holdings LLC as the seller, with New APR Energy, LLC as the acquired entity. The deal, which closed without public announcement, came to light on May 14.

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Analysts inferred the deal’s scale from minority stakeholder disclosures, including one report of a 5 percent interest sold for approximately $50.4 million. Fortress Investment Group had purchased APR’s assets in late 2024, rebranded the operation as New APR Energy, and subsequently transferred ownership to Musk.

APR Energy specializes in rapidly deployable power infrastructure. The company maintains one of the world’s largest fleets of mobile gas and diesel turbines, with more than 1.1 gigawatts of generation capacity. Its modular units, which are often trailer-mounted, enable turnkey installations ranging from 20 MW to over 500 MW.

Elon Musk admits he was ‘clearly wrong’ about Anthropic

APR provides full engineering, procurement, construction, operation, and maintenance services for behind-the-meter power plants, serving everything from data centers, utilities, and industrial clients.

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The firm has expanded aggressively to meet surging demand, recently adding turbines and deploying over 100 MW for a major AI hyperscaler. Its solutions bridge critical gaps where grid interconnections face delays of two to five years, according to Yahoo.

The acquisition means something more for Musk. As he continues to expand projects in artificial intelligence, especially xAI, his AI venture, there is a greater need to supply energy-intensive supercomputing clusters, including the Colossus project, with what they need: reliable and high-capacity power.

Ownership of APR provides immediate access to flexible generation assets that can be deployed adjacent to data centers, reducing dependence on a strained infrastructure. It also complements Tesla’s energy storage business, so Musk will be able to pull from his own entities to address the rapid scaling demands of AI training and compute.

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Tesla has to fix a big problem with its old headlights, NHTSA says

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tesla model 3 first generation headlight
Credit: Tesla Asia/Twitter

Tesla had a petition protesting a recall to fix a potential issue with 2017-2023 Model Y and Model 3 vehicles’ headlights was denied, as the National Highway Traffic Safety Administration (NHTSA) disagreed with the company’s opinion of things.

The recall covers approximately 19,917 Model Y and Model 3 vehicles built from 2017 to 2023. Tesla initially submitted a noncompliance report for the headlights on these vehicles on March 15, 2024. Tesla then petitioned for an exemption from the fix, which violated FMVSS No. 108 (40 CFR 571.108), arguing that the “noncompliance is inconsequential as it relates to motor vehicle safety.

The NHTSA disagreed, stating that Tesla’s conclusion that the headlights do not increase any risk was not an opinion it shared. The agency said it disagreed with Tesla’s assumption that glare is not increased to surrounding traffic. This issue could be highlighted even more in certain weather conditions.

Tesla will be required to remedy the issue, the NHTSA ruled:

“In consideration of the foregoing, NHTSA has decided that Tesla has not met its burden of persuasion that the subject FMVSS No. 108 noncompliance is inconsequential to motor vehicle safety. Accordingly, Tesla’s petition is hereby denied, and Tesla is consequently obligated to provide notification of and free remedy for that noncompliance under 49 U.S.C. 30118 and 30120.”

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The issue here appears to be the angle of the headlights and the brightness they emit during operation. The NHTSA report states that:

“Tesla’s headlamp supplier, Marelli Automotive Lighting, tested 25 right-hand and 25 left-hand lamps, and for this sample, found the maximum photometric intensity measured in the 10°U to 90°U and 90°L to 90°R zone was between 136.2 cd and 230.1 cd for the right-hand lamps and between 117.5 cd and 160.3 cd for the left-hand lamps. According to Tesla, these tests revealed that the photometric intensity of the right-hand and left-hand headlamp lower beam on the subject vehicles may measure as much as 230.1 cd in the 10°U to 90°U and 90°L to 90°R zone, exceeding the maximum photometric intensity by 105.1 cd. Additionally, Tesla states that a left-hand lamp tested by a Transport Canada recognized laboratory measured a maximum of 171.27 cd in the 10°U to 90°U and 90°L to 90°R zone. Despite these measurements exceeding the allowed photometric maximum of 125 cd, Tesla believes that the subject noncompliance is inconsequential to motor vehicle safety.”

Tesla also argued at some points that the headlights had not been deemed responsible for any complaints, accidents, or injuries related to the noncompliance.

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NTSB findings on fatal Tesla crash tell a very different story

The NTSB confirmed the driver, not Tesla’s FSD, caused the fatal Texas house crash.

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The National Transportation Safety Board released preliminary findings Wednesday confirming that a Tesla driver, not the vehicle’s software, caused a fatal crash in Katy, Texas in June. The driver, 44-year-old Michael Butler, had engaged Full Self-Driving Supervised mode on Rose Hollow Lane, a residential street with a 30 mph speed limit, before manually overriding the system by pressing the accelerator pedal all the way to 100%. Data recovered from the 2025 Tesla Model 3 showed the vehicle was traveling over 70 miles per hour when it struck a home and killed 76-year-old Martha Avila, who was inside. Weather was clear, the road was dry, and it was daylight.

Texas man charged in fatal Tesla crash where he blamed Autopilot

Butler told authorities he had passed out at the wheel. But security camera footage obtained by the NTSB told a different story, and showed the car accelerating through an intersection before leaving the road entirely. Police also found that Butler’s phone had Google searches including the terms “Tesla FSD not aggressive enough 2026” and “Tesla FSD too timid,” raising serious questions about how he was using the system before the crash. Butler has since been charged with manslaughter. The victim’s family has filed a lawsuit against both Butler and Tesla, alleging negligence.

The NTSB findings aligned directly with what Tesla VP of AI Software Ashok Elluswamy had already stated publicly on X in the weeks after the crash, writing that “the driver manually overrode self-driving by pressing the accelerator all the way to 100%.” The data confirmed his account.

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