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Tesla FSD Beta V11.4.1 has ‘major architectural improvements’: Elon Musk

Credit: Whole Mars Blog | Twitter

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Tesla’s release notes for FSD Beta V.11.4.1 are out now. 

Version 11.4.1 may seem like another minor update, but Elon Musk commented that it introduces some significant improvements to FSD Beta. In a tweet, Musk hinted about what’s to come in FSD Beta V.12. 

“As mentioned earlier, V11.4.1 has major architectural improvements it’s actually much more than a point release. Should arguably be v12.0, but that reserved for when FSD is fully AI from video in to control out,” Musk commented.

Below are all the improvements introduced in V11.4.1. 

  • Improved control through turns, and smoothness in general, by improving geometry, curvature, position, type and topology of lanes, lines, road edges, and restricted space. Among other improvements, the perception of lanes in city streets improved by 36%, forks improved by 44%, merges improved by 27% and turns improved by 16%, due to a bigger and cleaner training set and updated lane-guidance module. 
  • Added lane-guidance inputs to the Occupancy Network to improve detections of long-range roadway features, resulting in a 16% reduction in false negative median detections. 
  • Improved ego’s assertiveness for crossing pedestrians in cases where ego can easily and safely cross before the pedestrian. 
  • Improved motorbike recall by 8% and increased vehicle detection precision to reduce false positive detections. These models also add more robustness to variance in vision frame-rate.
  • Reduced interventions cause by other vehicles cutting into ego’s lane by 43%. This was accomplished by creating a framework to probabilistically anticipate objects that may cut into ego’s lane and proactively offset and/or adjust speed to position ego optimally for these futures. 
  • Improved cut-in control by reducing lane-centric velocity error by 40-50% for close-by vehicles. 
  • Improved recall for object partial lane encroachment by 20%, high yaw-rate cut-in by 40%, and cut-out by 26% by using additional features of the lane-change trajectory to improve supervision. 
  • Reduced highway false slowdowns related to underestimated velocities for faraway object by adding 68K videos to the training set with improved auto-labeled ground truth. 
  • Smoothed in-lane offsetting for large vehicles by tuning the amount of lateral jerk allowed for the maneuver. 
  • Improved lateral control for upcoming high-curvature merges to bias away from the merging lane. 

Do you notice a change in V11.4.1? I’d like to hear your thoughts on the FSD beta update. Please get in touch with me at maria@teslarati.com or via Twitter @Writer_01001101.

Maria--aka "M"-- is an experienced writer and book editor. She's written about several topics including health, tech, and politics. As a book editor, she's worked with authors who write Sci-Fi, Romance, and Dark Fantasy. M loves hearing from TESLARATI readers. If you have any tips or article ideas, contact her at maria@teslarati.com or via X, @Writer_01001101.

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Tesla crosses major Unsupervised Self-Driving milestone

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Credit: Tesla

Tesla has reached a notable benchmark in its autonomous driving program after its Robotaxi fleet surpassed one million miles of unsupervised operation. The company made the announcement during its Cybercab event in Austin on September 3.

Tesla Vice President of AI Ashok Elluswamy told attendees he was happy to report the fleet had achieved one million miles of unsupervised Robotaxi operation as a testament to safety.

The new total marked a sharp increase from the 380,000 unsupervised miles Tesla disclosed during its second-quarter 2026 earnings update in late July.

In roughly six weeks, the company added about 620,000 miles. That acceleration followed Tesla’s decision to remove in-vehicle safety monitors from most of its operations outside the San Francisco Bay Area.

Credit: Tesla

Tesla first launched Robotaxi service in Austin in June 2025 with safety drivers present. It later began fully unsupervised rides and expanded into Dallas, Houston, Miami, Orlando, and Tampa. The San Francisco Bay Area remains the exception, where a safety monitor still rides in the vehicle under California permitting rules.

The company has not released a city-by-city breakdown of the one million unsupervised miles.

The milestone arrived as Tesla began offering public Cybercab rides in Austin. The purpose-built vehicle has no steering wheel or pedals and is designed only for autonomous ride-hailing. Production versions joined the existing fleet of modified Tesla vehicles already operating in the service.

Tesla’s unsupervised mileage is growing at a double-digit weekly rate according to earlier company comments, yet its fleet size remains modest compared with established competitors. Waymo has accumulated more than 200 million fully autonomous rider-only miles. Tesla has described its own unsupervised operations as having recorded zero notable incidents in the period leading up to the July update.

The one-million-mile figure reflects Tesla’s shift from supervised testing to broader driverless service in multiple states. It also highlights the company’s strategy of using both existing Model Y vehicles and the new Cybercab to scale its network.

Credit: Tesla

Whether the rapid recent growth continues will depend on further city expansions, regulatory approvals, and the performance of the purpose-built Cybercab in everyday paid rides. Tesla has not specified how many of the latest miles involved the new vehicle versus the rest of the fleet.

The announcement underscores Tesla’s progress toward a larger robotaxi network while illustrating the remaining gap in total autonomous experience relative to longer-operating rivals.

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Tesla Robotaxi will be a 24/7 service: here’s when

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Credit: @AdanGuajardo/X

Tesla AI lead Ashok Elluswamy said this week that 24-hour Robotaxi service is close. Replying on X to a rider who wanted Cybercab trips all night, he wrote that the capability would arrive “next month or so” once “the next tech to merge on the v15 plan” is ready.

The comment landed on September 4, one day after Tesla opened public Cybercab rides in Austin. It is the clearest near-term timeline yet for overnight unsupervised operation. Tesla’s paid Robotaxi network currently runs from 6 a.m. to 10 p.m. seven days a week across Austin, Dallas, Houston, Miami, Orlando, and Tampa.

That 16-hour window is shorter than the 6 a.m. to 2 a.m. schedule the company used for much of the prior year.

Elluswamy did not name the specific feature or say whether the change would apply first to purpose-built Cybercabs, the existing Model Y fleet, or both. He also offered no city-by-city rollout list. The link to Full Self-Driving v15 is nevertheless significant.

Tesla has described v15 as a step-change architecture with seven parallel improvement tracks and roughly ten times more parameters than earlier builds. Early versions of that software already operate on the Robotaxi fleet and contain about 40 percent of the planned gains.

By July 2026, the unsupervised fleet had logged more than 380,000 miles across six cities in two states with what the company called an impeccable safety record and no notable incidents caused by the vehicles themselves. Tesla has repeatedly argued that camera-based end-to-end neural networks, rather than extra sensors, are the core of the solution.

Overnight service would test that claim in lower-light conditions and would also raise vehicle utilization, a key variable for Robotaxi unit economics. The company has already begun using public Superchargers at night and is building dedicated Robotaxi charging sites.

Riders have asked why software must change if the cars already drive in the dark. The practical answer appears to be reliability and scale: Tesla has held back mass expansion until more of the v15 stack is merged, citing the need for higher confidence before putting thousands of unoccupied vehicles on streets around the clock.

If the next module arrives on the timetable Elluswamy sketched, 24-hour service could begin in October 2026 in at least some markets.

That would mark a shift from a daytime-bounded pilot to a service that can run whenever demand exists, including the late-night hours that have so far remained out of reach.

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Tesla Full Self-Driving will now overtake manual driving to avoid disaster

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Credit: Tesla

Tesla is beginning to roll out Full Self-Driving Supervised v14.3.9 with a new active safety layer that can take control even when the driver is operating the car manually.

Tesla AI said the software can activate FSD on the driver’s behalf when an imminent collision is detected and Automatic Emergency Braking may not be enough. It may also engage if the system detects heavy distraction or an accidental FSD disengagement.

The capability is essentially Automatic Collision Evasion. However, unlike conventional AEB, which mainly applies the brakes in a straight line, this feature can use steering, braking, and acceleration together if the car calculates that stopping alone will not prevent impact and a safer path exists. The system may change lanes or move toward a shoulder when conditions allow, then continue driving after the immediate threat is handled rather than simply coming to a stop.

The intervention is meant as a last-resort safety net, not a replacement for attentive driving.

Tesla Full Self-Driving v14.3.7 early review: FSD saved me from an accident

Tesla’s own description still frames FSD as supervised assistance. Secondary reports on internal release notes say the feature can fire while the car is being driven manually if cabin-camera monitoring suggests the driver is not sufficiently attentive, such as reaching toward the back seat, or if FSD appears to have been turned off unintentionally.

After the emergency maneuver, the car is expected to alert the driver and request a return to manual control.

The safety case is straightforward. Many collisions happen in the last second because a driver is looking away, fumbles a control, or faces an obstacle that braking cannot fully solve. A system that can both recognize that AEB is insufficient and execute a coordinated evasive path can reduce those remaining high-severity events.

Re-engaging after accidental disengagement also addresses a practical failure mode: a small steering nudge that drops FSD at the worst moment. The advantage is a background safety net that uses the same vision stack already running in v14, instead of leaving the car solely to emergency braking once the driver is no longer in command.

The feature still depends on FSD being enabled and, according to reports, an active FSD purchase or subscription. It does not make the vehicle unsupervised. Drivers remain responsible, and Tesla has not published how often the system is expected to intervene or how it will handle false positives.

If the rollout is conservative and the false-alarm rate stays low, the update is a meaningful step: FSD is no longer only a feature the driver turns on. In the rare moments when disaster is already forming, it can step in.

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