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SpaceX makes rocket fairing catch look easy with “autopilot” recovery
SpaceX has made Falcon 9 rocket fairing recovery look easy in a video of the latest nosecone catch, published hours after the company’s successful Starlink-10 launch.
Posted on Twitter by Elon Musk not long after a SpaceX webcast host and engineer revealed that one of two fairing catch attempts had been successful, the video offers the best in-action view yet of an operational fairing recovery. Backed by elevator music, it also certainly carries a clear signature of the CEO’s humor, carrying the torch from previous hits like “How Not to Land an Orbital Rocket Booster“, “Grasshopper vs. Cows“, and the successful launch of a Tesla Roadster and spacesuit-wearing mannequin into interplanetary space.
Lackadaisical theme song aside, Musk also shed some light on the actual process of catching Falcon fairings with giant ships and nets. Those new details point towards a major improvement made in the last six or so months that’s helped enable an unprecedented three successful fairing catches in less than 30 days.
According to Musk, SpaceX caught the Starlink-10 fairing half with both recovery ship GO Ms. Tree and the parasailing fairing half “operating on (SpaceX) autopilot.” While his comments leave a great deal of room for interpretation, they seem to imply that SpaceX has found ways to make fairing recovery almost as automatic as Falcon booster landings. During Falcon first stage recovery, the booster and drone ship technically operate as if the other doesn’t exist – the ship simply station keeps in a very specific location and the booster targets that same specific location.
Fairing recovery, as SpaceX would quickly find out, was a dramatically more complex and touchy ballet of humans, machinery, and rocket parts. Little is known about the specifics of fairing recovery beyond the fact that fairing halves have cold gas thrusters for positioning in vacuum and use GPS-guided parafoils to travel towards a rough landing zone. For most prior attempts, it’s believed that one or several crew members were responsible for manually maneuvering the recovery ship during catch attempts.
Including controlled helicopter drop tests, SpaceX failed a dozen or more consecutive fairing catch attempts and even shipped the entire operation from California to Florida before the first successful catch finally came in June 2019. In an apparent fluke, SpaceX managed to catch another fairing half less than two months later. Five months later, SpaceX secured its third fairing catch – possibly the very same fairing half caught on Monday. Another six months after #3, SpaceX hit a major milestone, simultaneously catching both halves of a Falcon fairing with two separate ships on July 21st, 2020.
Now, just 29 days after that spectacular double catch, SpaceX has caught another Falcon 9 fairing half – tempered only by the fact that sister ship Ms. Chief missed her own catch attempt. While it could certainly be a fluke of luck akin to SpaceX’s back-to-back STP-2 and Amos-17 catches, Musk’s note that “fairing chute control & ship control are closing the loop locally” points to cautious optimism.
Cryptic as ever, the comment seems to imply that SpaceX has debuted – or at least recently introduced – a kind of cooperative, autonomous navigation system that allows Falcon fairings and their recovery ships to communicate and function as a unit. For now, we’ll have to wait for the next catch attempt to get a better idea of just how much of a step forward SpaceX has made. SAOCOM 1B, SpaceX’s next Falcon 9 fairing recovery (and launch), is currently scheduled no earlier than (NET) August 27th.
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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.
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.
next month or so. the next tech to merge on the v15 plan will enable it.
— Ashok Elluswamy (@aelluswamy) September 4, 2026
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.
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.
FSD Supervised v14.3.9 starting to roll out shortly
This release includes a new active safety feature set: FSD Supervised can now activate on your behalf when an imminent collision is detected and Automatic Emergency Braking (AEB) may not be enough.
It may also engage if we…
— Tesla AI (@Tesla_AI) September 4, 2026
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.
Tesla crosses major Unsupervised Self-Driving milestone
Tesla Robotaxi will be a 24/7 service: here’s when
