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Stanford studies human impact when self-driving car returns control to driver
Researchers involved with the Stanford University Dynamic Design Lab have completed a study that examines how human drivers respond when an autonomous driving system returns control of a car to them. The Lab’s mission, according to its website, is to “study the design and control of motion, especially as it relates to cars and vehicle safety. Our research blends analytical approaches to vehicle dynamics and control together with experiments in a variety of test vehicles and a healthy appreciation for the talents and demands of human drivers.” The results of the study were published on December 6 in the first edition of the journal Science Robotics.
Holly Russell, lead author of study and former graduate student at the Dynamic Design Lab says, “Many people have been doing research on paying attention and situation awareness. That’s very important. But, in addition, there is this physical change and we need to acknowledge that people’s performance might not be at its peak if they haven’t actively been participating in the driving.”
The report emphasizes that the DDL’s autonomous driving program is its own proprietary system and is not intended to mimic any particular autonomous driving system currently available from any automobile manufacturer, such as Tesla’s Autopilot.
The study found that the period of time known as “the handoff” — when the computer returns control of a car to a human driver — can be an especially risky period, especially if the speed of the vehicle has changed since the last time the person had direct control of the car. The amount of steering input required to accurately control a vehicle varies according to speed. Greater input is needed at slower speeds while less movement of the wheel is required at higher speeds.
People learn over time how to steer accurately at all speeds based on experience. But when some time elapses during which the driver is not directly involved in steering the car, the researchers found that drivers require a brief period of adjustment before they can accurately steer the car again. The greater the speed change while the computer is in control, the more erratic the human drivers were in their steering inputs upon resuming control.
“Even knowing about the change, being able to make a plan and do some explicit motor planning for how to compensate, you still saw a very different steering behavior and compromised performance,” said Lene Harbott, co-author of the research and a research associate in the Revs Program at Stanford.
Handoff From Computer to Human
The testing was done on a closed course. The participants drove for 15 seconds on a course that included a straightaway and a lane change. Then they took their hands off the wheel and the car took over, bringing them back to the start. After familiarizing themselves with the course four times, the researchers altered the steering ratio of the cars at the beginning of the next lap. The changes were designed to mimic the different steering inputs required at different speeds. The drivers then went around the course 10 more times.
Even though they were notified of the changes to the steering ratio, the drivers’ steering maneuvers differed significantly from their paths previous to the modifications during those ten laps. At the end, the steering ratios were returned to the original settings and the drivers drove 6 more laps around the course. Again the researchers found the drivers needed a period of adjustment to accurately steer the cars.
The DDL experiment is very similar to a classic neuroscience experiment that assesses motor adaptation. In one version, participants use a hand control to move a cursor on a screen to specific points. The way the cursor moves in response to their control is adjusted during the experiment and they, in turn, change their movements to make the cursor go where they want it to go.
Just as in the driving test, people who take part in the experiment have to adjust to changes in how the controller moves the cursor. They also must adjust a second time if the original response relationship is restored. People can performed this experiment themselves by adjusting the speed of the cursor on their personal computers.
“Even though there are really substantial differences between these classic experiments and the car trials, you can see this basic phenomena of adaptation and then after-effect of adaptation,” says IIana Nisky, another co-author of the study and a senior lecturer at Ben-Gurion University in Israel “What we learn in the laboratory studies of adaptation in neuroscience actually extends to real life.”
In neuroscience this is explained as a difference between explicit and implicit learning, Nisky explains. Even when a person is aware of a change, their implicit motor control is unaware of what that change means and can only figure out how to react through experience.
Federal and state regulators are currently working on guidelines that will apply to Level 5 autonomous cars. What the Stanford research shows is that until full autonomy becomes a reality, the “hand off” moment will represent a period of special risk, not because of any failing on the part of computers but rather because of limitations inherent in the brains of human drivers.
The best way to protect ourselves from that period of risk is to eliminate the “hand off” period entirely by ceding total control of driving to computers as soon as possible.
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Tesla readies its autonomous Cybercab and Robotaxi cleaning service
A Texas permit just confirmed Tesla’s cleaning robot is coming to service its Cybercab and Robotaxi fleet.
A routine Texas building permit may have quietly confirmed that Tesla’s robot vacuum and autonomous cleaning bot for the Robotaxi and Cybercab is coming. A state filing with the Texas Department of Licensing and Regulation, as first discovered by Tesla enthusiast Spencer and posted to X, that project number TABS2025022006, lists the scope of work at Tesla’s Austin Robotaxi hub at 5900 E Ben White Blvd to include a “Cleaning Robot” alongside Supercharger cabinets and an Equipment Inspection System.
Tesla first showed the cleaning robot publicly on January 31, 2025, posting a short video on X with the caption “This robot sucks,” showing a large robotic arm inside a Cybercab cabin switching between attachments to vacuum debris, pick up trash, and wipe down surfaces.
The operational case for this hardware comes down to mathematics. A robotaxi running rides across Austin needs to cycle passengers continuously to generate revenue. Every minute a vehicle sits waiting for a human cleaning crew is a minute it is not earning. A robotic arm that can fully clean a Cybercab cabin between rides in under two minutes removes one of the key bottlenecks in fleet utilization that no autonomous vehicle company has yet solved at scale.
This robot sucks pic.twitter.com/VUmGfCM5B3
— Tesla (@Tesla) January 31, 2025
The 5900 E Ben White Blvd address sits roughly 12 miles southwest of Gigafactory Texas, where Tesla has been mass producing its Cybercab. The Ben White facility is expected to functions as Tesla’s Austin Robotaxi Hub, the physical base of operations where fleet vehicles return between rides to charge, get cleaned, and undergo inspection before being dispatched again – and all autonomously. One can imagine a Cybercab dropping off a passenger, routes itself back to Ben White, pulls into the cleaning station, charges on one of the Supercharger cabinets listed in the same permit, passes the equipment inspection system, and returns to service, all without a human making a single decision.
The sighting activity around both locations has accelerated in parallel with production. By mid-March 2026, Cybercabs were spotted regularly on public roads across Austin and Silicon Valley. Tesla’s Robotaxi operations in Texas has expanded to cover the entire Austin metro area and has spread to Dallas, while autonomous Cybercab employee shuttle runs at Gigafactory Texas are also set to begin soon. What it represents is the physical infrastructure behind a fleet that Tesla intends to run without anyone cleaning, driving, or dispatching it by hand.
SpaceX is preparing for the 13th integrated flight test of its Starship system, with a targeted launch as early as Thursday, July 16. The 90-minute launch window opens at 5:45 p.m. CT from Starbase in South Texas.
This comes roughly seven weeks after Flight 12 on May 22, underscoring the company’s accelerating pace in its rapid development campaign. The mission will use the latest Starship and Super Heavy V3 vehicles equipped with Raptor 3 engines. Booster 20 will attempt a controlled boostback burn, followed by a splashdown in the Gulf of Mexico, while Ship 40 will follow a suborbital trajectory.
Starship’s thirteenth flight test is preparing to launch as early as Thursday, July 16 → https://t.co/Rp7VwBzpWx pic.twitter.com/jdpFlQUEpF
— SpaceX (@SpaceX) July 11, 2026
Key objectives for Flight 13 will include demonstrating reliable stage separation, engine performance under various conditions, and controlled reentry.
A major milestone for Flight 13 is the first deployment of 20 next-generation Starlink V3 satellites. These satellites feature advanced laser links for inter-satellite communication, deployable solar arrays, and onboard cameras, six of which will capture imagery of Starship’s heat shield during flight.
Several heat shield tiles on Ship 40 will be painted white to serve as imaging targets, while additional experiments test upgraded tiles on aft flaps, modified attachments on the aft skirt, and load-sensing tiles to measure stresses. The upper stage will also attempt a single Raptor engine relight in space before a targeted splashdown in the Indian Ocean.
These tests build directly on lessons from Flight 12, which introduced the V3 configuration but encountered issues including a booster flip anomaly during boostback and an engine-out event on the ship. Hardware and software modifications on Booster 20 and Ship 40 aim to improve engine relight reliability, startup sequencing, and overall robustness.
Next Starship launch aiming for Thursday https://t.co/SajPPd4pdb
— Elon Musk (@elonmusk) July 12, 2026
The short interval between Flights 12 and 13 highlights SpaceX’s iterative approach. Elon Musk has repeatedly emphasized that Starship launches will become “incredibly common” in the coming years.
The company envisions scaling to rates as high as one launch per hour within 4-5 years, potentially enabling thousands of flights annually. Such cadence is essential for Starship’s goals: establishing orbital refueling for lunar and Mars missions, deploying massive satellite constellations, and making life multiplanetary.
With each flight, Starship edges closer to full reusability and operational maturity. Success on July 16 would mark another step toward routine access to space and the ambitious vision of humanity becoming a spacefaring civilization.
Tesla shared a striking video showcasing the decommissioning of the original Model S and Model X assembly line at its Fremont Factory in Northern California. Completed in just 46 days, the teardown involved heavy machinery dismantling concrete pits, removing robotic arms and conveyors, and clearing the space for new production.
The post, captioned “End of an era,” captured both the end of a historic chapter and Tesla’s aggressive pivot toward its next major initiative, Optimus.
End of an era: Decommissioning the original Model S & X assembly line in just 46 days pic.twitter.com/kGEdfhl62h
— Tesla Manufacturing (@gigafactories) July 10, 2026
The decision to retire the Model S and Model X originated during Tesla’s Q4 2025 Earnings Call in late January 2026. CEO Elon Musk announced that production of the company’s flagship sedan and SUV would wind down by the end of Q2 2026, describing it as bringing the programs to an “honorable discharge.”
Custom orders ceased around early April 2026, with the final vehicles rolling off the line in early May. A special signature delivery ceremony on May 20 marked the emotional close for these vehicles, which had defined Tesla’s early success and luxury EV segment since the Model S launch in 2012.
The primary reason for tearing down the lines was to repurpose the valuable factory floor space for high-volume production of Tesla’s Optimus humanoid robot. Musk had indicated on Earnings Calls that the Fremont S/X line would be replaced by a dedicated Optimus manufacturing line targeting a capacity of one million units per year.
This move aligns with Tesla’s broader strategic shift from traditional vehicle manufacturing toward robotics and artificial intelligence, leveraging the company’s expertise in autonomy, AI training, and high-volume production.
Optimus, Tesla’s general-purpose humanoid robot, is designed to perform repetitive or dangerous tasks in factories, warehouses, and eventually homes. Powered by Tesla’s AI and Neural Networks, it aims to be a versatile, affordable platform. Production of Optimus Gen 3 is already underway in limited form at Fremont, with full-scale output on the converted line expected to begin in late July or August.
Tesla is targeting rapid scaling, with internal ambitions pointing toward tens or even hundreds of thousands of units annually by the end of 2026.
Longer-term, Tesla is constructing a much larger second-generation Optimus facility at Giga Texas, with potential capacity reaching millions of units per year. The company views Optimus as a transformative product that could eventually surpass its automotive business in scale and value, enabling widespread deployment of useful robots across industries. CEO Elon Musk has even predicted it would be the most popular product of all-time.
As one era closes at Fremont, another is rapidly taking shape.
Tesla readies its autonomous Cybercab and Robotaxi cleaning service
SpaceX reveals Starship Flight 13 launch date
