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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.
News
SpaceX just locked up a NASA record no other U.S. spacecraft can touch
SpaceX’s Crew-13 Dragon reached the ISS in under eight hours, and NASA confirmed a record.
SpaceX now owns every spot on the list of the five fastest trips a U.S. spacecraft has ever made to the International Space Station, and its newest entry beat the old mark by more than four hours.
Crew Dragon Grace docked to the forward port of the station’s Harmony module at 7:05 p.m. ET on October 1, just 7 hours and 55 minutes after lifting off from Space Launch Complex 40 at Cape Canaveral. NASA confirmed the milestone in a space station blog update, writing that the flight “marked the fastest launch‑to‑docking of a U.S. spacecraft in the history of the International Space Station.”
The previous U.S. record also belonged to Dragon. SpaceX’s uncrewed CRS-31 cargo mission reached the station in a little over 12 hours in November 2024. The fastest crewed trip before last week was Crew-11, which took 14 hours and 43 minutes in August 2025, according to Space.com.
A post that Elon Musk reposted on Monday filled out the rest of the ranking. Behind Crew-13, CRS-31 and Crew-11 sit Axiom’s Ax-2 mission at 15 hours and 35 minutes and NASA’s Crew-4 at 15 hours and 44 minutes. All five flew on Dragon.
SpaceX turned a heralding moment for Starship into its greatest
Crew-13 carried NASA astronauts Jessica Watkins and Luke Delaney, Canadian Space Agency astronaut Joshua Kutryk, and Roscosmos cosmonaut Sergey Teteryatnikov. NASA had projected a docking around 8 p.m. ET, as Teslarati reported the day before launch, and Dragon arrived nearly an hour early. Our launch day coverage noted that the flight was lined up to be the quickest Crew Dragon transit yet.
The speed came from timing more than hardware. SpaceX’s Julianna Scheiman said the station “was in an opportune spot in space,” which let Dragon start closing the gap almost immediately after reaching orbit. “This is close to the fastest it could be,” she added. Most Crew Dragon flights still take close to a day, using a series of Draco thruster burns to raise and phase their orbit before arrival.
Dragon’s next job at the station is a departure. NASA said Monday it is targeting 8:05 a.m. ET on Wednesday, October 7, for Crew-12 to undock, setting up a splashdown off the coast of California around 11:34 a.m. on Thursday. Clearing that port makes room for CRS-35, a cargo Dragon carrying the final set of iROSA solar arrays.
Dragon remains NASA’s only operational ride to the station while Boeing’s Starliner stays grounded, and the agency recently added Crew-15, Crew-16 and Crew-17 to SpaceX’s contract in a $946 million modification.
Elon Musk has acknowledged that early discussions with Taiwan Semiconductor Manufacturing Company (TSMC) could bring the company into his ambitious Terafab semiconductor project, signaling a possible partnership with the world’s leading contract chipmaker.
Musk confirmed that early talks are underway, but as of right now, they are “just discussions.” There is no confirmation of a deal nor dismissal of the possibility of one, leaving open the prospect of one of the largest advanced-chip collaborations under discussion in the U.S.
@wholemars Just discussions, but something may come of it
— Elon Musk (@elonmusk) October 3, 2026
The report that speculated on potential discussions between Terafab and TSMC comes from Tim Culpan, who outlined a few ways the collaboration could operate. One is TSMC using the project as an “anchor customer” for future facilities in Texas, potentially contributing process expertise, operational know-how, or capacity while Terafab provides capital, long-term purchase commitments, or both.
Tesla and SpaceX jointly developed the Terafab project, with Intel already participating on the tech side. Elon Musk announced the project in March, and it intends to produce more than one terawatt of AI compute capacity annually once fully built.
Company statements place the first phase at approximately $16.8 billion in cost, with later filings pointing to a total that could reach well into the tens of billions across multiple stages.
Intel joined the effort in April 2026 and is expected to supply its 14A manufacturing process for the full-scale plant.
Musk has said existing suppliers, including Samsung and TSMC, remain important for near-term needs; Tesla already has production arrangements with Samsung for AI5 and AI6 chips, but that future demand from Optimus robots, Cybercab vehicles, and planned space-based data centers will eventually exceed what the global industry can currently deliver.
Terafab is positioned as the long-term answer to that projected shortfall, and Tesla did something similar during COVID to avoid a chip shortage. This is just a much larger-scale solution.
If the partnership were to materialize, it would add TSMC’s industry-leading strategies to a project that already combines Tesla’s and SpaceX’s capital and offtake with Intel’s process technology. For now, the only public confirmation is Musk’s brief acknowledgement that conversations are occurring.
Tesla’s early strategy for charging units operating within its Robotaxi fleet reveals that the company surely has not lost any of that scrappy DNA that took it from an unlikely success story to the most valuable carmaker in the world.
An observer at a Tesla Supercharger in Austin spotted ten total Robotaxi vehicles arrive: one Cybercab and nine Model Y units. A Tesla employee was waiting at the lot and allowed each unit to park itself; every car that arrived had nobody in it.
Tesla wins FCC approval for wireless Cybercab charging system
The Tesla employee would walk around and plug each car in, adjusting the parking if needed:
So look at what I found. This is how Tesla charges unsupervised robotaxis at a public supercharger. Here is a driverless Cybercab showing up with no one in it. There are 9 other Model Ys that showed up too. A Tesla employee is walking around and plugging each of them in. She also moves the cars if they are not positioned well enough to charge. I love this process. One person charges multiple robotaxis at once
— Abhimanyu Yadav (@WorldlyReviewer) October 3, 2026
It’s a very interesting strategy, but extremely understandable at this early point in the Robotaxi program. It’s only been out for about 15 months, and Cybercab just entered the fleet in early September.
On top of that, Tesla is still working tirelessly on its wireless charging apparatus, and a new patent was just published regarding that product last week.
However, this is just another example of how Tesla still has plenty of that scrappy DNA leftover from the “production hell” days, when CEO Elon Musk slept on the floor of the factory, employees were working crazy hours, Tesla was building Sprung Structures to build cars in, and the company was tiptoeing on the brink of bankruptcy.
@Teslarati Sheer magnitude of the entire production system is hard to appreciate. Almost every element of production is >75% automated. Only wire harnesses & general assembly, which are <10% of production costs, are primarily manual.
— Elon Musk (@elonmusk) October 12, 2020
For now, Tesla is utilizing a simple system for recharging its ride-hailing vehicles, and that is a Tesla employee doing it manually until another solution presents itself. Sure, it’s not the most high-tech thing, and it certainly is not what people might have expected at this point in time, but it works, and it’s keeping the entire suite running.
SpaceX just locked up a NASA record no other U.S. spacecraft can touch
Elon Musk teases TSMC as potential Terafab partner
