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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.
In Pennsylvania, we got between 10 and 12 inches of snow over the weekend as a nasty Winter storm ripped through a large portion of the country, bringing snow to some areas and nasty ice storms to others.
I have had a Model Y Performance for the week courtesy of Tesla, which got the car to me last Monday. Today was my last full day with it before I take it back to my local showroom, and with all the accumulation on it, I decided to run a cool little experiment: How long would it take for Tesla’s Defrost feature to melt 8 inches of snow?
Tesla’s Defrost feature is one of the best and most underrated that the car has in its arsenal. While every car out there has a defrost setting, Tesla’s can be activated through the Smartphone App and is one of the better-performing systems in my opinion.
It has come in handy a lot through the Fall and Winter, helping clear up my windshield more efficiently while also clearing up more of the front glass than other cars I’ve owned.
The test was simple: don’t touch any of the ice or snow with my ice scraper, and let the car do all the work, no matter how long it took. Of course, it would be quicker to just clear the ice off manually, but I really wanted to see how long it would take.
Tesla Model Y heat pump takes on Model S resistive heating in defrosting showdown
Observations
I started this test at around 10:30 a.m. It was still pretty cloudy and cold out, and I knew the latter portion of the test would get some help from the Sun as it was expected to come out around noon, maybe a little bit after.
I cranked it up and set my iPhone up on a tripod, and activated the Time Lapse feature in the Camera settings.
The rest of the test was sitting and waiting.
It didn’t take long to see some difference. In fact, by the 20-minute mark, there was some notable melting of snow and ice along the sides of the windshield near the A Pillar.
However, this test was not one that was “efficient” in any manner; it took about three hours and 40 minutes to get the snow to a point where I would feel comfortable driving out in public. In no way would I do this normally; I simply wanted to see how it would do with a massive accumulation of snow.
It did well, but in the future, I’ll stick to clearing it off manually and using the Defrost setting for clearing up some ice before the gym in the morning.
Check out the video of the test below:
❄️ How long will it take for the Tesla Model Y Performance to defrost and melt ONE FOOT of snow after a blizzard?
Let’s find out: pic.twitter.com/Zmfeveap1x
— TESLARATI (@Teslarati) January 26, 2026
Tesla Robotaxi ride-hailing without a Safety Monitor is proving to be a difficult task, according to some riders who made the journey to Austin to attempt to ride in one of its vehicles that has zero supervision.
Last week, Tesla officially removed Safety Monitors from some — not all — of its Robotaxi vehicles in Austin, Texas, answering skeptics who said the vehicles still needed supervision to operate safely and efficiently.
BREAKING: Tesla launches public Robotaxi rides in Austin with no Safety Monitor
Tesla aimed to remove Safety Monitors before the end of 2025, and it did, but only to company employees. It made the move last week to open the rides to the public, just a couple of weeks late to its original goal, but the accomplishment was impressive, nonetheless.
However, the small number of Robotaxis that are operating without Safety Monitors has proven difficult to hail for a ride. David Moss, who has gained notoriety recently as the person who has traveled over 10,000 miles in his Tesla on Full Self-Driving v14 without any interventions, made it to Austin last week.
He has tried to get a ride in a Safety Monitor-less Robotaxi for the better part of four days, and after 38 attempts, he still has yet to grab one:
Wow just wow!
It’s 8:30PM, 29° out ice storm hailing & Tesla Robotaxi service has turned back on!
Waymo is offline & vast majority of humans are home in the storm
Ride 38 was still supervised but by far most impressive yet pic.twitter.com/1aUnJkcYm8
— David Moss (@DavidMoss) January 25, 2026
Tesla said last week that it was rolling out a controlled test of the Safety Monitor-less Robotaxis. Ashok Elluswamy, who heads the AI program at Tesla, confirmed that the company was “starting with a few unsupervised vehicles mixed in with the broader Robotaxi fleet with Safety Monitors,” and that “the ratio will increase over time.”
This is a good strategy that prioritizes safety and keeps the company’s controlled rollout at the forefront of the Robotaxi rollout.
However, it will be interesting to see how quickly the company can scale these completely monitor-less rides. It has proven to be extremely difficult to get one, but that is understandable considering only a handful of the cars in the entire Austin fleet are operating with no supervision within the vehicle.
Tesla has given its biggest hint that Full Self-Driving in Europe is imminent, as a new feature seems to show that the company is preparing for frequent border crossings.
Tesla owner and influencer BLKMDL3, also known as Zack, recently took his Tesla to the border of California and Mexico at Tijuana, and at the international crossing, Full Self-Driving showed an interesting message: “Upcoming country border — FSD (Supervised) will become unavailable.”
FSD now shows a new message when approaching an international border crossing.
Stayed engaged the whole way as we crossed the border and worked great in Mexico! pic.twitter.com/bDzyLnyq0g
— Zack (@BLKMDL3) January 26, 2026
Due to regulatory approvals, once a Tesla operating on Full Self-Driving enters a new country, it is required to comply with the laws and regulations that are applicable to that territory. Even if legal, it seems Tesla will shut off FSD temporarily, confirming it is in a location where operation is approved.
This is something that will be extremely important in Europe, as crossing borders there is like crossing states in the U.S.; it’s pretty frequent compared to life in America, Canada, and Mexico.
Tesla has been working to get FSD approved in Europe for several years, and it has been getting close to being able to offer it to owners on the continent. However, it is still working through a lot of the red tape that is necessary for European regulators to approve use of the system on their continent.
This feature seems to be one that would be extremely useful in Europe, considering the fact that crossing borders into other countries is much more frequent than here in the U.S., and would cater to an area where approvals would differ.
Tesla has been testing FSD in Spain, France, England, and other European countries, and plans to continue expanding this effort. European owners have been fighting for a very long time to utilize the functionality, but the red tape has been the biggest bottleneck in the process.
Tesla Europe builds momentum with expanding FSD demos and regional launches
Tesla operates Full Self-Driving in the United States, China, Canada, Mexico, Puerto Rico, Australia, New Zealand, and South Korea.
Tesla winter weather test: How long does it take to melt 8 inches of snow?
Tesla Robotaxi ride-hailing without a Safety Monitor proves to be difficult
