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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 Robotaxi has a highly-requested hardware feature not available on typical Model Ys
These camera washers are crucial for keeping the operation going, as they are the sole way Teslas operate autonomously. The cameras act as eyes for the car to drive, recognize speed limit and traffic signs, and travel safely.
Tesla Robotaxi has a highly-requested hardware feature that is not available on typical Model Ys that people like you and me bring home after we buy them. The feature is something that many have been wanting for years, especially after the company adopted a vision-only approach to self-driving.
After Tesla launched driverless Robotaxi rides to the public earlier this week in Austin, people have been traveling to the Lone Star State in an effort to hopefully snag a ride from one of the few vehicles in the fleet that are now no longer required to have Safety Monitors present.
BREAKING: Tesla launches public Robotaxi rides in Austin with no Safety Monitor
Although only a few of those completely driverless rides are available, there have been some new things seen on these cars that are additions from regular Model Ys, including the presence of one new feature: camera washers.
With the Model Y, there has been a front camera washer, but the other exterior “eyes” have been void of any solution for this. For now, owners are required to clean them manually.
In Austin, Tesla is doing things differently. It is now utilizing camera washers on the side repeater and rear bumper cameras, which will keep the cameras clean and keep operation as smooth and as uninterrupted as possible:
🚨 Tesla looks to have installed Camera Washers on the side repeater cameras on Robotaxis in Austin
pic.twitter.com/xemRtDtlRR— TESLARATI (@Teslarati) January 23, 2026
Rear Camera Washer on Tesla Robotaxi pic.twitter.com/P9hgGStHmV
— TESLARATI (@Teslarati) January 24, 2026
These camera washers are crucial for keeping the operation going, as they are the sole way Teslas operate autonomously. The cameras act as eyes for the car to drive, recognize speed limit and traffic signs, and travel safely.
This is the first time we are seeing them, so it seems as if Safety Monitors might have been responsible for keeping the lenses clean and unobstructed previously.
However, as Tesla transitions to a fully autonomous self-driving suite and Robotaxi expands to more vehicles in the Robotaxi fleet, it needed to find a way to clean the cameras without any manual intervention, at least for a short period, until they can return for interior and exterior washing.
Tesla made a dramatic change to the Online Design Studio to show its plans for Full Self-Driving, a major part of the company’s plans moving forward, as CEO Elon Musk has been extremely clear on the direction moving forward.
With Tesla taking a stand and removing the ability to purchase Full Self-Driving outright next month, it is already taking steps to initiate that with owners and potential buyers.
On Thursday night, the company updated its Online Design Studio to reflect that in a new move that now lists the three purchase options that are currently available: Monthly Subscription, One-Time Purchase, or Add Later:
🚨 Check out the change Tesla made to its Online Design Studio:
It now lists the Monthly Subscription as an option for Full Self-Driving
It also shows the outright purchase option as expiring on February 14 pic.twitter.com/pM6Svmyy8d
— TESLARATI (@Teslarati) January 23, 2026
This change replaces the former option for purchasing Full Self-Driving at the time of purchase, which was a simple and single box to purchase the suite outright. Subscriptions were activated through the vehicle exclusively.
However, with Musk announcing that Tesla would soon remove the outright purchase option, it is clearer than ever that the Subscription plan is where the company is headed.
The removal of the outright purchase option has been a polarizing topic among the Tesla community, especially considering that there are many people who are concerned about potential price increases or have been saving to purchase it for $8,000.
This would bring an end to the ability to pay for it once and never have to pay for it again. With the Subscription strategy, things are definitely going to change, and if people are paying for their cars monthly, it will essentially add $100 per month to their payment, pricing some people out. The price will increase as well, as Musk said on Thursday, as it improves in functionality.
I should also mention that the $99/month for supervised FSD will rise as FSD’s capabilities improve.
The massive value jump is when you can be on your phone or sleeping for the entire ride (unsupervised FSD). https://t.co/YDKhXN3aaG
— Elon Musk (@elonmusk) January 23, 2026
Those skeptics have grown concerned that this will actually lower the take rate of Full Self-Driving. While it is understandable that FSD would increase in price as the capabilities improve, there are arguments for a tiered system that would allow owners to pay for features that they appreciate and can afford, which would help with data accumulation for the company.
Musk’s new compensation package also would require Tesla to have 10 million active FSD subscriptions, but people are not sure if this will move the needle in the correct direction. If Tesla can potentially offer a cheaper alternative that is not quite unsupervised, things could improve in terms of the number of owners who pay for it.
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Tesla Model S completes first ever FSD Cannonball Run with zero interventions
The coast-to-coast drive marked the first time Tesla’s FSD system completed the iconic, 3,000-mile route end to end with no interventions.
A Tesla Model S has completed the first-ever full Cannonball Run using Full Self-Driving (FSD), traveling from Los Angeles to New York with zero interventions. The coast-to-coast drive marked the first time Tesla’s FSD system completed the iconic, 3,000-mile route end to end, fulfilling a long-discussed benchmark for autonomy.
A full FSD Cannonball Run
As per a report from The Drive, a 2024 Tesla Model S with AI4 and FSD v14.2.2.3 completed the 3,081-mile trip from Redondo Beach in Los Angeles to midtown Manhattan in New York City. The drive was completed by Alex Roy, a former automotive journalist and investor, along with a small team of autonomy experts.
Roy said FSD handled all driving tasks for the entirety of the route, including highway cruising, lane changes, navigation, and adverse weather conditions. The trip took a total of 58 hours and 22 minutes at an average speed of 64 mph, and about 10 hours were spent charging the vehicle. In later comments, Roy noted that he and his team cleaned out the Model S’ cameras during their stops to keep FSD’s performance optimal.
History made
The historic trip was quite impressive, considering that the journey was in the middle of winter. This meant that FSD didn’t just deal with other cars on the road. The vehicle also had to handle extreme cold, snow, ice, slush, and rain.
As per Roy in a post on X, FSD performed so well during the trip that the journey would have been completed faster if the Model S did not have people onboard. “Elon Musk was right. Once an autonomous vehicle is mature, most human input is error. A comedy of human errors added hours and hundreds of miles, but FSD stunned us with its consistent and comfortable behavior,” Roy wrote in a post on X.
Roy’s comments are quite notable as he has previously attempted Cannonball Runs using FSD on December 2024 and February 2025. Neither were zero intervention drives.
Tesla Robotaxi has a highly-requested hardware feature not available on typical Model Ys
Tesla makes big Full Self-Driving change to reflect future plans
