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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.
Tesla has officially launched public Robotaxi rides in Austin, Texas, without a Safety Monitor in the vehicle, marking the first time the company has removed anyone from the vehicle other than the rider.
The Safety Monitor has been present in Tesla Robotaxis in Austin since its launch last June, maintaining safety for passengers and other vehicles, and was placed in the passenger’s seat.
Tesla planned to remove the Safety Monitor at the end of 2025, but it was not quite ready to do so. Now, in January, riders are officially reporting that they are able to hail a ride from a Model Y Robotaxi without anyone in the vehicle:
I am in a robotaxi without safety monitor pic.twitter.com/fzHu385oIb
— TSLA99T (@Tsla99T) January 22, 2026
Tesla started testing this internally late last year and had several employees show that they were riding in the vehicle without anyone else there to intervene in case of an emergency.
Tesla has now expanded that program to the public. It is not active in the entire fleet, but there are a “few unsupervised vehicles mixed in with the broader robotaxi fleet with safety monitors,” Ashok Elluswamy said:
Robotaxi rides without any safety monitors are now publicly available in Austin.
Starting with a few unsupervised vehicles mixed in with the broader robotaxi fleet with safety monitors, and the ratio will increase over time. https://t.co/ShMpZjefwB
— Ashok Elluswamy (@aelluswamy) January 22, 2026
Tesla Robotaxi goes driverless as Musk confirms Safety Monitor removal testing
The Robotaxi program also operates in the California Bay Area, where the fleet is much larger, but Safety Monitors are placed in the driver’s seat and utilize Full Self-Driving, so it is essentially the same as an Uber driver using a Tesla with FSD.
In Austin, the removal of Safety Monitors marks a substantial achievement for Tesla moving forward. Now that it has enough confidence to remove Safety Monitors from Robotaxis altogether, there are nearly unlimited options for the company in terms of expansion.
While it is hoping to launch the ride-hailing service in more cities across the U.S. this year, this is a much larger development than expansion, at least for now, as it is the first time it is performing driverless rides in Robotaxi anywhere in the world for the public to enjoy.
Tesla has scheduled its Earnings Call for Q4 and Full Year 2025 for next Wednesday, January 28, at 5:30 p.m. EST, and investors are already preparing to get some answers from executives regarding a wide variety of topics.
The company accepts several questions from retail investors through the platform Say, which then allows shareholders to vote on the best questions.
Tesla does not answer anything regarding future product releases, but they are willing to shed light on current timelines, progress of certain projects, and other plans.
There are five questions that range over a variety of topics, including SpaceX, Full Self-Driving, Robotaxi, and Optimus, which are currently in the lead to be asked and potentially answered by Elon Musk and other Tesla executives:
- You once said: Loyalty deserves loyalty. Will long-term Tesla shareholders still be prioritized if SpaceX does an IPO?
- Our Take – With a lot of speculation regarding an incoming SpaceX IPO, Tesla investors, especially long-term ones, should be able to benefit from an early opportunity to purchase shares. This has been discussed endlessly over the past year, and we must be getting close to it.
- When is FSD going to be 100% unsupervised?
- Our Take – Musk said today that this is essentially a solved problem, and it could be available in the U.S. by the end of this year.
- What is the current bottleneck to increase Robotaxi deployment & personal use unsupervised FSD? The safety/performance of the most recent models or people to monitor robots, robotaxis, in-car, or remotely? Or something else?
- Our Take – The bottleneck seems to be based on data, which Musk said Tesla needs 10 billion miles of data to achieve unsupervised FSD. Once that happens, regulatory issues will be what hold things up from moving forward.
- Regarding Optimus, could you share the current number of units deployed in Tesla factories and actively performing production tasks? What specific roles or operations are they handling, and how has their integration impacted factory efficiency or output?
- Our Take – Optimus is going to have a larger role in factories moving forward, and later this year, they will have larger responsibilities.
- Can you please tie purchased FSD to our owner accounts vs. locked to the car? This will help us enjoy it in any Tesla we drive/buy and reward us for hanging in so long, some of us since 2017.
- Our Take – This is a good one and should get us some additional information on the FSD transfer plans and Subscription-only model that Tesla will adopt soon.
Tesla will have its Earnings Call on Wednesday, January 28.
Elon Musk shared an incredible detail about Tesla Cybercab’s potential efficiency, as the company has hinted in the past that it could be one of the most affordable vehicles to operate from a per-mile basis.
ARK Invest released a report recently that shed some light on the potential incremental cost per mile of various Robotaxis that will be available on the market in the coming years.
The Cybercab, which is detailed for the year 2030, has an exceptionally low cost of operation, which is something Tesla revealed when it unveiled the vehicle a year and a half ago at the “We, Robot” event in Los Angeles.
Musk said on numerous occasions that Tesla plans to hit the $0.20 cents per mile mark with the Cybercab, describing a “clear path” to achieving that figure and emphasizing it is the “full considered” cost, which would include energy, maintenance, cleaning, depreciation, and insurance.
Probably true
— Elon Musk (@elonmusk) January 22, 2026
ARK’s report showed that the Cybercab would be roughly half the cost of the Waymo 6th Gen Robotaxi in 2030, as that would come in at around $0.40 per mile all in. Cybercab, at scale, would be at $0.20.
Credit: ARK Invest
This would be a dramatic decrease in the cost of operation for Tesla, and the savings would then be passed on to customers who choose to utilize the ride-sharing service for their own transportation needs.
The U.S. average cost of new vehicle ownership is about $0.77 per mile, according to AAA. Meanwhile, Uber and Lyft rideshares often cost between $1 and $4 per mile, while Waymo can cost between $0.60 and $1 or more per mile, according to some estimates.
Tesla’s engineering has been the true driver of these cost efficiencies, and its focus on creating a vehicle that is as cost-effective to operate as possible is truly going to pay off as the vehicle begins to scale. Tesla wants to get the Cybercab to about 5.5-6 miles per kWh, which has been discussed with prototypes.
Additionally, fewer parts due to the umboxed manufacturing process, a lower initial cost, and eliminating the need to pay humans for their labor would also contribute to a cheaper operational cost overall. While aspirational, all of the ingredients for this to be a real goal are there.
It may take some time as Tesla needs to hammer the manufacturing processes, and Musk has said there will be growing pains early. This week, he said regarding the early production efforts:
“…initial production is always very slow and follows an S-curve. The speed of production ramp is inversely proportionate to how many new parts and steps there are. For Cybercab and Optimus, almost everything is new, so the early production rate will be agonizingly slow, but eventually end up being insanely fast.”
BREAKING: Tesla launches public Robotaxi rides in Austin with no Safety Monitor
Tesla Earnings Call: Top 5 questions investors are asking
