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Stanford studies human impact when self-driving car returns control to driver

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Tesla Autopilot in 'Shadow Mode' will pit human vs computer

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.

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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.

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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.

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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.

"I write about technology and the coming zero emissions revolution."

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Tesla Full Self-Driving is taking over Europe: fourth country gets FSD approval

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Credit: Tesla

Tesla has secured regulatory approval for its Full Self-Driving (Supervised) system in Denmark, marking a significant step in the technology’s expansion across Europe.

Announced on June 9, the approval positions Denmark as the fourth European country to greenlight FSD Supervised, following the Netherlands, Lithuania, and Estonia.

Rollout to Danish vehicle owners is expected to begin soon, the company said.

The Danish Road Traffic Authority granted provisional approval after reviewing the original type approval issued by the Dutch vehicle authority (RDW) on April 10, 2026.

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This national recognition approach allows individual countries to bypass slower EU-wide harmonization processes, accelerating deployment. Lithuania activated the system on May 20, with Estonia following on May 29, demonstrating a rapid domino effect across the region.

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FSD Supervised enables advanced driver assistance capabilities, including automatic steering, acceleration, braking, lane changes, and navigation through complex urban and rural environments. The system is designed for supervised use, as its name states, meaning drivers must remain attentive and ready to intervene at all times.

It adapts to diverse conditions, such as rain, night driving, and varied road types common in Denmark, but it is important to note that the tech is not fully autonomous.

Following a launch in Europe just a few months ago, with its first approval coming in the Netherlands, Tesla is just now highlighting the successful start.

Early data from the Netherlands highlights strong safety performance. Between April 10 and June 5, vehicles using FSD Supervised recorded 3.5 times fewer collisions than manual driving overall, with zero crashes reported on highways across more than 16.6 million kilometers driven.

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These results underscore the potential of the technology to enhance road safety when properly supervised.

Tesla’s European push builds on its global footprint, now reaching 12 countries with FSD Supervised availability. The software receives continuous over-the-air updates, improving performance based on real-world data from millions of miles.

In Denmark, owners with compatible hardware—particularly newer vehicles equipped with Hardware 4 (HW4)—are anticipated to gain access first, though exact timelines and eligibility details will be confirmed during rollout.

This approval reflects growing regulatory confidence in supervised autonomy across Europe. As more nations recognize the Dutch certification, Tesla continues to demonstrate how its AI-driven approach can navigate real-world driving scenarios effectively. Denmark’s addition strengthens Tesla’s position in the region, paving the way for broader adoption on a continent that his been surprisingly slow to adopt the technology.

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With FSD Supervised now approved in four European markets in just two months, the technology is steadily advancing toward wider availability. Tesla aims to refine the system further through ongoing data collection and software iterations, supporting its vision for safer and more efficient transportation.

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Tesla revises FSD transfer policy on new Cybertruck trim, causing cancellations

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Credit: Tesla

Tesla has apparently revised the policy it previously had listed for Full Self-Driving transfers on the newest All-Wheel-Drive Cybertruck that the company had sold for a steal price of just $59,000 earlier this year.

After initially stating that customers who bought the pickup would be able to transfer FSD purchases, Tesla recently changed the language in those terms and conditions to reflect that this would no longer be the case.

Tesla launches new Cybertruck trim with more features than ever for a low price

The adjustment in terminology has caused a handful of orderers to cancel their reservations due to the loss of FSD transfer:

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Tesla said orders for the new Cybertruck AWD must be placed by March 31, 2026, to qualify for the FSD transfer. The language in the document from earlier this year explicitly states that they “may qualify” for the transfer program, but the date of March 31 is explicitly mentioned.

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Additionally, Tesla Delivery Advisors reached out to some orderers of the AWD Cybertruck, who were told there was “an update to the eligibility of the Full Self-Driving (Supervised) transfer.” Tesla stated they could:

  • proceed without the transfer,
  • upgrade to a Premium or Cyberbeast trim and request an FSD Transfer
  • cancel the order and be refunded the $250 order fee.

Tesla turning around and changing these terms will undoubtedly result in a handful of cancellations on the part of those who have placed an order for this truck. They could pay $99 per month for an FSD subscription, which is now the only option available, but having purchased the suite outright on another vehicle and being told the transfer policy would be upheld, only to have it cancelled, is a tough pill to swallow.

These moves were also made by Tesla just before deliveries were set to begin on the Cybertruck AWD configuration. Reservation holders have started receiving VINs for their trucks, and Tesla is preparing to hand over the first units.

It’s a disappointing move from Tesla that will undoubtedly make some of its fans who have bought the truck frustrated.

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Tesla tipped its hand at where Robotaxi is heading next

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Tesla Cybercab production units rolling off the factory line in Gigafactory Texas (Credit: Tesla)
Tesla Cybercab production units rolling off the factory line in Gigafactory Texas (Credit: Tesla)

In the world of autonomous ride-hailing, there are only a handful of names. Among those few companies lies a strategy play by each to keep the opposition on their toes. Tesla, on the other hand, already tipped its hand at where it is headed next.

Tesla has signaled its next major push in the autonomous ride-hailing market by filing for an Autonomous Vehicle Network Company permit in Nevada (Docket 26-05015). Through Tesla Robotaxi, LLC, the company seeks approval to operate up to 5,000 robotaxis in Clark County, including high-traffic areas like Las Vegas and Henderson airports, within the first 12 months of launch.

This filing builds on Tesla’s earlier testing approvals from the Nevada DMV in September 2025 and preparations such as maintenance hubs in the Las Vegas area. Nevada represents a strategic expansion into a major tourist destination, where high visitor volumes could drive strong utilization and showcase the reliability of unsupervised autonomy to a broad audience.

Approval would mark a significant step toward commercial operations in a new state, following progress in Texas.

Tesla’s shareholder decks and earnings calls have clearly outlined these ambitions. In the Q4 2025 shareholder deck, the company listed planned Robotaxi coverage for the first half of 2026, explicitly naming Las Vegas alongside Phoenix, Miami, Orlando, and Tampa, with Dallas and Houston already advancing. Austin was noted as “ramping unsupervised,” while the Bay Area remained in safety-driver mode.

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By Q1 2026, the deck updated statuses to reflect launches in Dallas and Houston, with “preparations underway” for the remaining cities, including Las Vegas. Paid Robotaxi miles nearly doubled sequentially in Q1, underscoring momentum even as broader timelines adjusted slightly for regulatory and operational readiness.

On earnings calls, CEO Elon Musk and executives have emphasized a phased rollout prioritizing safety. Unsupervised operations in Texas have shown strong results with no reported accidents or injuries in the program. Tesla continues groundwork in additional major U.S. metros through testing and permitting, positioning it to scale quickly once approvals clear.

This Nevada move aligns with Tesla’s vision of transforming from an EV maker into an AI and robotics leader. The forthcoming Cybercab, which started production at Giga Texas in April, is expected to eventually dominate the fleet, replacing many Model Y vehicles and driving down costs to enable affordable rides.

For investors and the industry, this signals Tesla’s intent to dominate key Sun Belt and tourist markets where weather, regulations, and demand favor rapid scaling. Success in Las Vegas could validate the model for denser urban and high-tourism environments, accelerating the shift toward a future where robotaxis generate meaningful revenue.

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Las Vegas will also expand knowledge among the general public at Tesla’s capabilities, helping people experience driverless ride-hailing from several companies during their time on The Strip.

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