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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 the global race toward electrification, Norway stands unchallenged as the world’s most mature EV market.
In the first quarter of this year, EVs captured a staggering 97.9 percent market share, with plugin EVs reaching 98.6 percent. Out of 27,175 new vehicles registered, non-BEV powertrains have been reduced to statistical noise—petrol and hybrids combined accounted for fewer than 80 units.
At the heart of this transformation is Tesla.
The Model Y dominated overall vehicle sales with 5,406 units, outselling the next five best-selling non-Tesla models combined. The refreshed Model 3 followed in second place with 2,010 units, giving Tesla a commanding one-two finish. Toyota’s bZ4X placed third with 1,400 units, while Volvo’s EX40 and others trailed further back.
The @Tesla Model Y was the #1 best-selling vehicle overall in Norway in Q1 2026 by a wide margin, with BEVs in general taking a 97.9% market share. Model 3 ranked #2.
Model Y (5,406 units) sold more units than the next five best-selling non-Tesla vehicles on the list. pic.twitter.com/LE2SD5UQjs
— Sawyer Merritt (@SawyerMerritt) May 5, 2026
This dominance is no fluke. Norway has spent decades building the infrastructure and policy framework that makes EVs the rational choice. Generous tax incentives, exemption from VAT, reduced tolls, free ferries for EVs, and a dense charging network have turned the country into a living laboratory for mass adoption. High fuel prices—often exceeding $8 per gallon—further tilt the economics decisively toward electricity.
The result is a market where choosing anything but an EV feels increasingly anachronistic. Diesel and petrol cars have all but vanished from new registrations. Even plug-in hybrids, once a transitional favorite, have collapsed to 0.7 percent share.
Chinese brands like XPeng, BYD, and Zeekr are making inroads, while legacy European and Japanese automakers scramble to field competitive BEVs. Yet Tesla’s combination of range, performance, software, Supercharger network, and brand cachet continues to set the benchmark.
Norway’s Q1 figures come after a volatile start to 2026 caused by VAT changes that pulled forward sales into late 2025. The market rebounded strongly in March, underscoring underlying demand. Tesla’s Q1 performance in the country also jumped significantly year-over-year, reinforcing its position even as competition intensifies.
What happens in Norway rarely stays there. The country has long served as a bellwether for EV trends across Europe and beyond.
Its near-total transition demonstrates that when incentives align with infrastructure and consumer economics, adoption accelerates dramatically. For automakers, Norway signals a future where success hinges not on legacy powertrains but on delivering compelling electric vehicles at scale.
As other nations ramp up their own EV ambitions, Tesla’s continued reign in the world’s heaviest EV market sends a clear message: in a fully mature electric future, the company that started the revolution remains the one to beat. With the Model Y still the best-selling vehicle overall—quarter after quarter—Norway’s roads are a rolling testament to Tesla’s enduring leadership.
Tesla has taken home the “Overall Loyalty to Make” award from S&P Global Mobility for the fourth consecutive year, reinforcing Tesla owners’ willingness to come back. The 2025 awards are based on S&P Global Mobility’s analysis of 13.6 million new retail vehicle registrations in the U.S. from October 2024 through September 2025. The complete list of 2025 winners includes General Motors for Overall Loyalty to Manufacturer, Tesla for Overall Loyalty to Make, Chevrolet Equinox for Overall Loyalty to Model, Mini for Most Improved Make Loyalty, Subaru for Overall Loyalty to Dealer, and Tesla again for both Ethnic Market Loyalty to Make and Highest Conquest Percentage.
Tesla’s streak in this category started in 2022, and the brand has now won the Highest Conquest Percentage award for six straight years, meaning it keeps pulling buyers away from other brands at a rate no competitor has matched. Tesla’s retention among Asian households reached 63.6% and among Hispanic households 61.9%, rates that significantly outpace national averages for those groups. That breadth of appeal across demographics adds a layer of significance to a win that some might dismiss as routine.
The timing matters too. After several consecutive quarters of decline, Tesla’s share of U.S. EV sales jumped to 59% in Q4 2025. That rebound, arriving just as competitors were flooding the market with new models and incentives, suggests Tesla’s loyalty numbers are not simply the result of limited alternatives. Buyers are still choosing it when they have plenty of other options.
What keeps Tesla owners coming back has a lot to do with the and convenience of charging. The Supercharger network is the most straightforward example. With over 65,000 Superchargers globally, it remains the largest and most reliable fast-charging network in the world, and owners who have built their routines around it face a real practical cost when considering a switch. Competitors have made progress, but the consistency, speed, and availability of Tesla’s network is still the benchmark the rest of the industry is chasing. Then there is the software side. Tesla has built a model where the car you own today is functionally different from the car you bought two years ago, through over-the-air updates that add continuous game-changing improvements such as Full Self-Driving that has moved from a driver-assist feature to an increasingly capable autonomous system. For many Tesla owners, leaving the brand means starting over with a car that will not get meaningfully better over time, and that is a trade-off fewer and fewer are willing to make.
Tesla Robotaxi services in Austin have been operating since last Summer, but Tesla has admittedly been delayed in its expansion of the geofence, fleet size, and other details in a bid to prioritize safety as new technology rolls out.
But those barriers are being broken with new guardrails being removed from the program.
Tesla has achieved a significant advancement in its autonomous ride-hailing program. As of May 4, the Robotaxi fleet in Austin, Texas, has begun operating unsupervised during evening hours for the first time. This expansion moves beyond previous limitations that restricted unsupervised service to daylight hours, typically ending in mid-afternoon.
Tesla Robotaxi in Austin is operating unsupervised in the evenings for the first time today.
Previously in Austin, unsupervised operation ended mid-afternoon
— Robotaxi Tracker (@RtaxiTracker) May 4, 2026
The change brings Austin in line with operations in Dallas and Houston. Those cities have supported evening unsupervised runs since their initial launches in April, and both recently received additions of new unsupervised vehicles to their fleets. This coordinated progress across Texas strengthens Tesla’s regional presence and provides a broader testing ground for the technology.
This milestone carries substantial weight in the development of autonomous vehicles. Extending operations into low-light conditions meaningfully expands the Robotaxi’s operational design domain (ODD)—the specific environments and scenarios in which the system is approved to operate safely without human intervention.
Nighttime driving presents unique technical demands: diminished visibility, headlight glare from oncoming traffic, reduced contrast for identifying pedestrians and lane markings, and greater variability in camera sensor exposure.
Tesla’s pure vision approach, powered by neural networks trained on vast real-world datasets rather than lidar or pre-mapped routes, must handle these variables reliably. Demonstrating consistent unsupervised performance after sunset validates the robustness of the end-to-end AI stack and its ability to generalize across diverse lighting conditions.
Beyond technical validation, the expansion holds important operational and economic implications. Evening hours often coincide with peak urban demand for rides, including commutes, dining, and entertainment outings.
Enabling service during these periods increases daily vehicle utilization, allowing each Robotaxi to generate more revenue while gathering additional high-value training data. Higher utilization accelerates the virtuous cycle of data collection, model improvement, and further ODD growth.
Looking ahead, this step paves the way for more ambitious rollouts. Success in low-light environments positions Tesla to pursue near-24-hour operations, potentially integrating highways and expanding into varied weather patterns. Regulators worldwide frequently demand evidence of safe performance across day-night cycles before granting wider approvals.
Proven capability in Texas could expedite deployments in planned cities such as Phoenix, Miami, Orlando, Tampa, and Las Vegas during the first half of 2026.
Tesla confirms Robotaxi expansion plans with new cities and aggressive timeline
Moreover, scaling evening service supports Tesla’s long-term vision of a high-efficiency robotaxi network. Greater fleet productivity lowers the cost per mile, making autonomous mobility more accessible and competitive against traditional ride-hailing.
As the company iterates on software updates informed by nighttime data, reliability is expected to compound rapidly, unlocking denser urban coverage and longer-distance trips.
In summary, the introduction of an unsupervised evening Robotaxi service in Austin represents more than an incremental schedule adjustment. It signals a critical maturation of the underlying technology and sets the foundation for broader geographic and temporal expansion.
With Texas operations gaining momentum, Tesla is steadily advancing toward transforming urban transportation at scale.
Tesla reigns supreme in the heaviest EV market on Earth
Tesla owners keep coming back for more
