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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 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.
We’d have to assume this means Tesla is targeting Las Vegas, and it’s a great move from a business perspective.
Vegas is such a melting pot of people from all around the country and the world. It will expose people from all corners of the globe to Tesla’s autonomy capabilities https://t.co/Qz3fQmhULF pic.twitter.com/Du5pj2RyWC
— TESLARATI (@Teslarati) June 6, 2026
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.
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.
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.
Investor's Corner
Tesla just did something in South Korea that no foreign carmaker has ever done
Tesla’s Model Y just became South Korea’s best-selling car, beating every domestic model in May.
Tesla did something last month that no foreign car has ever done in South Korea by outselling every vehicle in the country, domestic or imported, finishing the month with Model Y as the single best-selling car across the entire Korean market. According to data from the Korea Automobile Importers and Distributors Association released on June 4, the Model Y recorded 8,762 units sold in May, pushing the Kia Sorento into second place at 7,836 units and the Hyundai Grandeur into third at 5,183 units. It is the first time an imported vehicle has outsold every domestic model on a single-month basis.
Tesla imported 10,866 cars into South Korea in May, making it the top import brand for the fourth consecutive month. BMW followed at 6,555 units, less than two-thirds of Tesla’s total, while BYD registered just 1,032 units. The combined domestic sales of GM Korea, Renault Korea, and KG Mobility last month totaled just 7,019 units, meaning a single Tesla model outsold three Korean automakers combined.
Tesla FSD earns high praise in South Korea’s real-world autonomous driving test
South Korea has historically been one of the hardest markets for foreign automakers to crack. Hyundai and Kia together control close to 70% of the overall market and carry deep consumer loyalty built over decades. Tesla’s path into this market was an uphill battle due to high import duties, limited service infrastructure, and early skepticism about charging networks. In 2024, the Model Y was the best-selling imported car in South Korea with 18,717 units for the full year. By 2025, after the Juniper refresh, it cleared 50,000 units and took the top spot among all EVs.
Year to date, Tesla has a 250.8% increase in the country over the same period last year, and now holds a 30.8% share of the entire imported car segment for 2026. EVs as a category represented 48.6% of all imported passenger car registrations in May. As Teslarati has reported, the Juniper refresh brought meaningful improvements to range, interior quality, and ride refinement that addressed the most common criticisms of earlier Model Y versions. Those upgrades appear to be resonating in markets like South Korea where buyers compare Tesla directly against high end domestic competitors.
The Tesla Model 3’s cheapest trim level just got a major accolade, as Edmunds just revealed the Rear-Wheel-Drive trim of the all-electric sedan is the most efficient EV that is currently in production.
The 2026 Tesla Model 3 Rear-Wheel-Drive not only beat its EPA-estimated range by 30 miles, but it also bested its efficiency mark by 13.2 percent. The Model 3 tested by Edmunds traveled 393 miles, beating its EPA rating by 8.3 percent, while it returned 21.7 kWh per 100 miles, or 4.61 mi/kWh.
Beating those two metrics is especially pertinent when it comes to EV ownership and driving down the cost of ownership from ICE counterparts across the board. The real money savings come from driving down the cost of driving per mile, especially when it comes to high-mileage driving.
Edmunds stated in its report and review that the process it uses to test EV efficiency is aimed at giving “the most accurate representation of a car’s real-world range.” The assessment uses a strict route that features 60 percent city and 40 percent highway driving, and an average speed of 40 MPH across the trip.
It also drives each car within 5 MPH of all posted speed limits, and the climate control is set on Auto at 72 degrees to ensure even testing. In other words, Edmunds does not use methods to maximize efficiency, and instead tries to make it reasonable to achieve the same ratings yourself.
In comparison to other EVs, it beat the 2026 Mercedes-Benz CLA 350, which went 385 miles, as well as the 2026 Audi A6 Sportback E-tron Prestige AWD, which traveled 392 miles. Only the Mercedes-Benz CLA 250+ traveled farther, making it an impressive 434 miles on a charge.
However, the Tesla Model 3 RWD’s efficiency is “unmatched” because of its incredibly low energy usage per mile.
🚨 Tesla Model 3 RWD:
-At $36,990, it is $9,000 cheaper than the average transaction price for a new car ($46,023 via KBB)
-Was 13.2% more efficient than its EPA estimate
-Traveled 393 miles on a charge despite its 363-mile EPA range https://t.co/Grov2hXqpa pic.twitter.com/Zl8rnZZLIB
— TESLARATI (@Teslarati) June 8, 2026
The Model 3 Rear-Wheel-Drive might be the best bang-for-your-buck EV if you’re looking to buy new and want access to features like Full Self-Driving, while also being aware of efficiency. This trim of the Model 3 is also priced over $9,000 cheaper than what Kelley Blue Book says the average transactional price for a new car was in May 2026, which sits at $46,023.
If you’re looking for something with more speed, an All-Wheel-Drive drivetrain, or more premium features, the Premium trims of the Model 3 currently come with one year of Free Supercharging.
Tesla tipped its hand at where Robotaxi is heading next
Tesla just did something in South Korea that no foreign carmaker has ever done
