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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 expands Unsupervised Robotaxi service to two new cities
This expansion builds directly on Tesla’s existing operations. Robotaxi has been ramping unsupervised rides in Austin for months and maintains activity in the San Francisco Bay Area.
Tesla has taken a major step forward in its autonomous ride-hailing ambitions.
On April 18, the company’s official Robotaxi account announced that Robotaxi service is now rolling out in Dallas and Houston, Texas. The update signals the rapid scaling of unsupervised autonomous operations in the Lone Star State.
The announcement includes a compelling 14-second video captured from inside a Model Y. Shot from the passenger perspective, the footage shows the vehicle navigating suburban roads in both cities with zero driver intervention, with no Safety Monitor to be seen.
Robotaxi now rolling out in Dallas & Houston 🤠 pic.twitter.com/G3KFQwqGxB
— Tesla Robotaxi (@robotaxi) April 18, 2026
Tesla also shared geofence maps highlighting the initial service areas: a compact zone in Houston covering parts of Willowbrook and Jersey Village, and a similarly defined area in Dallas near Highland Park and central neighborhoods.
🚨 Tesla has expanded Robotaxi to two new cities: Houston and Dallas, joining Austin and the SF Bay Area as active Robotaxi areas https://t.co/S3Ck4EaGpR pic.twitter.com/N0qu0bcTyd
— TESLARATI (@Teslarati) April 18, 2026
This expansion builds directly on Tesla’s existing operations. Robotaxi has been ramping unsupervised rides in Austin for months and maintains activity in the San Francisco Bay Area.
With Dallas and Houston now live, Texas hosts three active hubs—an impressive concentration that triples the company’s Lone Star footprint in just weeks. The move aligns with Tesla’s Q4 2025 earnings guidance, which outlined a broader H1 2026 rollout across seven U.S. cities, including Phoenix, Miami, Orlando, Tampa, and Las Vegas.
Texas offers favorable regulations, high ride-share demand, and relatively straightforward suburban-to-urban driving patterns ideal for early autonomous scaling. While initial geofences appear modest—roughly 25 square miles per city—Tesla has historically expanded these zones quickly as it gathers real-world data.
Tesla confirms Robotaxi expansion plans with new cities and aggressive timeline
Unsupervised operation marks a critical milestone: passengers can summon, ride, and exit without safety drivers, a leap beyond many competitors still requiring human oversight.
For Tesla, the implications are significant. Successful scaling in major metros could accelerate the transition to a fully driverless fleet, unlocking new revenue streams and validating years of Full Self-Driving investment.
Riders gain convenient, potentially lower-cost mobility, while the company edges closer to Elon Musk’s vision of Robotaxis transforming urban transport.
As Tesla pushes into more cities this year, today’s launch in Dallas and Houston underscores its momentum. Hopefully, Tesla will be able to expand unsupervised rides to another U.S. state soon, which will mark yet another chapter in this short-but-encouraging Robotaxi story.
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Tesla is pushing Robotaxi features to owner cars with Spring Update
Tesla has quietly begun rolling out one of its most forward-looking Robotaxi-inspired features to existing customer vehicles.
Tesla is starting to push Robotaxi features to owner cars, and the first instances are coming as the Spring 2026 Update starts to roll out.
Tesla has quietly begun rolling out one of its most forward-looking Robotaxi-inspired features to existing customer vehicles.
With the 2026 Spring Update (version 2026.14+), the rear passenger display now features a fully interactive navigation map that works while the car is driving — a capability previously reserved for Tesla Robotaxi.
First look at Tesla’s v2026.14.1 Spring Update.
🧭Rear screen interactive map #teslaupdate #tesla #teslasrpingupdate pic.twitter.com/yH3T4U8qHp— Sergiu Mogan (@sergiumogan) April 17, 2026
Until now, Tesla’s rear displays have been largely limited to media controls, climate settings, and static route overviews. The new interactive map transforms the backseat into an active navigation hub, exactly the kind of passenger-first interface Tesla has been prototyping for its driverless fleet.
In a Robotaxi, where no one sits behind the wheel, every rider will need intuitive, real-time map access. By shipping this UI into thousands of owner cars months ahead of the Cybercab’s planned unveiling, Tesla is stress-testing the software in real-world conditions and giving loyal customers an early taste of the autonomous future.
The rollout is still in its early wave. Only a small number of vehicles have received 2026.14.1 so far, but the feature is expected to expand rapidly in the coming weeks. Owners of Model S, Model X, Model 3, Model Y, and Cybertruck are all eligible.
For buyers of the new Signature Edition Model S and X Plaid vehicles — whose deliveries begin in May — the update will likely arrive shortly after they take delivery, meaning the final chapter of Tesla’s flagship lineup will ship with cutting-edge Robotaxi preview tech baked in.
Elon Musk has long emphasized that Tesla ships supporting infrastructure well before new products launch. This rear-map rollout is a textbook example of that philosophy — quietly preparing both the software and the customer base for a world of fully driverless rides.
While the interactive map may seem like a modest convenience upgrade on the surface, its deeper purpose is unmistakable. Tesla is using its massive installed base of vehicles as a proving ground for the exact passenger experience that will define the Robotaxi era.
For current owners, it’s a free preview of tomorrow’s mobility; for the company, it’s invaluable data and real-world validation before the Cybercab hits the streets.
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Tesla Cybertruck sales bolstered by bold Musk move, report claims
If accurate, that means nearly one in every five Cybertrucks registered in the quarter was transferred internally within Musk’s business empire. The purchases, valued at more than $100 million, have continued into 2026.
A new report from Bloomberg claims Tesla Cybertruck sales were inflated by internal buyers, meaning companies owned by CEO Elon Musk, and most notably, SpaceX.
According to a new registration data analysis, a significant portion of the fourth quarter’s Cybertruck sales came from Musk companies.
In the fourth quarter of 2025, 7,071 Cybertrucks were registered in the United States. SpaceX, Musk’s rocket and satellite company, accounted for 1,279 of those vehicles—more than 18 percent of the total. Musk’s additional ventures, including xAI, the Boring Company, and Neuralink, acquired another 60 trucks during the same period.
Tesla Cybertruck just won a rare and elusive crash safety honor
If accurate, that means nearly one in every five Cybertrucks registered in the quarter was transferred internally within Musk’s business empire. The purchases, valued at more than $100 million, have continued into 2026.
These internal sales supplemented the Cybertruck’s overall performance for the quarter, as without them, sales would have plunged 51 percent. The vehicle, which has repeatedly been called “the best product Tesla has ever made,” has fallen short of expectations due to pricing.
When first unveiled back in 2019, Tesla had a $39,990, $49,990, and $69,990 configuration for sale. Those prices inflated significantly as the truck was not released to customers until 2023. Those who had placed orders for affordable configurations were priced out.
Sam Fiorani, VP of Global Vehicle Forecasting at AutoForecast Solutions, said, “Tesla is running out of buyers for the Cybertruck.” In reality, there are probably a lot of buyers, but they simply cannot afford the truck at its current price point.
The Cybertruck was supposed to broaden Tesla’s appeal beyond its core lineup of sleek sedans and SUVs. While it has done a lot for brand notoriety, it has not lived up to its monumental expectations, and it’s simply because the truck has not been as available as most had thought.
The truck is still the best-selling electric pickup in the country, outpacing rivals like the Ford F-150 Lightning and Chevrolet Silverado EV. It is also not uncommon for companies to use their own vehicles for internal operations, like Ford using its own Transit van for Mobile Service.
However, this much inventory of Cybertrucks being purchased by Musk’s companies is not what you love to see as a fan or investor.
Tesla expands Unsupervised Robotaxi service to two new cities
Tesla is pushing Robotaxi features to owner cars with Spring Update
