News
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.
News
Elon Musk’s Grokipedia surges to 5.6M articles, almost 79% of English Wikipedia
The explosive growth marks a major milestone for the AI-powered online encyclopedia, which was launched by Elon Musk’s xAI just months ago.
Elon Musk’s Grokipedia has grown to an impressive 5,615,201 articles as of today, closing in on 79% of the English Wikipedia’s current total of 7,119,376 articles.
The explosive growth marks a major milestone for the AI-powered online encyclopedia, which was launched by Elon Musk’s xAI just months ago. Needless to say, it would only be a matter of time before Grokipedia exceeds English Wikipedia in sheer volume.
Grokipedia’s rapid growth
xAI’s vision for Grokipedia emphasizes neutrality, while Grok’s reasoning capabilities allow for fast drafting and fact-checking. When Elon Musk announced the initiative in late September 2025, he noted that Grokipedia would be an improvement to Wikipedia because it would be designed to avoid bias.
At the time, Musk noted that Grokipedia “is a necessary step towards the xAI goal of understanding the Universe.”
Grokipedia was launched in late October, and while xAI was careful to list it only as Version 0.1 at the time, the online encyclopedia immediately earned praise. Wikipedia co-founder Larry Sanger highlighted the project’s innovative approach, noting how it leverages AI to fill knowledge gaps and enable rapid updates. Netizens also observed how Grokipedia tends to present articles in a more objective manner compared to Wikipedia, which is edited by humans.
Elon Musk’s ambitious plans
With 5,615,201 total articles, Grokipedia has now grown to almost 79% of English Wikipedia’s article base. This is incredibly quick, though Grokipedia remains text-only for now. xAI, for its part, has now updated the online encyclopedia’s iteration to v0.2.
Elon Musk has shared bold ideas for Grokipedia, including sending a record of the entire knowledge base to space as part of xAI’s mission to preserve and expand human understanding. At some point, Musk stated that Grokipedia will be renamed to Encyclopedia Galactica, and it will be sent to the cosmos.
“When Grokipedia is good enough (long way to go), we will change the name to Encyclopedia Galactica. It will be an open source distillation of all knowledge, including audio, images and video. Join xAI to help build the sci-fi version of the Library of Alexandria!” Musk wrote, adding in a later post that “Copies will be etched in stone and sent to the Moon, Mars and beyond. This time, it will not be lost.”
News
Tesla Model 3 becomes Netherlands’ best-selling used EV in 2025
More than one in ten second-hand electric cars sold in the country last year was a Tesla Model 3.
The Tesla Model 3 became the most popular used electric car in the Netherlands in 2025, cementing its dominance well beyond the country’s new-car market.
After years at the top of Dutch EV sales charts, the Model 3 now leads the country’s second-hand EV market by a wide margin, as record used-car purchases pushed electric vehicles further into the mainstream.
Model 3 takes a commanding lead
The Netherlands recorded more than 2.1 million used car sales last year, the highest level on record. Of those, roughly 4.8%, or about 102,000 vehicles, were electric. Within that growing segment, the Tesla Model 3 stood far ahead of its competitors.
In 2025 alone, 11,338 used Model 3s changed hands, giving the car an 11.1% share of the country’s entire used EV market. That means more than one in ten second-hand electric cars sold in the country last year was a Tesla Model 3, Auto Week Netherlands reported. The scale of its lead is striking: the gap between the Model 3 and the second-place finisher, the Volkswagen ID3, is more than 6,700 vehicles.
Rivals trail as residual values shape rankings
The Volkswagen ID.3 ranked a distant second, with 4,595 used units sold and a 4.5% market share. Close behind was the Audi e-tron, which placed third with 4,236 registrations. As noted by Auto Week Netherlands, relatively low residual values likely boosted the e-tron’s appeal in the used market, despite its higher original price.
Other strong performers included the Kia Niro, the Tesla Model Y, and the Hyundai Kona, highlighting continued demand for compact and midsize electric vehicles with proven range and reliability. No other model, however, came close to matching the Model 3’s scale or market presence.
News
Tesla Model Y Standard Long Range RWD launches in Europe
The update was announced by Tesla Europe & Middle East in a post on its official social media account on X.
Tesla has expanded the Model Y lineup in Europe with the introduction of the Standard Long Range RWD variant, which offers an impressive 657 km of WLTP range.
The update was announced by Tesla Europe & Middle East in a post on its official social media account on X.
Model Y Standard Long Range RWD Details
Tesla Europe & Middle East highlighted some of the Model Y Standard Long Range RWD’s most notable specs, from its 657 km of WLTP range to its 2,118 liters of cargo volume. More importantly, Tesla also noted that the newly released variant only consumes 12.7 kWh per 100 km, making it the most efficient Model Y to date.
The Model Y Standard provides a lower entry point for consumers who wish to enter the Tesla ecosystem at the lowest possible price. While the Model 3 Standard is still more affordable, some consumers might prefer the Model Y Standard due to its larger size and crossover form factor. The fact that the Model Y Standard is equipped with Tesla’s AI4 computer also makes it ready for FSD’s eventual rollout to the region.
Top Gear’s Model Y Standard review
Top Gear‘s recent review of the Tesla Model Y Standard highlighted some of the vehicle’s most notable features, such as its impressive real-world range, stellar infotainment system, and spacious interior. As per the publication, the Model Y Standard still retains a lot of what makes Tesla’s vehicles well-rounded, even if it’s been equipped with a simplified interior.
Top Gear compared the Model Y Standard to its rivals in the same segment. “The introduction of the Standard trim brings the Model Y in line with the entry price of most of its closest competition. In fact, it’s actually cheaper than a Peugeot e-3008 and costs £5k less than an entry-level Audi Q4 e-tron. It also makes the Ford Mustang Mach-E look a little short with its higher entry price and worse range,” the publication wrote.
Elon Musk’s Grokipedia surges to 5.6M articles, almost 79% of English Wikipedia
Tesla Model 3 becomes Netherlands’ best-selling used EV in 2025
