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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.
Tesla has rolled out a new Supercharging safety feature in the United States, one that will answer concerns that some owners may have if they need to leave in a pinch.
It is also a suitable alternative for non-Tesla chargers, like third-party options that feature J1772 or CCS to NACS adapters.
The feature has been available in Europe for some time, but it is now rolling out to Model 3 and Model Y owners in the U.S.
With Software Update 2026.2.3, Tesla is launching the Unlatching Charge Cable function, which will now utilize the left rear door handle to release the charging cable from the port. The release notes state:
“Charging can now be stopped and the charge cable released by pulling and holding the rear left door handle for three seconds, provided the vehicle is unlocked, and a recognized key is nearby. This is especially useful when the charge cable doesn’t have an unlatch button. You can still release the cable using the vehicle touchscreen or the Tesla app.”
The feature was first spotted by Not a Tesla App.
This is an especially nice feature for those who commonly charge at third-party locations that utilize plugs that are not NACS, which is the Tesla standard.
For example, after plugging into a J1772 charger, you will still be required to unlock the port through the touchscreen, which is a minor inconvenience, but an inconvenience nonetheless.
Additionally, it could be viewed as a safety feature, especially if you’re in need of unlocking the charger from your car in a pinch. Simply holding open the handle on the rear driver’s door will now unhatch the port from the car, allowing you to pull it out and place it back in its housing.
This feature is currently only available on the Model 3 and Model Y, so Model S, Model X, and Cybertruck owners will have to wait for a different solution to this particular feature.
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LG Energy Solution pursuing battery deal for Tesla Optimus, other humanoid robots: report
Optimus is expected to be one of Tesla’s most ambitious projects, with Elon Musk estimating that the humanoid robot could be the company’s most important product.
A recent report has suggested that LG Energy Solution is in discussions to supply batteries for Tesla’s Optimus humanoid robot.
Optimus is expected to be one of Tesla’s most ambitious projects, with Elon Musk estimating that the humanoid robot could be the company’s most important product.
Humanoid robot battery deals
LG Energy Solution shares jumped more than 11% on the 28th after a report from the Korea Economic Daily claimed that the company is pursuing battery supply and joint development agreements with several humanoid robot makers. These reportedly include Tesla, which is developing Optimus, as well as multiple Chinese robotics companies.
China is already home to several leading battery manufacturers, such as CATL and BYD, making the robot makers’ reported interest in LG Energy Solution quite interesting. Market participants interpreted the reported outreach as a signal that performance requirements for humanoid robots may favor battery chemistries developed by companies like LG.
LF Energy Solution vs rivals
According to the report, energy density is believed to be the primary reason humanoid robot developers are evaluating LG Energy Solution’s batteries. Unlike electric vehicles, humanoid robots have significantly less space available for battery packs while requiring substantial power to operate dozens of joint motors and onboard artificial intelligence processors.
LG Energy Solution’s ternary lithium batteries offer higher energy density compared with rivals’ lithium iron phosphate (LFP) batteries, which are widely used by Chinese EV manufacturers. That advantage could prove critical for humanoid robots, where runtime, weight, and compact packaging are key design constraints.
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Tesla receives approval for FSD Supervised tests in Sweden
Tesla confirmed that it has been granted permission to test FSD Supervised vehicles across Sweden in a press release.
Tesla has received regulatory approval to begin tests of its Full Self-Driving Supervised system on public roads in Sweden, a notable step in the company’s efforts to secure FSD approval for the wider European market.
FSD Supervised testing in Sweden
Tesla confirmed that it has been granted permission to test FSD Supervised vehicles across Sweden following cooperation with national authorities and local municipalities. The approval covers the Swedish Transport Administration’s entire road network, as well as urban and highways in the Municipality of Nacka.
Tesla shared some insights into its recent FSD approvals in a press release. “The approval shows that cooperation between authorities, municipalities and businesses enables technological leaps and Nacka Municipality is the first to become part of the transport system of the future. The fact that the driving of the future is also being tested on Swedish roads is an important step in the development towards autonomy in real everyday traffic,” the company noted.
With approval secured for FSD tests, Tesla can now evaluate the system’s performance in diverse environments, including dense urban areas and high-speed roadways across Sweden, as noted in a report from Allt Om Elbil. Tesla highlighted that the continued development of advanced driver assistance systems is expected to pave the way for improved traffic safety, increased accessibility, and lower emissions, particularly in populated city centers.
Tesla FSD Supervised Europe rollout
FSD Supervised is already available to drivers in several global markets, including Australia, Canada, China, Mexico, New Zealand, and the United States. The system is capable of handling city and highway driving tasks such as steering, acceleration, braking, and lane changes, though it still requires drivers to supervise the vehicle’s operations.
Tesla has stated that FSD Supervised has accumulated extensive driving data from its existing markets. In Europe, however, deployment remains subject to regulatory approval, with Tesla currently awaiting clearance from relevant authorities.
The company reiterated that it expects to start rolling out FSD Supervised to European customers in early 2026, pending approvals. It would then be unsurprising if the company secures approvals for FSD tests in other European territories in the coming months.
Tesla rolls out new Supercharging safety feature in the U.S.
LG Energy Solution pursuing battery deal for Tesla Optimus, other humanoid robots: report
