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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.
News
Tesla confirms that it finally solved its 4680 battery’s dry cathode process
The suggests the company has finally resolved one of the most challenging aspects of its next-generation battery cells.
Tesla has confirmed that it is now producing both the anode and cathode of its 4680 battery cells using a dry-electrode process, marking a key breakthrough in a technology the company has been working to industrialize for years.
The update, disclosed in Tesla’s Q4 and FY 2025 update letter, suggests the company has finally resolved one of the most challenging aspects of its next-generation battery cells.
Dry cathode 4680 cells
In its Q4 and FY 2025 update letter, Tesla stated that it is now producing 4680 cells whose anode and cathode were produced during the dry electrode process. The confirmation addresses long-standing questions around whether Tesla could bring its dry cathode process into sustained production.
The disclosure was highlighted on X by Bonne Eggleston, Tesla’s Vice President of 4680 batteries, who wrote that “both electrodes use our dry process.”
Tesla first introduced the dry-electrode concept during its Battery Day presentation in 2020, pitching it as a way to simplify production, reduce factory footprint, lower costs, and improve energy density. While Tesla has been producing 4680 cells for some time, the company had previously relied on more conventional approaches for parts of the process, leading to questions about whether a full dry-electrode process could even be achieved.
4680 packs for Model Y
Tesla also revealed in its Q4 and FY 2025 Update Letter that it has begun producing battery packs for certain Model Y vehicles using its in-house 4680 cells. As per Tesla:
“We have begun to produce battery packs for certain Model Ys with our 4680 cells, unlocking an additional vector of supply to help navigate increasingly complex supply chain challenges caused by trade barriers and tariff risks.”
The timing is notable. With Tesla preparing to wind down Model S and Model X production, the Model Y and Model 3 are expected to account for an even larger share of the company’s vehicle output. Ensuring that the Model Y can be equipped with domestically produced 4680 battery packs gives Tesla greater flexibility to maintain production volumes in the United States, even as global battery supply chains face increasing complexity.
Elon Musk
Tesla Giga Texas to feature massive Optimus V4 production line
This suggests that while the first Optimus line will be set up in the Fremont Factory, the real ramp of Optimus’ production will happen in Giga Texas.
Tesla will build Optimus 4 in Giga Texas, and its production line will be massive. This was, at least, as per recent comments by CEO Elon Musk on social media platform X.
Optimus 4 production
In response to a post on X which expressed surprise that Optimus will be produced in California, Musk stated that “Optimus 4 will be built in Texas at much higher volume.” This suggests that while the first Optimus line will be set up in the Fremont Factory, and while the line itself will be capable of producing 1 million humanoid robots per year, the real ramp of Optimus’ production will happen in Giga Texas.
This was not the first time that Elon Musk shared his plans for Optimus’ production at Gigafactory Texas. During the 2025 Annual Shareholder Meeting, he stated that Giga Texas’ Optimus line will produce 10 million units of the humanoid robot per year. He did not, however, state at the time that Giga Texas would produce Optimus V4.
“So we’re going to launch on the fastest production ramp of any product of any large complex manufactured product ever, starting with building a one-million-unit production line in Fremont. And that’s Line one. And then a ten million unit per year production line here,” Musk stated.
How big Optimus could become
During Tesla’s Q4 and FY 2025 earnings call, Musk offered additional context on the potential of Optimus. While he stated that the ramp of Optimus’ production will be deliberate at first, the humanoid robot itself will have the potential to change the world.
“Optimus really will be a general-purpose robot that can learn by observing human behavior. You can demonstrate a task or verbally describe a task or show it a task. Even show it a video, it will be able to do that task. It’s going to be a very capable robot. I think long-term Optimus will have a very significant impact on the US GDP.
“It will actually move the needle on US GDP significantly. In conclusion, there are still many who doubt our ambitions for creating amazing abundance. We are confident it can be done, and we are making the right moves technologically to ensure that it does. Tesla, Inc. has never been a company to shy away from solving the hardest problems,” Musk stated.
Elon Musk
Rumored SpaceX-xAI merger gets apparent confirmation from Elon Musk
The comment follows reports that the rocket maker is weighing a transaction that could further consolidate Musk’s space and AI ventures.
Elon Musk appeared to confirm reports that SpaceX is exploring a potential merger with artificial intelligence startup xAI by responding positively to a post about the reported transaction on X.
Musk’s comment follows reports that the rocket maker is weighing a transaction that could further consolidate his space and AI ventures.
SpaceX xAI merger
As per a recent Reuters report, SpaceX has held discussions about merging with xAI, with the proposed structure potentially involving an exchange of xAI shares for SpaceX stock. The value, structure, and timing of any deal have not been finalized, and no agreement has been signed.
Musk appeared to acknowledge the report in a brief reply on X, responding “Yeah” to a post that described SpaceX as a future “Dyson Swarm company.” The comment references a Dyson Swarm, a sci-fi megastructure concept that consists of a massive network of satellites or structures that orbit a celestial body to harness its energy.
Reuters noted that two entities were formed in Nevada on January 21 to facilitate a potential transaction for the possible SpaceX-xAI merger. The discussions remain ongoing, and a transaction is not yet guaranteed, however.
AI and space infrastructure
A potential merger with xAI would align with Musk’s stated strategy of integrating artificial intelligence development with space-based systems. Musk has previously said that space-based infrastructure could support large-scale computing by leveraging continuous solar energy, an approach he has framed as economically scalable over time.
xAI already has operational ties to Musk’s other companies. The startup develops Grok, a large language model that holds a U.S. Department of Defense contract valued at up to $200 million. AI also plays a central role in SpaceX’s Starlink and Starshield satellite programs, which rely on automation and machine learning for network management and national security applications.
Musk has previously consolidated his businesses through share-based transactions, including Tesla’s acquisition of SolarCity in 2016 and xAI’s acquisition of X last year. Bloomberg has also claimed that Musk is considering a merger between SpaceX and Tesla in the future.
Tesla confirms that it finally solved its 4680 battery’s dry cathode process
Tesla Giga Texas to feature massive Optimus V4 production line
