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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 VP explains latest updates in trade secret theft case
Tesla reportedly caught Matthews copying the tech into machines that were sold to competitors, claiming they lied about doing so for three years, and continued to ship it. That is when Tesla chose to sue Matthews in July 2024 in Federal court, demanding over $1 billion in damages due to trade secret theft.
Tesla Vice President Bonne Eggleston explained the latest updates in a trade secret theft case the company has against a former manufacturing equipment supplier, Matthews International.
Back in 2024, Tesla had filed a lawsuit against Matthews International, alleging that the firm stole trade secrets about battery manufacturing and shared those details with some of Tesla’s competitors.
Early last year, a U.S. District Court Judge denied Tesla’s request to block Matthews International from selling its dry battery electrode (DBE) technology across the world. The judge, Edward Davila, said that the patent for the tech was due to Matthews’ “extensive research and development.”
The two companies’ relationship began back in 2019, as Tesla hired Matthews to help build the equipment for its 4680 battery cell. Tesla shared confidential software, designs, and know-how under strict secrecy rules.
Fast forward a few years, and Tesla reportedly caught Matthews copying the tech into machines that were sold to competitors, claiming they lied about doing so for three years, and continued to ship it. That is when Tesla chose to sue Matthews in July 2024 in Federal court, demanding over $1 billion in damages due to trade secret theft.
Now, the latest twist, as this month, a Judge issued a permanent injunction—a court order banning Matthews from using certain stolen Tesla parts or designs in their machines. Matthews is also officially “liable” for damages. The exact amount would still to be calculated later.
Bonne Eggleston, a VP for Tesla, said on X today that Matthews is a supplier who “exploited customer IP through theft or deception,” and has no place in Tesla’s ecosystem:
Buyer beware: Matthews International stole Tesla’s DBE technology and is now subject to an injunction and liable for damages.
During our work with Matthews, we caught them red-handed copying our technology—including proprietary software and sensitive mechanical designs—into… https://t.co/Toc8ilakeM
— Bonne Eggleston (@BonneEggleston) March 10, 2026
Tesla calls this a big win and warns other companies: “Buyer beware—don’t buy from thieves.”
Matthews hit back with a press release claiming victory. They say an arbitrator ruled they can keep selling their own DBE equipment to anyone and rejected Tesla’s request for a total sales ban. They call Tesla’s claims “nonsense” and insist their 20-year-old tech is independent. Both sides are spinning the same narrow ruling: Matthews can sell their version, but they’re blocked from using Tesla’s specific secrets.
What are Tesla’s Current Legal Options
The case isn’t over—it’s moving to the damages phase. Tesla can:
- Push forward in court or arbitration to calculate and collect huge financial penalties (potentially $1 billion+ if willful theft is proven).
- Enforce the permanent injunction with contempt charges, fines, or even jail time if Matthews violates it.
- Challenge Matthews’ new patents that allegedly copy Tesla’s work, asking courts to invalidate them or add Tesla as co-inventor.
- Seek extra damages, lawyer fees, and possibly punitive awards under the federal Defend Trade Secrets Act and California law.
Tesla could also refer evidence to federal prosecutors for possible criminal trade-secret charges (rare but serious). Settlement is always possible, but Tesla’s fiery public response suggests they want full accountability.
This isn’t just corporate drama. It shows why trade secrets matter even when Tesla open-sources some patents, confidential know-how shared in trust must stay protected. For the EV industry, it’s a reminder: steal from your biggest customer, and you risk losing everything.
News
Tesla Cybercab includes this small but significant feature
The Cybercab is Tesla’s big plan to introduce fully autonomous ride-sharing in a seamless fashion. In fact, the Full Self-Driving suite was geared toward alleviating the need to manually drive vehicles.
Tesla Cybercab manufacturing is strikingly close, as the company is still aiming for an April start date. But small and significant features are still being identified for the first time as production units appear all over the country for testing and for regulatory events, like one yesterday in Washington, D.C.
The Cybercab is Tesla’s big plan to introduce fully autonomous ride-sharing in a seamless fashion. In fact, the Full Self-Driving suite was geared toward alleviating the need to manually drive vehicles.
This was for everyone, including the disabled, who are widely reliant on ride-sharing platforms, family members, and medical shuttles for transportation of any kind. Cybercab aims to change that, and Tesla evidently put a focus on those riders while developing the vehicle, evident in a small but significant feature revealed during its appearance in the Nation’s Capital.
Tesla Cybercab display highlights interior wizardry in the small two-seater
Tesla has implemented Braille within the Cybercab to make it easier for blind passengers to utilize the vehicle. On both the ‘Stop/Hazard Lights’ button and the Door Releases, Tesla has placed Braille so that blind passengers can navigate their way through the vehicle:
The hazard lights button will be used as an emergency stop. Smart pic.twitter.com/vkYBioqmKm
— Whole Mars Catalog (@wholemars) March 10, 2026
We have braille on the interior door releases as well
— Eric (@EricETesla) March 11, 2026
This is a great addition to the Cybercab, especially as Full Self-Driving has been partially pointed at as a solution for those with disabilities that would keep them from driving themselves from place to place.
It truly is a great addition and just another way that Tesla is showing they are making this massive product inclusive for everyone out there, including those who have not been able to drive due to not having vision.
The Cybercab is set to enter mass production sometime in April, and it will be responsible for launching Tesla’s massive plans for an autonomous ride-sharing program.
Elon Musk
Tesla and xAI team up on massive new project
It is the latest move by a Musk company to automate, streamline, and reduce the manual, monotonous, and tedious work currently performed by humans through AI and robotics development. Digital Optimus will be capable of processing and actioning the past five seconds of a real-time computer screen video and keyboard and mouse actions.
Elon Musk teased a massive new project, to be developed jointly by Tesla and xAI, called “Digital Optimus” or “Macrohard,” the first development under Tesla’s investment agreement with xAI.
Musk announced on X that Digital Optimus will “be capable of emulating the function of entire companies.”
Macrohard or Digital Optimus is a joint xAI-Tesla project, coming as part of Tesla’s investment agreement with xAI.
Grok is the master conductor/navigator with deep understanding of the world to direct digital Optimus, which is processing and actioning the past 5 secs of…
— Elon Musk (@elonmusk) March 11, 2026
It is the latest move by a Musk company to automate, streamline, and reduce the manual, monotonous, and tedious work currently performed by humans through AI and robotics development. Digital Optimus will be capable of processing and actioning the past five seconds of a real-time computer screen video and keyboard and mouse actions.
Essentially, it will be an AI version of a desk worker in many capacities, including accounting, HR tasks, and others.
Musk said:
“Grok is the master conductor/navigator with deep understanding of the world to direct digital Optimus, which is processing and actioning the past 5 secs of real-time computer screen video and keyboard/mouse actions. Grok is like a much more advanced and sophisticated version of turn-by-turn navigation software. You can think of it as Digital Optimus AI being System 1 (instinctive part of the mind) and Grok being System 2. (thinking part of the mind).”
Its key applications would be used for enterprise automation, simulating entire companies, high-volume repetitive tasks, and potentially, future hybrid use with the Optimus robot, which would handle physical tasks, while Digital Optimus would handle the clerical work.
The creation of a digital AI suite like Digital Optimus would help companies save time and money, as well as become more efficient in their operations through massive scalability. However, there will undoubtedly be concerns from people who are skeptical of a fully-integrated AI workhorse like this one.
From an energy consumption perspective and just a general concern for the human workforce, these types of AI projects are polarizing in nature.
However, Digital Optimus would be a great digital counterpart to Tesla’s physical Optimus robot, as it would be a hyper-efficient addition to any company that is looking for more production for less cost.
Musk maintains that there is no other company on Earth that will be able to do this.
Tesla VP explains latest updates in trade secret theft case
Tesla Cybercab includes this small but significant feature
