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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 (NASDAQ: TSLA) stock received a price target update from Pierre Ferragu of Wall Street firm New Street Research, citing the company’s growing lead in self-driving and autonomy.
On Tuesday, Ferragu bumped his price target from $520 to $600, stating that the consensus from the Consumer Electronics Show in Las Vegas was that Tesla’s lead in autonomy has been sustained, is growing, and sits at a multiple-year lead over its competitors.
CES 2026 validates Tesla’s FSD strategy, but there’s a big lag for rivals: analyst
“The signal from Vegas is loud and clear,” the analyst writes. “The industry isn’t catching up to Tesla; it is actively validating Tesla’s strategy…just with a 12-year lag.”
The note shows that the company’s prowess in vehicle autonomy is being solidified by lagging competitors that claim to have the best method. The only problem is that Tesla’s Vision-based approach, which it adopted back in 2022 with the Model 3 and Model Y initially, has been proven to be more effective than competitors’ approach, which utilizes other technology, such as LiDAR and sensors.
Currently, Tesla shares are sitting at around $433, as the company’s stock price closed at $432.96 on Tuesday afternoon.
Ferragu’s consensus on Tesla shares echoes that of other Wall Street analysts who are bullish on the company’s stock and position within the AI, autonomy, and robotics sector.
Dan Ives of Wedbush wrote in a note in mid-December that he anticipates Tesla having a massive 2026, and could reach a $3 trillion valuation this year, especially with the “AI chapter” taking hold of the narrative at the company.
Ives also said that the big step in the right direction for Tesla will be initiating production of the Cybercab, as well as expanding on the Robotaxi program through the next 12 months:
“…as full-scale volume production begins with the autonomous and robotics roadmap…The company has started to test the all-important Cybercab in Austin over the past few weeks, which is an incremental step towards launching in 2026 with important volume production of Cybercabs starting in April/May, which remains the golden goose in unlocking TSLA’s AI valuation.”
Tesla analyst breaks down delivery report: ‘A step in the right direction’
Tesla has transitioned from an automaker to a full-fledged AI company, and its Robotaxi and Cybercab programs, fueled by the Full Self-Driving suite, are leading the charge moving forward. In 2026, there are major goals the company has outlined. The first is removing Safety Drivers from vehicles in Austin, Texas, one of the areas where it operates a ride-hailing service within the U.S.
Ultimately, Tesla will aim to launch a Level 5 autonomy suite to the public in the coming years.
Elon Musk
Elon Musk’s Biggest Revelations on AI, Robots, and the Future of Work from the Moonshots Podcast
Elon Musk’s appearance on the Moonshots with Peter Diamandis podcast was packed with bold predictions, candid admissions, and surprising tech insights. The nearly three-hour conversation covered everything from artificial intelligence to humanoid robots, geopolitics, and the future of work. Here are the top 10 most intriguing takeaways:
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Aggressive AGI Timeline Predictions
Musk offered a detailed view on when artificial general intelligence (AGI) could emerge, suggesting it may arrive sooner than many expect, emphasizing both transformative potential and risks.
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U.S. vs. China in the AI Race
He discussed the strategic competition between the United States and China over AI development, noting that geopolitical dynamics will shape how and who leads in the next decades.
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Future of Job Markets
Musk touched on how AI and automation could reshape employment, predicting massive boosts in productivity alongside potential disruptions in traditional work structures.
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Clean Energy Transition
A recurring theme was the role of clean energy in future economies, with Musk reiterating the importance of scaling sustainable power generation and storage.
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Humanoid Robots Are Coming
On the podcast, Musk elaborated on Tesla’s work on humanoid robots, hinting at timelines and applications that go beyond factories to general-purpose assistance.
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Tesla Roadster “Last Human-Driven Car”
Outside the core discussion topics, Musk teased features of the upcoming Tesla Roadster — calling it “the best of the last of the human-driven cars” and suggesting safety won’t be its main selling point.
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The Role of AI in Clean Energy and Robotics
Linking AI to both energy optimization and robotics, Musk explained how smarter systems could accelerate decarbonization and task automation across industries.
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U.S. Innovation Leadership
Musk argued that maintaining American leadership in key tech sectors like AI, space, and robotics should be a national priority, with thoughtful policy and investment.
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Job Creation vs. Job Elimination
While acknowledging automation’s disruptive effects, he also outlined scenarios where new industries and opportunities could emerge, particularly in AI, space, and advanced manufacturing.
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Long-Term Vision for Humanity
Throughout the conversation, Musk revisited his long-term philosophical views — including a belief in humanity’s responsibility to become a multi-planetary and technologically empowered species.
Whether you agree with Musk’s optimism or not, the podcast offers a window into the thinking of one of the most influential figures in tech today, in and why his visions continue to spark debate and inspiration.
Elon Musk appeared on the Moonshots podcast with Peter Diamandis today to discuss AGI, U.S. vs. China, Tesla, and some other interesting topics, but there was some discussion about the upcoming unveiling of the Roadster, the company’s electric supercar that will arrive several years after it was initially slated for release.
Musk made some pretty amazing claims about the Roadster; we already know it is supposed to be lightning-fast and could even hover, if Tesla gets everything to happen the way it wants to. However, the car has some pretty crazy capabilities, some of which have not even been revealed.
On the podcast, Musk said:
“This is not a…safety is not the main goal. If you buy a Ferrari, safety is not the number one goal. I say, if safety is your number one goal, do not buy the Roadster…We’ll aspire not to kill anyone in this car. It’ll be the best of the last of the human-driven cars. The best of the last.”
🚨 Elon on the Roadster unveiling, scheduled for April 1:
— TESLARATI (@Teslarati) January 6, 2026
Musk makes a good point: people who buy expensive sports cars with ridiculous top speeds and acceleration rates do not buy them to be safe. They hope they are safe in case of an emergency or crash, but safety is not at the forefront of their thoughts, because nobody buys a car thinking they’ll crash it.
The Roadster is truly going to push the limits and capabilities of passenger vehicles; there’s no doubt about that. Tesla plans to show off the new version car for the first time on April 1, and Musk has only hinted at what is possible with it.
Musk said back in November:
“Whether it’s good or bad, it will be unforgettable. My friend Peter Thiel once reflected that the future was supposed to have flying cars, but we don’t have flying cars. I think if Peter wants a flying car, he should be able to buy one…I think it has a shot at being the most memorable product unveiling ever. [It will be unveiled] hopefully before the end of the year. You know, we need to make sure that it works. This is some crazy technology in this car. Let’s just put it this way: if you took all the James Bond cars and combined them, it’s crazier than that.”
Production is set to begin between 12 and 18 months after the unveiling, which would put the car out sometime in 2027. Hopefully, Tesla is able to stay on track with the scheduling of the Roadster; many people have been waiting a long time for it.
Tesla gets price target bump, citing growing lead in self-driving
Elon Musk’s Biggest Revelations on AI, Robots, and the Future of Work from the Moonshots Podcast
