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Stanford studies human impact when self-driving car returns control to driver

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Tesla Autopilot in 'Shadow Mode' will pit human vs computer

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.

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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.

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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.

"I write about technology and the coming zero emissions revolution."

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Tesla Semi lands the biggest electric truck deal in U.S. history

Tesla leads a record 2,500 truck order, but not every truck will be a Semi.

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Tesla has landed the largest electric truck order in U.S. history. ZET SCALE, a new alliance of shippers and carriers, named Tesla its primary manufacturer on Tuesday for an initial order of 2,500 electric Class 8 trucks. The deal alone would nearly double the number of electric heavy trucks operating in the country.

According to the press release from Catalyst Mobility, the nonprofit formerly known as CALSTART, Kenworth, RIDE and Volvo were also selected as secondary manufacturers that carriers can pick if their operations call for it. No split between the four brands has been published, so the exact number of Semis in the order is not yet known.

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Tesla won the top slot through a competitive request for proposals. The alliance, which Catalyst Mobility runs with the Smart Freight Centre, scored bidders on price, range, charging capability and production capacity. Pooling freight demand from founding shippers, including Microsoft and PepsiCo, let every truck maker bid lower than it would for a single fleet. “The Tesla Semi is designed for lower cost per mile operations than diesel,” said Dan Priestley, director of the Tesla Semi program, as noted in the press release.

The financing is built to pull in carriers who have avoided electric trucks. ZET Financial is issuing the purchase order for all 2,500 units and will place them with fleets through a fair market value lease. The trucks will be deployed over the next few years across 10 freight hubs in Los Angeles, Stockton, Bakersfield, Seattle and Tacoma, Houston, Dallas, San Antonio, Chicago, Atlanta, and the Newark and New York area. ZET SCALE says the first order is only the opening round, with a longer term goal of 10,000 trucks or more.

Even if Tesla ends up with only a majority share, it would still be the biggest Semi deal to date. Einride’s 500 unit order in August was the previous record, and WattEV’s 370 truck order in May was the largest California deal at the time. Einride’s CEO has since said he expects all 500 trucks delivered by the end of 2027.

The announcement lands two days before Tesla formally inaugurates its Semi factory in Nevada on September 24. The 1.7 million square foot plant sits next to Gigafactory Nevada’s 4680 cell lines and is designed for 50,000 trucks a year.

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Tesla integrates Grok Bot into its vehicles for the ultimate personal assistant

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Credit: Grok

Tesla has expanded Grok from an in-car chatbot into a hands-free work assistant. On September 22, Tesla officially launched Grok Bot capability, confirming that drivers can now manage email, calendars, files, chats, and tasks by voice and then hand more ambitious errands to the AI-fueled productivity cheat code.

Grok itself is built by xAI. The new car features split into two layers: Connectors link Grok to outside accounts. Grok Bot, currently limited to SuperGrok Heavy subscribers, can complete multi-step tasks such as placing a usual coffee order, booking a reservation, or scheduling an appointment. It truly puts the driver in a nearly complete hands-free driving and productivity setting, with ironically the only task truly requiring your hands being to touch the “Start Self-Driving” button.

We were granted access to Grok Bot’s Tesla integration a few weeks back, and we’ve been able to do a handful of things with it. On a handful of occasions, we’ve used it to order food and have it ready for pickup slightly later into the evening; we’ve managed to pick up groceries after a day of errands with Grok Bot, and outside of the car, it’s helped with budgeting and even my fantasy football draft.

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Tesla shows another way to utilize it: in their demo, a driver says “Hey Grok,” asks the assistant to check an inbox, and hears that a message concerns a weekend reservation. Grok then scans the calendar, reports no conflicts, and confirms the Tahoe trip is clear. It can also add check-in details to a road-trip itinerary. The point is not novelty chat. It is keeping eyes on the road, or on Full Self-Driving, while the car handles the paperwork of a trip:

This Grok rollout is not a gadget add-on as much as it is Tesla’s thesis in software form: the car should stop being a machine you operate and start being a room you occupy.

Connectors and Grok Bot treat the cabin as an office that happens to move, and that has truly been Tesla’s intention for years now. The car has slowly become an extension of a home more than a vehicle. Inbox, calendar, groceries, takeout, and reservations become voice work, not dashboard chores that you need to do before you get in your car.

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Responsibility shifts from the driver to the stack, and as many Tesla owners rely on FSD for travel, Grok Bot now handles the monotony of dinner reservations or appointments.

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Elon Musk

X changed how everyone gets paid, and this lawsuit shows why

X sued a Bitcoin account network over fake payouts as its creator pay model shifts

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Elon Musk’s X has taken a Bitcoin-focused engagement ring to court, and the case doubles as a receipt for how differently the platform pays creators today. The company filed suit in the High Court of England and Wales against Vivek Kumar Sen and Zamyang Sherpa, alleging the pair ran six accounts, including @Vivek4real_, @Bitcoin_Teddy and @TrendingBitcoin, as one coordinated operation to fake the kind of engagement that used to translate directly into money.

According to the filing, first reported by Gizmodo, the accounts posted near identical “BREAKING” crypto headlines seconds apart, in one case 11 seconds, then had three more handles like, reply to and repost the material to manufacture what X called “a false appearance of genuine, human communication and interaction.” X says the scheme pulled in at least £207,384, about $278,000, and pegs its own investigation and remediation costs at another £75,000. The accounts were suspended August 18. X general counsel James Burnham announced the case on X last weekend, writing that the company “will act forcefully to protect our platform and the earnings of genuine creators.” Musk’s own reaction, posted shortly after, was three words: “Don’t mess with 𝕏.”

The timing lines up with a a recent update to how X pays its creators. The program these accounts allegedly gamed, Creator Revenue Sharing, launched in mid 2023 and paid out based on how much a post got engaged with. Originality was never part of the formula, which is exactly how the platform ended up flooded with recycled clips, copy pasted “BREAKING” posts and replies engineered purely to farm reactions from paying subscribers.

X tried patching the model more than once, including an April cut to aggregator payouts and a March regional weighting change that Musk personally paused hours after it was announced. X retired Creator Revenue Sharing for good on September 7 and opened its replacement, Original Content Rewards, the next day.

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The new math is stricter. Payouts now come only from qualified impressions, meaning unique Home Timeline views from Premium subscribers where at least half the post is visible, and replies no longer count toward eligibility at all. Copied posts, reuploaded media and reposts without meaningful changes are explicitly excluded. Allegra Jacchia, senior product manager for Creators at SpaceXAI, which now runs X’s product and AI work following xAI’s acquisition of the platform, put it bluntly, saying the goal is to reward creators who bring original ideas and perspective, “not those who have become best at gaming the system.”

Read that way, the lawsuit isn’t really about six crypto accounts. It’s X putting a dollar figure on what the old incentive structure cost, then suing to collect it right as the new one goes live. For live updates on how the case and the new rewards program shake out, follow @Teslarati on X.

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