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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 revealed its Cybertruck light bar installation fix after a recall exposed a serious issue with the accessory.
Tesla and the National Highway Traffic Safety Administration (NHTSA) initiated a recall of 6,197 Cybertrucks back in October to resolve an issue with the Cybertruck light bar accessory. It was an issue with the adhesive that was provided by a Romanian company called Hella Romania S.R.L.
Tesla recalls 6,197 Cybertrucks for light bar adhesive issue
The issue was with the primer quality, as the recall report from the NHTSA had stated the light bar had “inadvertently attached to the windshield using the incorrect surface primer.”
Instead of trying to adhere the light bar to the Cybertruck with an adhesive, Tesla is now going to attach it with a bracketing system, which will physically mount it to the vehicle instead of relying on adhesive strips or glue.
Tesla outlines this in its new Service Bulletin, labeled SB-25-90-001, (spotted by Not a Tesla App) where it shows the light bar will be remounted more securely:
The entire process will take a few hours, but it can be completed by the Mobile Service techs, so if you have a Cybertruck that needs a light bar adjustment, it can be done without taking the vehicle to the Service Center for repair.
However, the repair will only happen if there is no delamination or damage present; then Tesla could “retrofit the service-installed optional off-road light bar accessory with a positive mechanical attachment.”
The company said it would repair the light bar at no charge to customers. The light bar issue was one that did not result in any accidents or injuries, according to the NHTSA’s report.
This was the third recall on Cybertruck this year, as one was highlighted in March for exterior trim panels detaching during operation. Another had to do with front parking lights being too bright, which was fixed with an Over-the-Air update last month.
News
Tesla is already expanding its Rental program aggressively
The program has already launched in a handful of locations, specifically, it has been confined to California for now. However, it does not seem like Tesla has any interest in keeping it restricted to the Golden State.
Tesla is looking to expand its Rental Program aggressively, just weeks after the program was first spotted on its Careers website.
Earlier this month, we reported on Tesla’s intention to launch a crazy new Rental program with cheap daily rates, which would give people in various locations the opportunity to borrow a vehicle in the company’s lineup with some outrageous perks.
Along with the cheap rates that start at about $60 per day, Tesla also provides free Full Self-Driving operation and free Supercharging for the duration of the rental. There are also no limits on mileage or charging, but the terms do not allow the renter to leave the state from which they are renting.
🚨🚨 If you look up details on the Tesla Rental program on Google, you’ll see a bunch of sites saying it’s because of decreasing demand 🤣 pic.twitter.com/WlSQrDJhMg
— TESLARATI (@Teslarati) November 10, 2025
The program has already launched in a handful of locations, specifically, it has been confined to California for now. However, it does not seem like Tesla has any interest in keeping it restricted to the Golden State.
Job postings from Tesla now show it is planning to launch the Rental program in at least three new states: Texas, Tennessee, and Massachusetts.
The jobs specifically are listed as a Rental Readiness Specialist, which lists the following job description:
“The Tesla Rental Program is looking for a Rental Readiness Specialist to work on one of the most progressive vehicle brands in the world. The Rental Readiness Specialist is a key contributor to the Tesla experience by coordinating the receipt of incoming new and used vehicle inventory. This position is responsible for fleet/lot management, movement of vehicles, vehicle readiness, rental invoicing, and customer hand-off. Candidates must have a high level of accountability, and personal satisfaction in doing a great job.”
It also says that those who take the position will have to charge and clean the cars, work with clients on scheduling pickups and drop-offs, and prepare the paperwork necessary to initiate the rental.
The establishment of a Rental program is big for Tesla because it not only gives people the opportunity to experience the vehicles, but it is also a new way to rent a car.
Just as the Tesla purchasing process is more streamlined and more efficient than the traditional car-buying experience, it seems this could be less painful and a new way to borrow a car for a trip instead of using your own.
Elon Musk
Elon Musk’s xAI gains first access to Saudi supercluster with 600k Nvidia GPUs
The facility will deploy roughly 600,000 Nvidia GPUs, making it one of the world’s most notable superclusters.
A Saudi-backed developer is moving forward with one of the world’s largest AI data centers, and Elon Musk’s xAI will be its first customer. The project, unveiled at the U.S.–Saudi Investment Forum in Washington, D.C., is being built by Humain, a company supported by Saudi Arabia’s Public Investment Fund.
The facility will deploy roughly 600,000 Nvidia GPUs, making it one of the world’s most notable superclusters.
xAI secures priority access
Nvidia CEO Jensen Huang stated that the planned data center marks a major leap not just for the region but for the global AI ecosystem as a whole. Huang joked about the sheer capacity of the build, emphasizing how unusual it is for a startup to receive infrastructure of such magnitude. The facility is designed to deliver 500 megawatts of Nvidia GPU power, placing it among the world’s largest AI-focused installations, as noted in a Benzinga report.
“We worked together to get this company started and off the ground and just got an incredible customer with Elon. Could you imagine a startup company, approximately $0 billion in revenues, now going to build a data center for Elon? 500 megawatts is gigantic. This company is off the charts right away,” Huang said.
Global Chipmakers Join Multi-Vendor Buildout To Enhance Compute Diversity
While Nvidia GPUs serve as the backbone of the first phase, Humain is preparing a diversified hardware stack. AMD will supply its Instinct MI450 accelerators, which could draw up to 1 gigawatt of power by 2030 as deployments ramp. Qualcomm will also contribute AI200 and AI250 data center processors, accounting for an additional 200 megawatts of compute capacity. Cisco will support the networking and infrastructure layer, helping knit the multi-chip architecture together.
Apart from confirming that xAI will be the upcoming supercluster’s first customer, Musk also joked about the rapid scaling needed to train increasingly large AI models. He joked that a theoretical expansion one thousand times larger of the upcoming supercluster “would be 8 bazillion, trillion dollars,” highlighting the playful exaggeration he often brings to discussions around extreme compute demand.
Tesla reveals its Cybertruck light bar installation fix
Tesla is already expanding its Rental program aggressively
