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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.
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Tesla adds new feature that will be great for crowded parking situations
This is the most recent iteration of the app and was priming owners for the slowly-released Holiday Update.
Tesla has added a new feature that will be great for crowded parking lots, congested parking garages, or other confusing times when you cannot seem to pinpoint where your car went.
Tesla has added a new Vehicle Locator feature to the Tesla App with App Update v4.51.5.
This is the most recent iteration of the app and was priming owners for the slowly-released Holiday Update.
While there are several new features, which we will reveal later in this article, perhaps one of the coolest is that of the Vehicle Locator, which will now point you in the direction of your car using a directional arrow on the home screen. This is similar to what Apple uses to find devices:
Interesting. The location arrow in the Tesla app now points to your car when you’re nearby. pic.twitter.com/b0yjmwwzxN
— Whole Mars Catalog (@wholemars) December 7, 2025
In real time, the arrow gives an accurate depiction of which direction you should walk in to find your car. This seems extremely helpful in large parking lots or unfamiliar shopping centers.
Getting to your car after a sporting event is an event all in itself; this feature will undoubtedly help with it:
The nice little touch that Tesla have put in the app – continuous tracking of your vehicle location relative to you.
There’s people reporting dizziness testing this.
To those I say… try spinning your phone instead. 😉 pic.twitter.com/BAYmJ3mzzD
— Some UK Tesla Guy (UnSupervised…) (@SomeUKTeslaGuy) December 8, 2025
Tesla’s previous app versions revealed the address at which you could locate your car, which was great if you parked on the street in a city setting. It was also possible to use the map within the app to locate your car.
However, this new feature gives a more definitive location for your car and helps with the navigation to it, instead of potentially walking randomly.
It also reveals the distance you are from your car, which is a big plus.
Along with this new addition, Tesla added Photobooth features, Dog Mode Live Activity, Custom Wraps and Tints for Colorizer, and Dashcam Clip details.
🚨 Tesla App v4.51.5 looks to be preparing for the Holiday Update pic.twitter.com/ztts8poV82
— TESLARATI (@Teslarati) December 8, 2025
All in all, this App update was pretty robust.
Tesla CEO Elon Musk shaded Waymo in a post on X on Wednesday, stating the company “never really had a chance” and that it “will be obvious in hindsight.”
Tesla and Waymo are the two primary contributors to the self-driving efforts in the United States, with both operating driverless ride-hailing services in the country. Tesla does have a Safety Monitor present in its vehicles in Austin, Texas, and someone in the driver’s seat in its Bay Area operation.
Musk says the Austin operation will be completely void of any Safety Monitors by the end of the year.
🚨 Tesla vs. Waymo Geofence in Austin https://t.co/A6ffPtp5xv pic.twitter.com/mrnL0YNSn4
— TESLARATI (@Teslarati) December 10, 2025
With the two companies being the main members of the driverless movement in the U.S., there is certainly a rivalry. The two have sparred back and forth with their geofences, or service areas, in both Austin and the Bay Area.
While that is a metric for comparison now, ultimately, it will not matter in the coming years, as the two companies will likely operate in a similar fashion.
Waymo has geared its business toward larger cities, and Tesla has said that its self-driving efforts will expand to every single one of its vehicles in any location globally. This is where the true difference between the two lies, along with the fact that Tesla uses its own vehicles, while Waymo has several models in its lineup from different manufacturers.
The two also have different ideas on how to solve self-driving, as Tesla uses a vision-only approach. Waymo relies on several things, including LiDAR, which Musk once called “a fool’s errand.”
This is where Tesla sets itself apart from the competition, and Musk highlighted the company’s position against Waymo.
Jeff Dean, the Chief Scientist for Google DeepMind, said on X:
“I don’t think Tesla has anywhere near the volume of rider-only autonomous miles that Waymo has (96M for Waymo, as of today). The safety data is quite compelling for Waymo, as well.”
Musk replied:
“Waymo never really had a chance against Tesla. This will be obvious in hindsight.”
Waymo never really had a chance against Tesla. This will be obvious in hindsight.
— Elon Musk (@elonmusk) December 10, 2025
Tesla stands to have a much larger fleet of vehicles in the coming years if it chooses to activate Robotaxi services with all passenger vehicles. A simple Over-the-Air update will activate this capability, while Waymo would likely be confined to the vehicles it commissions as Robotaxis.
News
Tesla supplier Samsung preps for AI5 production with latest move
According to a new report from Sedaily, Samsung is accelerating its preparation for U.S. production of the AI5 chips by hiring veteran engineers for its Customer Engineering team.
Tesla supplier Samsung is preparing to manufacture the AI5 chip, which will launch the company’s self-driving efforts even further, with its latest move.
According to a new report from Sedaily, Samsung is accelerating its preparation for U.S. production of the AI5 chips by hiring veteran engineers for its Customer Engineering team, which will help resolve complex foundry challenges, stabilize production and yields, and ensure manufacturing goes smoothly for the new project.
The hiring push signals that Tesla’s AI5 project is moving forward quickly at Samsung, which was one of two suppliers to win a contract order from the world’s leading EV maker.
🚨🚨 FIRST LOOK at Tesla’s AI5 chip, which will be available in late 2026 or early 2027 pic.twitter.com/aLomUuifhT
— TESLARATI (@Teslarati) November 6, 2025
TSMC is the other. TSMC is using its 3nm process, reportedly, while Samsung will do a 2nm as a litmus test for the process.
The different versions are due to the fact that “they translate designs to physical form differently,” CEO Elon Musk said recently. The goal is for the two to operate identically, obviously, which is a challenge.
Some might remember Apple’s A9 “Chipgate” saga, which found that the chips differed in performance because of different manufacturers.
The AI5 chip is Tesla’s next-generation hardware chip for its self-driving program, but it will also contribute to the Optimus program and other AI-driven features in both vehicles and other projects. Currently, Tesla utilizes AI4, formerly known as HW4 or Hardware 4, in its vehicles.
Tesla teases new AI5 chip that will revolutionize self-driving
AI5 is specialized for use by Tesla as it will work in conjunction with the company’s Neural Networks, focusing on real-time inference to make safe and logical decisions during operation.
Musk said it was an “amazing design” and an “immense jump” from Tesla’s current AI4 chip. It will be roughly 40 times faster, and have 8 times the raw compute, with 9 times the memory capacity. It is also expected to be three times as efficient per watt as AI4.
“We’re going to focus TSMC and Samsung, initially, on AI5. The AI5 chip, design by Tesla, it’s an amazing design. I’ve spent almost every weekend for the last few months with the chip team working on AI5.”
It will be 40x better than the AI4 chip, Musk says.
— TESLARATI (@Teslarati) October 22, 2025
AI5 will make its way into “maybe a small number of units” next year, Musk confirmed. However, it will not make its way to high-volume production until 2027. AI5 is not the last step, either, as Musk has already confirmed AI6 would likely enter production in mid-2028.
Tesla adds new feature that will be great for crowded parking situations
Tesla CEO Elon Musk shades Waymo: ‘Never really had a chance’
