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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 FSD (Supervised) is about to go on “widespread” release
In a comment last October, Elon Musk stated that FSD V14.2 is “for widespread use.”
Tesla has begun rolling out Full Self-Driving (Supervised) V14.2, and with this, the wide release of the system could very well begin.
The update introduces a new high-resolution vision encoder, expanded emergency-vehicle handling, smarter routing, new parking options, and more refined driving behavior, among other improvements.
FSD V14.2 improvements
FSD (Supervised) V14.2’s release notes highlight a fully upgraded neural-network vision encoder capable of reading higher-resolution features, giving the system improved awareness of emergency vehicles, road obstacles, and even human gestures. Tesla also expanded its emergency-vehicle protocols, adding controlled pull-overs and yielding behavior for police cars, fire trucks, and ambulances, among others.
A deeper integration of navigation and routing into the vision network now allows the system to respond to blocked roads or detours in real time. The update also enhances decision-making in several complex scenarios, including unprotected turns, lane changes, vehicle cut-ins, and interactions with school buses. All in all, these improvements should help FSD (Supervised) V14.2 perform in a very smooth and comfortable manner.
Elon Musk’s predicted wide release
The significance of V14.2 grows when paired with Elon Musk’s comments from October. While responding to FSD tester AI DRIVR, who praised V14.1.2 for fixing “95% of indecisive lane changes and braking” and who noted that it was time for FSD to go on wide release, Musk stated that “14.2 for widespread use.”
FSD V14 has so far received a substantial amount of positive reviews from Tesla owners, many of whom have stated that the system now drives better than some human drivers as it is confident, cautious, and considerate at the same time. With V14.2 now rolling out, it remains to be seen if the update also makes it to the company’s wide FSD fleet, which is still populated by a large number of HW3 vehicles.
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Tesla FSD V14.2 starts rolling out to initial batch of vehicles
It would likely only be a matter of time before FSD V14.2 videos are posted and shared on social media.
Tesla has begun pushing Full Self-Driving (Supervised) v14.2 to its initial batch of vehicles. The update was initially observed by Tesla owners and veteran FSD users on social media platform X on Friday.
So far, reports of the update have been shared by Model Y owners in California whose vehicles are equipped with the company’s AI4 hardware, though it would not be surprising if more Tesla owners across the country receive the update as well.
Based on the release notes of the update, key improvements in FSD V14.2 include a revamped neural network for better detection of emergency vehicles, obstacles, and human gestures, as well as options to select arrival spots.
It would likely only be a matter of time before FSD V14.2 videos are posted and shared on social media.
Following are the release notes of FSD (Supervised) V14.2, as shared on X by longtime FSD tester Whole Mars Catalog.
Release Notes
2025.38.9.5
Currently Installed
FSD (Supervised) v14.2
Full Self-Driving (Supervised) v14.2 includes:
- Upgraded the neural network vision encoder, leveraging higher resolution features to further improve scenarios like handling emergency vehicles, obstacles on the road, and human gestures.
- Added Arrival Options for you to select where FSD should park: in a Parking Lot, on the Street, in a Driveway, in a Parking Garage, or at the Curbside.
- Added handling to pull over or yield for emergency vehicles (e.g. police cars, fire trucks, ambulances.
- Added navigation and routing into the vision-based neural network for real-time handling of blocked roads and detours.
- Added additional Speed Profile to further customize driving style preference.
- Improved handling for static and dynamic gates.
- Improved offsetting for road debris (e.g. tires, tree branches, boxes).
- Improve handling of several scenarios including: unprotected turns, lane changes, vehicle cut-ins, and school busses.
- Improved FSD’s ability to manage system faults and improve scenarios like handling emergency vehicles, obstacles on the road, and human gestures.
- Added Arrival Options for you to select where FSD should park: in a Parking Lot, on the Street, in a Driveway, in a Parking Garage, or at the Curbside.
- Added handling to pull over or yield for emergency vehicles (e.g. police cars, fire trucks, ambulances).
- Added navigation and routing into the vision-based neural network for real-time handling of blocked roads and detours.
- Added additional Speed Profile to further customize driving style preference.
- Improved handling for static and dynamic gates.
- Improved offsetting for road debris (e.g. tires, tree branches, boxes).
- Improve handling of several scenarios, including unprotected turns, lane changes, vehicle cut-ins, and school buses.
- Improved FSD’s ability to manage system faults and recover smoothly from degraded operation for enhanced reliability.
- Added alerting for residue build-up on interior windshield that may impact front camera visibility. If affected, visit Service for cleaning!
Upcoming Improvements:
- Overall smoothness and sentience
- Parking spot selection and parking quality
The Tesla Model X has always been one of the company’s most loved vehicles, despite its low sales figures, which can be attributed to its high price tag.
However, the Model X has been a signature item on Tesla’s menu of cars, most notably recognized by its Falcon Wing Doors, which are aware of its surroundings and open according to what’s around it.
But recent improvements to the Model X were looking slim to none, but it appears most of the fixes actually happened under the body, at least according to Tesla’s Vice President of Powertrain, Lars Moravy.
In a recent interview with Car and Driver, Moravy detailed all of the changes to the 2026 iteration of the vehicle, which was about 400 pounds lighter than it was originally. The biggest change is a modification with the rear motor, switching from an induction-type motor to a permanent-magnet design and optimizing the half-shafts, which shed about 100 pounds.
Tesla also got “almost 80 pounds out of the interior bits and pieces,” which “included making parts thinner, different manufacturing process choices, and incorporating airbag-deployment requirements into the headliner fabric,” the report said.
Additionally, the standard five-passenger, bench seat configuration saved 50 pounds by ditching pedestal mounting. This also helped with practicality, as it helped the seat fold flat. Engineers at Tesla also saved 44 pounds from the high-voltage wiring through optimizing the wiring from the charge-port DC/DC converter and switching from copper to aluminum wiring.
Tesla makes a decision on the future of its flagship Model S and Model X
Tesla also simplified the cooling system by reducing the number of radiators. It also incorporated Nürburgring cooling requirements for the Plaid variant, which saved nearly 30 pounds.
Many Tesla fans will be familiar with the megacastings, manufactured in-house by presses from IDRA, which also saves more than 20 pounds and boosts torsional stiffness by around 10 percent. Tweaks to the suspension also saved 10 pounds.
People were truly disappointed with what Tesla did with the Model S and Model X, arguing that the cars needed a more severe exterior overhaul, which might be true. However, Tesla really did a lot to reduce the weight of the vehicle, which helps increase range and efficiency. According to Grok, every 200 pounds removed adds between 7 and 15 percent to range estimations.
This makes sense considering the range estimations both increased by 7 percent from the Model X’s 2025 configuration to the 2026 builds. Range increased on the All-Wheel-Drive trim from 329 miles to 352 miles, while the Plaid went from 314 miles to 335 miles.
Tesla FSD (Supervised) is about to go on “widespread” release
Tesla FSD V14.2 starts rolling out to initial batch of vehicles
