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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.

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“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.

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“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 fleet is nearing 7 billion total miles, including 2.5 billion city miles

As can be seen on Tesla’s official FSD webpage, vehicles equipped with the system have now navigated over 6.99 billion miles.

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Tesla’s Full Self-Driving (Supervised) fleet is closing in on almost 7 billion total miles driven, as per data posted by the company on its official FSD webpage. 

These figures hint at the massive scale of data fueling Tesla’s rapid FSD improvements, which have been quite notable as of late.

FSD mileage milestones

As can be seen on Tesla’s official FSD webpage, vehicles equipped with the system have now navigated over 6.99 billion miles. Tesla owner and avid FSD tester Whole Mars Catalog also shared a screenshot indicating that from the nearly 7 billion miles traveled by the FSD fleet, more than 2.5 billion miles were driven inside cities. 

City miles are particularly valuable for complex urban scenarios like unprotected turns, pedestrian interactions, and traffic lights. This is also the difference-maker for FSD, as only complex solutions, such as Waymo’s self-driving taxis, operate similarly on inner-city streets. And even then, incidents such as the San Francisco blackouts have proven challenging for sensor-rich vehicles like Waymos. 

Tesla’s data edge

Tesla has a number of advantages in the autonomous vehicle sector, one of which is the size of its fleet and the number of vehicles training FSD on real-world roads. Tesla’s nearly 7 billion FSD miles then allow the company to roll out updates that make its vehicles behave like they are being driven by experienced drivers, even if they are operating on their own. 

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So notable are Tesla’s improvements to FSD that NVIDIA Director of Robotics Jim Fan, after experiencing FSD v14, noted that the system is the first AI that passes what he described as a “Physical Turing Test.” 

“Despite knowing exactly how robot learning works, I still find it magical watching the steering wheel turn by itself. First it feels surreal, next it becomes routine. Then, like the smartphone, taking it away actively hurts. This is how humanity gets rewired and glued to god-like technologies,” Fan wrote in a post on X. 

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Tesla starts showing how FSD will change lives in Europe

Local officials tested the system on narrow country roads and were impressed by FSD’s smooth, human-like driving, with some calling the service a game-changer for everyday life in areas that are far from urban centers.

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Credit: Grok Imagine

Tesla has launched Europe’s first public shuttle service using Full Self-Driving (Supervised) in the rural Eifelkreis Bitburg-Prüm region of Germany, demonstrating how the technology can restore independence and mobility for people who struggle with limited transport options. 

Local officials tested the system on narrow country roads and were impressed by FSD’s smooth, human-like driving, with some calling the service a game-changer for everyday life in areas that are far from urban centers.

Officials see real impact on rural residents

Arzfeld Mayor Johannes Kuhl and District Administrator Andreas Kruppert personally tested the Tesla shuttle service. This allowed them to see just how well FSD navigated winding lanes and rural roads confidently. Kruppert said, “Autonomous driving sounds like science fiction to many, but we simply see here that it works totally well in rural regions too.” Kuhl, for his part, also noted that FSD “feels like a very experienced driver.”

The pilot complements the area’s “Citizen Bus” program, which provides on-demand rides for elderly residents who can no longer drive themselves. Tesla Europe shared a video of a demonstration of the service, highlighting how FSD gives people their freedom back, even in places where public transport is not as prevalent.

What the Ministry for Economic Affairs and Transport says

Rhineland-Palatinate’s Minister Daniela Schmitt supported the project, praising the collaboration that made this “first of its kind in Europe” possible. As per the ministry, the rural rollout for the service shows FSD’s potential beyond major cities, and it delivers tangible benefits like grocery runs, doctor visits, and social connections for isolated residents. 

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“Reliable and flexible mobility is especially vital in rural areas. With the launch of a shuttle service using self-driving vehicles (FSD supervised) by Tesla in the Eifelkreis Bitburg-Prüm, an innovative pilot project is now getting underway that complements local community bus services. It is the first project of its kind in Europe. 

“The result is a real gain for rural mobility: greater accessibility, more flexibility and tangible benefits for everyday life. A strong signal for innovation, cooperation and future-oriented mobility beyond urban centers,” the ministry wrote in a LinkedIn post

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Tesla China quietly posts Robotaxi-related job listing

Tesla China is currently seeking a Low Voltage Electrical Engineer to work on circuit board design for the company’s autonomous vehicles.

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Credit: Tesla

Tesla has posted a new job listing in Shanghai explicitly tied to its Robotaxi program, fueling speculation that the company is preparing to launch its dedicated autonomous ride-hailing service in China. 

As noted in the listing, Tesla China is currently seeking a Low Voltage Electrical Engineer to work on circuit board design for the company’s autonomous vehicles.

Robotaxi-specific role

The listing, which was shared on social media platform X by industry watcher @tslaming, suggested that Tesla China is looking to fill the role urgently. The job listing itself specifically mentions that the person hired for the role will be working on the Low Voltage Hardware team, which would design the circuit boards that would serve as the nervous system of the Robotaxi. 

Key tasks for the role, as indicated in the job listing, include collaboration with PCB layout, firmware, mechanical, program management, and validation teams, among other responsibilities. The role is based in Shanghai.

China Robotaxi launch

China represents a massive potential market for robotaxis, with its dense urban centers and supportive policies in select cities. Tesla has limited permission to roll out FSD in the country, though despite this, its vehicles have been hailed as among the best in the market when it comes to autonomous features. So far, at least, it appears that China supports Tesla’s FSD and Robotaxi rollout.

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This was hinted at in November, when Tesla brought the Cybercab to the 8th China International Import Expo (CIIE) in Shanghai, marking the first time that the autonomous two-seater was brought to the Asia-Pacific region. The vehicle, despite not having a release date in China, received a significant amount of interest among the event’s attendees. 

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