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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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GM CEO Mary Barra says she told Biden to give Tesla and Musk EV credit

“He was crediting me, and I said, ‘Actually, I think a lot of that credit goes to Elon and Tesla…You know me, Andrew. I don’t want to take credit for things.”

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General Motors CEO Mary Barra said in a new interview on Wednesday that she told President Joe Biden to credit Tesla and its CEO, Elon Musk, for the widespread electric vehicle transition.

She said she told Biden this after the former President credited her and GM for leading EV efforts in the United States.

During an interview at the New York Times Dealbook Summit with Andrew Ross Sorkin, Barra said she told Biden that crediting her was essentially a mistake, and that Musk and Tesla should have been explicitly mentioned (via Business Insider):

“He was crediting me, and I said, ‘Actually, I think a lot of that credit goes to Elon and Tesla…You know me, Andrew. I don’t want to take credit for things.”

Back in 2021, President Biden visited GM’s “Factory Zero” plant in Detroit, which was the centerpiece of the company’s massive transition to EVs. The former President went on to discuss the EV industry, and claimed that GM and Barra were the true leaders who caused the change:

“In the auto industry, Detroit is leading the world in electric vehicles. You know how critical it is? Mary, I remember talking to you way back in January about the need for America to lead in electric vehicles. I can remember your dramatic announcement that by 2035, GM would be 100% electric. You changed the whole story, Mary. You did, Mary. You electrified the entire automotive industry. I’m serious. You led, and it matters.”

People were baffled by the President’s decision to highlight GM and Barra, and not Tesla and Musk, who truly started the transition to EVs. GM, Ford, and many other companies only followed in the footsteps of Tesla after it started to take market share from them.

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Elon Musk and Tesla try to save legacy automakers from Déjà vu

Musk would eventually go on to talk about Biden’s words later on:

They have so much power over the White House that they can exclude Tesla from an EV Summit. And, in case the first thing, in case that wasn’t enough, then you have President Biden with Mary Barra at a subsequent event, congratulating Mary for having led the EV revolution.”

In Q4 2021, which was shortly after Biden’s comments, Tesla delivered 300,000 EVs. GM delivered just 26.

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Tesla Full Self-Driving shows confident navigation in heavy snow

So far, from what we’ve seen, snow has not been a huge issue for the most recent Full Self-Driving release. It seems to be acting confidently and handling even snow-covered roads with relative ease.

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Credit: Grok

Tesla Full Self-Driving is getting its first taste of Winter weather for late 2025, as snow is starting to fall all across the United States.

The suite has been vastly improved after Tesla released v14 to many owners with capable hardware, and driving performance, along with overall behavior, has really been something to admire. This is by far the best version of FSD Tesla has ever released, and although there are a handful of regressions with each subsequent release, they are usually cleared up within a week or two.

Tesla is releasing a modified version of FSD v14 for Hardware 3 owners: here’s when

However, adverse weather conditions are something that Tesla will have to confront, as heavy rain, snow, and other interesting situations are bound to occur. In order for the vehicles to be fully autonomous, they will have to go through these scenarios safely and accurately.

One big issue I’ve had, especially in heavy rain, is that the camera vision might be obstructed, which will display messages that certain features’ performance might be degraded.

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So far, from what we’ve seen, snow has not been a huge issue for the most recent Full Self-Driving release. It seems to be acting confidently and handling even snow-covered roads with relative ease:

Moving into the winter months, it will be very interesting to see how FSD handles even more concerning conditions, especially with black ice, freezing rain and snow mix, and other things that happen during colder conditions.

We are excited to test it ourselves, but I am waiting for heavy snowfall to make it to Pennsylvania so I can truly push it to the limit.

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Tesla hosts Rome Mayor for first Italian FSD Supervised road demo

The event marked the first time an Italian mayor tested the advanced driver-assistance system in person in Rome’s urban streets.

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Credit: @andst7/X

Tesla definitely seems to be actively engaging European officials on FSD’s capabilities, with the company hosting Rome Mayor Roberto Gualtieri and Mobility Assessor Eugenio Patanè for a hands-on road demonstration. 

The event marked the first time an Italian mayor tested the advanced driver-assistance system in person in Rome’s urban streets. This comes amid Tesla’s push for FSD’s EU regulatory approvals in the coming year.

Rome officials experience FSD Supervised

Tesla conducted the demo using a Model 3 equipped with Full Self-Driving (Supervised), tackling typical Roman traffic including complex intersections, roundabouts, pedestrian crossings and mixed users like cars, bikes and scooters.

The system showcased AI-based assisted driving, prioritizing safety while maintaining flow. FSD also handled overtakes and lane decisions, though with constant driver supervision.

Investor Andrea Stroppa detailed the event on X, noting the system’s potential to reduce severe collision risks by up to seven times compared to traditional driving, based on Tesla’s data from billions of global fleet miles. The session highlighted FSD’s role as an assistance tool in its Supervised form, not a replacement, with the driver fully responsible at all times.

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Path to European rollout

Tesla has logged over 1 million kilometers of testing across 17 European countries, including Italy, to refine FSD for local conditions. The fact that Rome officials personally tested FSD Supervised bodes well for the program’s approval, as it suggests that key individuals are closely watching Tesla’s efforts and innovations.

Assessor Patanè also highlighted the administration’s interest in technologies that boost road safety and urban travel quality, viewing them as aids for both private and public transport while respecting rules.

Replies on X urged involving Italy’s Transport Ministry to speed approvals, with one user noting, “Great idea to involve the mayor! It would be necessary to involve components of the Ministry of Transport and the government as soon as possible: it’s they who can accelerate the approval of FSD in Italy.”

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