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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.
A Texas man has been arrested and charged with manslaughter after his Tesla crashed into a home last month, striking a woman inside and killing her. The driver, Michael Butler, claimed the vehicle was in self-driving mode, but information from Tesla shows that Butler overrode the system.
Butler was arrested on Wednesday and booked at the Harris County, Texas, jail. He remained in custody through Thursday and Friday; he did not enter a plea, and his next court hearing is scheduled for Monday.
Tesla finally clarifies fatal Texas crash, confirms driver manually overrode acceleration
There are a handful of new clues in the case that could clear Tesla of any wrongdoing, especially as the woman who was killed’s family, the Avilas, filed a wrongful death lawsuit against Tesla and Butler, seeking at least $1 million in damages.
Charging documents from the Harris County prosecutor now show that Butler, who was working DoorDash the evening of the accident, had been using Full Self-Driving mode without incident through the duration of multiple deliveries that evening.
In the moments leading up to the crash, while in FSD and approaching a left turn, Butler pressed the accelerator pedal, overriding FSD’s speed control, and continued to push it until it reached 100 percent. This caused rapid acceleration; the brake pedal was never pressed, and there is no data to show that Butler aimed to turn away from the curb or house.
The charging documents state:
“I noted that the brake pedal was never pressed in the final minute before the crash. I also did not see any data to indicate that the driver attempted to turn away from the curb that he eventually struck. Further, I observed that no mechanical error was detected or recorded by the vehicle before BUTLER and the Tesla struck the curb.”
Additionally, a forensic analysis of Butler’s phone showed that he searched Google around the time of the crash with queries questioning why FSD was “too timid,” “not aggressive enough,” and even searched, “FSD is not aggressive enough for city driving.”
The documents outlined this:
“Investigator Veal also informed me that he had received BUTLER’s cell phone from Deputy Amad and that HDAO digital forensics team had completed a data extraction and download of the phone. Multiple Google searches related to Tesla had been made from BUTLER’s phone in the months leading up the crash. I noted multiple searches in May of 2026 indicating an apparent frustration with Tesla’s FSD mode, including the following searches: “Tesla fsd not aggressive enough 2026 model,” “Tesla fsd not [sic) aggressive enough 2026,” “FSD is not aggressive enough for city driving,” and “tesla fsd too timid.”‘
Tesla had claimed just after the crash that its internal data showed Butler had overridden the system’s speed control and pressed the accelerator completely, causing the vehicle to travel at an excessive rate of speed. Eventually, the car slammed into Avila’s house, killing her.
Butler has now been formally charged with Manslaughter, a felony.
Tesla shocked with its quarterly delivery report yesterday by reporting it delivered 480,126 vehicles in the second quarter of 2026, a 25 percent year-over-year jump that crushed Wall Street estimates of roughly 400,000–408,000 units. Production reached 451,758, with Model 3 and Model Y accounting for the vast majority.
The result ended two years of annual delivery declines and drew down inventory, signaling demand that outpaced earlier production.
Tesla bears had long warned that the expiration of the U.S. federal EV tax credit would hammer demand. Without the $7,500 incentive, they argued, American buyers would balk at higher effective prices, leading to a sharp slowdown.
Will Tesla thrive without the EV tax credit? Five reasons why they might
That narrative has not played out as predicted. While U.S. EV sales faced broader headwinds, Tesla’s global numbers held firm, underscoring the company’s ability to offset domestic pressure through other levers.
There are several plausible factors that explain Tesla’s strength during this quarter. Let’s take a look at them:
Rising Gas Prices
Rising gas prices provided a powerful tailwind, especially in the U.S.
Geopolitical tensions tied to the Iran conflict pushed fuel costs higher earlier in the year, amplifying the lifetime savings of electric vehicles. Even as oil prices later moderated, the psychological and financial impact lingered, encouraging fleet operators and private buyers to accelerate EV purchases. European sales rebounded sharply, helping drive the quarter’s outperformance.
Full Self-Driving Adoption
Advances in Full Self-Driving (FSD) supervised software also appear to have boosted appeal. Tesla expanded FSD availability in select European markets and continued refining the system.
No complaints from me because I finally got to enjoy this drive on FSD; I usually like to manually drive down this mountain https://t.co/RBFniRPSR0 pic.twitter.com/XQ5sOpN1Yg
— TESLARATI (@Teslarati) June 26, 2026
For tech-oriented buyers, the promise of future autonomy and enhanced driver-assistance features adds perceived value beyond the car itself. This differentiation helps Tesla stand out in a crowded market where competitors focus primarily on hardware and basic range.
Pricing Strategy, Affordable Configurations
Tesla’s offerings and its pricing strategy during Q2 further stimulated demand. Tesla introduced lower-cost versions of the Model 3 and Model Y, widening accessibility without sacrificing core margins.
These moves countered affordability concerns and attracted buyers who had been waiting on the sidelines. Combined with attractive financing and leasing options, the pricing strategy converted interest into actual orders more effectively than many analysts expected.
Broad European Recovery
Supported by government incentives, corporate fleet electrification, and easing political headwinds around CEO Elon Musk, Tesla was supplied additional momentum through stronger registration numbers throughout Europe.
Strong exports from the Shanghai Gigafactory and a production ramp at Giga Berlin ensured supply met this resurgent demand. Corporate buyers, in particular, accelerated transitions to EVs to meet sustainability targets, providing a steady volume base.
These elements created a virtuous cycle that delivered the strong deliveries report. While bears correctly flagged the loss of the U.S. tax credit as a risk, Tesla’s diversified playbook demonstrated that it could remain resilient against those headwinds. The Q2 beat suggests the company remains adept at navigating shifting market conditions, even as competition intensifies.
A Tesla Semi was involved in a fatal collision on U.S. Highway 50 in Dayton, Nevada, on Sunday, June 28, 2026, marking the first known fatal crash involving the electric Class 8 truck. The incident occurred around 7:20 a.m. at the intersection with Traditions Parkway, approximately 40 miles east of Reno and close to Tesla’s Gigafactory Nevada.
According to the Lyon County Sheriff’s Office and the Nevada State Police Highway Patrol, a semi-truck struck two passenger vehicles stopped at a traffic signal. The truck hit the vehicles from behind. Two people were pronounced dead at the scene, and a third person suffered life-threatening injuries and was flown to a hospital, Forbes reported.
Preliminary statements gathered at the scene by the Lyon County Sheriff’s Office suggested the truck driver may have fallen asleep at the wheel. However, the Nevada Highway Patrol, which is leading the investigation, stated that the official cause has not yet been determined.
Additional information is expected to be released early the following week. The truck was seized for evidence as part of the ongoing probe.
Responders at the scene included deputies from the Lyon County Sheriff’s Office, personnel from the Nevada Highway Patrol, Central Lyon County Fire Department, and the Nevada Department of Transportation. The crash led to the temporary closure of U.S. 50 in both directions.
The Tesla Semi is Tesla’s battery-electric heavy-duty truck, produced at the nearby Gigafactory in Nevada. Authorities initially described the vehicle as a semi-truck; its make was subsequently confirmed through reporting and scene identification; an interesting bit of information here, as the Semi is not yet available publicly and many do not know that Tesla builds electric trucks.
The investigation remains active, with no further official details on contributing factors or vehicle systems released as of early July 2026.
This incident highlights ongoing scrutiny of commercial vehicle safety on Nevada highways, particularly involving fatigue. Law enforcement continues to gather evidence and witness statements.
Texas man charged in fatal Tesla crash where he blamed Autopilot
Tesla’s strong Q2 deliveries: Four key drivers behind the surprise
