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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.
Elon Musk
Tesla Semi’s official battery capacity leaked by California regulators
A California regulatory filing just confirmed the exact battery size inside each Tesla Semi variant.
A regulatory filing published by the California Air Resources Board in April 2026 has put official numbers on what Tesla Semi owners and fleet buyers have long wanted confirmed: the exact battery capacities of both the Long Range and Standard Range Semi truck variants. CARB is California’s independent air quality regulator, and it certifies zero-emission powertrains before they can be sold or operated in the state. When a manufacturer submits a vehicle for certification, the resulting executive order becomes a public document, making it one of the most reliable sources for confirmed production specs on any EV.
The document lists two certified powertrain configurations. The Long Range Semi carries a usable battery capacity of 822 kWh, while the Standard Range version comes in at 548 kWh. Both use lithium-ion NCMA chemistry and share the same peak and steady-state motor output ratings of 800 kW and 525 kW respectively. Cross-referencing Tesla’s published efficiency figure of approximately 1.7 kWh per mile under full load, the 822 kWh pack supports roughly 480 miles of real-world range, which aligns closely with Tesla’s advertised 500-mile figure for the Long Range trim. The 548 kWh Standard Range pack works out to approximately 320 miles, again consistent with Tesla’s stated 325-mile target.
Here is a direct comparison of the two versions based on the CARB filing and published specs:
| Tesla Semi Spec | Long Range | Standard Range |
| Battery Capacity | 822 kWh | 548 kWh |
| Battery Chemistry | NCMA Li-Ion | NCMA Li-Ion |
| Peak Motor Power | 800 kW | 525 kW |
| Estimated Range | ~500 miles | ~325 miles |
| Efficiency | ~1.7 kWh/mile | ~1.7 kWh/mile |
| Est. Price | ~$290,000 | ~$260,000 |
| GVW Rating | 82,000 lbs | 82,000 lbs |
The timing of this certification is not incidental. On April 29, 2026, Semi Programme Director Dan Priestley confirmed on X that high-volume production is now ramping at Tesla’s dedicated 1.7-million-square-foot facility in Sparks, Nevada. A key advantage of the Nevada location is vertical integration: the 4680 battery cells powering the Semi are manufactured in the same complex, eliminating the supply chain bottleneck that had delayed the program for years.
Tesla’s long-term goal is to reach a production capacity of 50,000 trucks annually at the Nevada factory, which would represent roughly 20 percent of the entire North American Class 8 market. With CARB certification now in hand and the production line running, the regulatory and manufacturing groundwork for that target is in place.
Tesla became the first company to pass the United States government’s new Advanced Driver Assistance Systems (ADAS) testing with the Model Y, completing each of the new tests with a passing performance.
In a landmark announcement on May 7, the National Highway Traffic Safety Administration (NHTSA) declared the 2026 Tesla Model Y the first vehicle to pass its newly ADAS benchmark under the New Car Assessment Program (NCAP).
Model Y vehicles manufactured on or after November 12, 2025, met rigorous pass/fail criteria for four newly added tests—pedestrian automatic emergency braking, lane keeping assistance, blind spot warning, and blind spot intervention—while also satisfying the program’s original four ADAS requirements: forward collision warning, crash imminent braking, dynamic brake support, and lane departure warning.
The NHTSA has just officially announced that the 2026 @Tesla Model Y is the first vehicle model to pass the agency’s new advanced driver assistance system tests.
2026 Tesla Model Y vehicles, manufactured on or after Nov. 12, 2025, successfully met the new criteria for four… pic.twitter.com/as8x1OsSL5
— Sawyer Merritt (@SawyerMerritt) May 7, 2026
NHTSA administration Jonathan Morrison hailed the achievement as a milestone:
“Today’s announcement marks a significant step forward in our efforts to provide consumers with the most comprehensive safety ratings ever. By successfully passing these new tests, the 2026 Tesla Model Y demonstrates the lifesaving potential of driver assistance technologies and sets a high bar for the industry. We hope to see many more manufacturers develop vehicles that can meet these requirements.”
The updates to NCAP, finalized in late 2024 and effective for 2026 models, reflect growing recognition that ADAS features are no longer optional luxuries but essential tools for preventing crashes.
Pedestrian automatic emergency braking, for instance, targets one of the fastest-rising causes of roadway fatalities, while blind spot intervention and lane keeping assistance address common sources of side-swipes and run-off-road incidents. By incorporating objective, performance-based evaluations rather than mere presence of the technology, NHTSA aims to give buyers clearer data on real-world effectiveness.
This milestone arrives at a pivotal moment when vehicle autonomy is transitioning from science fiction to everyday reality.
Tesla’s Full Self-Driving (FSD) software and the impending rollout of robotaxis underscore a broader industry shift toward higher levels of automation. Yet regulators and consumers remain cautious: safety data must keep pace with technological ambition.
The Model Y’s perfect score on these ADAS benchmarks validates that current driver-assist systems—when engineered rigorously—can dramatically reduce human error, which still accounts for the vast majority of crashes.
For Tesla, the result reinforces its long-standing claim of building the safest vehicles on the road. More importantly, it signals to the entire auto sector that meeting elevated federal standards is achievable and expected.
As autonomy edges closer to Level 3 and beyond, where drivers may disengage more fully, such independent verification becomes critical. It builds public trust, informs purchasing decisions, and accelerates the development of systems that could one day eliminate tens of thousands of annual traffic deaths.
In an era when software-defined vehicles promise transformative mobility, the 2026 Model Y’s NHTSA triumph is more than a manufacturer accolade—it is a regulatory green light that autonomy’s future must be built on proven, testable safety foundations. The bar has been raised. The industry, and the roads we share, will be safer for it.
Tesla is going to fix the nearly 219,000 vehicles that it recalled due to an issue with the rearview camera with a simple software update, giving owners no need to travel to a service center to resolve the problem.
Tesla is formally recalling 218,868 U.S. vehicles after regulators discovered a software glitch that can delay the rearview camera image by up to 11 seconds when drivers shift into reverse.
The affected models include certain 2024-2025 Model 3 and Model Y, as well as 2023-2025 Model S and Model X vehicles running software version 2026.8.6 and equipped with Hardware 3 computers. The National Highway Traffic Safety Administration (NHTSA) determined the lag violates Federal Motor Vehicle Safety Standard 111 on rear visibility and could increase crash risk.
Yet this is no ordinary recall. Owners do not need to schedule a service-center visit, hand over keys, or wait for parts.
Tesla fans call for recall terminology update, but the NHTSA isn’t convinced it’s needed
Tesla identified the issue on April 10, halted further deployment of the faulty firmware the same day, and began pushing a corrective over-the-air (OTA) software update on April 11.
By the time the NHTSA posted the recall notice on May 6, more than 99.92 percent of the affected fleet had already received the fix. Tesla reports no crashes, injuries, or fatalities linked to the glitch.
The episode underscores a deeper problem with regulatory language. For decades, “recall” meant hauling a vehicle to a dealership for hardware repairs or replacements. That definition no longer fits software-defined cars. When a fix arrives wirelessly in minutes — identical to an iPhone update — the term evokes unnecessary alarm and misleads the public about the actual risk and remedy.
Elon Musk has repeatedly called for exactly this change. After earlier NHTSA actions, he stated plainly: “The terminology is outdated & inaccurate. This is a tiny over-the-air software update.” On another occasion, he added that labeling OTA fixes as recalls is “anachronistic and just flat wrong.”
The terminology is outdated & inaccurate. This is a tiny over-the-air software update. To the best of our knowledge, there have been no injuries.
— Elon Musk (@elonmusk) September 22, 2022
Musk’s point is simple: regulators must evolve their vocabulary to match the technology. Traditional recalls involve physical intervention and downtime; OTA updates do not. Retaining the old label distorts consumer perception, inflates perceived defect rates, and slows the industry’s shift to faster, safer software iteration.
Tesla’s rapid, remote remedy demonstrates the safety advantage of over-the-air capability. Problems that once required weeks of dealer appointments are now resolved in hours, often before most owners notice. As more automakers adopt software-first designs, the entire regulatory framework needs to catch up.
Updating “recall” terminology would align language with reality, reduce public confusion, and recognize that modern vehicles are no longer static hardware — they are continuously improving computers on wheels.
For the 219,000 Tesla owners involved, the process is already complete. The camera works, the car is safe, and no one left their driveway. That is the new standard — and the vocabulary should reflect it.
Tesla Semi’s official battery capacity leaked by California regulators
Tesla crushes NHTSA’s brand-new ADAS safety tests – first vehicle to ever pass
