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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.
SpaceX’s Raptor engine family has steadily increased sea-level thrust while simultaneously reducing mass, turning iterative engineering gains into substantially higher performance for the Super Heavy booster. CEO Elon Musk shed more light on how powerful Raptor 4 will be, and it’s pretty crazy.
Raptor 1 delivered 185 metric tons of thrust at roughly 2,080 kilograms, while Raptor 2 raised that to 230 tons while cutting mass to about 1,630 kilograms. SpaceX data for Raptor 3 lists a target of 280 tons at 1,525 kilograms (with further reductions in associated vehicle hardware), while operational flight ratings have been cited near 250 tons.
This morning, Elon Musk stated that Raptor 4 is expected to reach 300 tons of sea-level thrust at roughly the same mass as Raptor 3. Across these versions, thrust has risen by more than 60 percent from the first engines while engine mass has fallen, producing a sharp improvement in thrust-to-weight ratio. This is a true testament to how great SpaceX engineering has been:
🚀 Elon says Raptor 4 will make 300 tons of thrust at sea level at roughly the same mass as Raptor 3.
How far Raptor has come:
Raptor 1: 185 tf, 2,080 kg
Raptor 2: 230 tf, 1,630 kg
Raptor 3: 250 tf, 1,525 kg
Raptor 4: 300 tf33 on Super Heavy = ~9,900 tf, nearly 3x Saturn V https://t.co/HQDswZhyvL
— TESLARATI (@Teslarati) October 26, 2026
With 33 engines on SuperHeavy, the jump from earlier versions to a 300-ton Raptor 4 would produce roughly 9,900 metric tons of total liftoff thrust, nearly three times the Saturn V’s approximately 3,400 metric tons from its five F-1 engines.
This is not just a larger number on a data sheet, either, as sea-level thrust is the force available the moment the vehicle leaves the pad. This is when it is heaviest and still deep in Earth’s gravity well and dense atmosphere.
Higher levels of sea-level thrust directly improves the initial thrust-to-weight ratio of the full stack. A higher ratio produces faster acceleration off the pad, which shortens the time the rocket has to spend fighting gravity, and gravity losses during the first minute or two of the flight are a major drain on total available delta-v. Every second shaved off of that early climb preserves more propellant energy for reaching orbital velocity or for carrying extra payload.
Because Super Heavy’s engine count is constrained by vehicle diameter and packing density, increasing thrust per engine is the most straightforward way to raise total liftoff performance without redesigning the booster structure or design.
The same-mass target for Raptor 4 is equally important. Holding engine mass roughly constant while raising thrust improves the engine’s own thrust-to-weight ratio and reduces the structural and propellant mass fraction that must be carried solely to support the engines.
Earlier Raptor iterations also simplified or eliminated extensive vehicle-side hardware such as heat shields. These gains compound: more force for less installed mass leaves additional capacity for propellant, payload, or operational margins such as engine-out capability.
For Starship’s architecture, the practical effects are large. Greater liftoff thrust supports heavier vehicles, larger propellant loads, and higher payloads to low Earth orbit, figures SpaceX has targeted in the 100-plus-ton reusable range. That capacity underpins orbital refueling, lunar missions, and eventual Mars cargo and crew flights.
Continued growth in sea-level thrust therefore tightens the gap between theoretical rocket performance and the high flight rates and reusability needed to make large-scale space transport practical.
Tesla has expanded its Tesla Assisted Driving (TAD) suite to its tenth European country, as Latvia joined a growing list of European Union member states authorizing the company’s advanced driver assistance technology.
Tesla Europe announced this morning that the system is now approved in Latvia, with a software rollout to begin soon.
The approval and rollout follow recognition of a provisional European type approval originally granted by the Netherlands’ RDW authority on April 10.
🚨 Latvia has just received approval for Tesla Full Self-Driving (Supervised)
This is the 10th country in the EU to gain approval:
1. Netherlands — 10 April 2026 (original RDW approval)
2. Lithuania — 20 May 2026
3. Estonia — 29 May 2026
4. Denmark — 9 June 2026
5. Belgium — 10 June 2026
6. Slovenia — 7 September 2026
7. Czechia — 21 September 2026
8. Croatia — 29 September 2026
9. Slovakia — 16 October 2026— TESLARATI (@Teslarati) October 26, 2026
Latvia’s Road Traffic Safety Directorate (CSDD) evaluated the Dutch documentation, coordinated with the other national authorities, and reviewed updates from Tesla. These included tightened limits on speed offsets, automatic resets of any manual speed adjustments after each trip, and stronger driver monitoring.
In Europe, the feature is marketed as Tesla Assisted Driving after a recent rule change that labeled the name “Full Self-Driving” as misleading.
The system remains an SAE Level 2 driver assistance tool, not unsupervised autonomy. It can control speed, steering, braking, lane changes, and intersections while following a navigation route. Drivers need not keep their hands on the wheel at all times, yet they must continuously monitor the road and be prepared to intervene immediately.
The sequence of EU recognitions began with the Netherlands in April and continued through Lithuania, Estonia, Denmark, Belgium, Slovenia, Czechia, Croatia, and Slovakia. Most have relied on mutual recognition of the single Dutch provisional approval rather than independent national testing.
These national clearances do not yet constitute bloc-wide authorization. An EU-wide decision requires a qualified majority in the Technical Committee on Motor Vehicles: at least 15 of 27 member states representing 65 percent of the EU population. Larger markets, like Germany, France, and Italy, remain under review, and a vote is expected this December.
However, for Latvian Tesla owners with compatible vehicles and software, the imminent rollout expands access to the supervised system while underscoring that attentive human oversight remains essential.
Tesla expanded its Robotaxi geofence in Houston, Texas, today, enlarging the service area and drawing a new rocket-shaped boundary across the city’s northwestern and central parts.
Although CEO Elon Musk said it was just a coincidence, the expansion seems to be an ode to SpaceX and its potential merger with Tesla.
🚨 Tesla has just expanded its Robotaxi geofence in Houston
It looks a lot like a rocket — we all remember the shape in Austin 🤣 https://t.co/cFOPEuiJ4u
— TESLARATI (@Teslarati) October 26, 2026
The official Tesla Robotaxi account announced the change with the lines “Houston, we have liftoff” and “Bigger service area now live,” pairing them with a map overlay.
The prior white geofence, roughly 25 square miles covering Jersey Village, Harvest Bend, and the Willowbrook corridor along U.S. 290 and State Highway 249, now sits inside a larger blue zone that stretches southeast toward Bellair and closer to central Houston.
Houston’s Robotaxi service launched on April 18, 2026, the same day as Dallas, as Tesla’s first unsupervised public expansion beyond Austin. From the start, the vehicles were Model Ys running without anyone in the front seats, booked through the dedicated Tesla Robotaxi app and open to the public inside the geofence.
Trackers measured the initial zone at about 25 square miles; by late September it had not grown substantially. Community and registry data indicated a modest local fleet of unsupervised Model Ys, while dozens to nearly 200 Cybercabs, the purpose-built, steering-wheel-free two-seaters, have been spotted staging and testing in Houston lots, though public Cybercab rides remain limited to Austin for now.
Across the broader Robotaxi program, Tesla began public Robotaxi rides in Austin in June 2025, as a small geofence and vehicles with safety monitors became available for a ride-hailing service. Later on, Tesla removed the monitors and steadily expanded the service area until it covered most of the Austin metro area.
Unsupervised Robotaxi services now run in six cities: Austin, Dallas, Houston, Miami, Orlando, and Tampa. Cumulative driverless miles reported by the company have reached several million. The Cybercab entered public service in Austin in early September 2026 and has scaled rapidly there. The company has also said that Dallas is next for Cybercab rides in November, with further cities and 24/7 hours targeted by year-end, plus fleet-operator sales of the vehicle.
Waymo also operates a fully driverless service in Houston, which has been open to the public since August of this year.
The Houston expansion continues Tesla’s pattern of starting with tightly controlled validation zones before widening coverage as real-world data accumulates, while the rocket motif on the map underscores the company’s habit of injecting visual flair into its autonomy rollout.
Riders inside the new boundary can request unsupervised Model Y trips in the Robotaxi app; exact operating hours and further Cybercab availability remain subject to app updates and company statements.
How SpaceX Is raising Raptor’s sea-level thrust while holding engine mass steady
Tesla Assisted Driving expands to tenth EU country
