News
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.
Lifestyle
Tesla teases “Halloween Mode” update with Optimus rising from a graveyard
Tesla’s Halloween teaser hides a covered vehicle and an Optimus hand rising from the ground.
Tesla has started teasing a Halloween software update for its vehicles, with a short clip on X that reads, “Halloween is coming.” The clip opens on a glowing pumpkin before pulling back to the car’s center touchscreen, where the usual parked visualization has been replaced by a graveyard scene, and the vehicle draped with a white sheet so it reads as a cartoon ghost.
Halloween is coming pic.twitter.com/lM3ASUeOqX
— Tesla (@Tesla) October 1, 2026
The second detail is a robotic hand clawing its way out of the dirt like a zombie, which looks to be the hand of Tesla’s latest Optimus V3 humanoid robot. While Tesla still has not formally shown Optimus Gen 3 walking around in service, renders pulled from Tesla’s Android app last month gave the clearest look yet, including far more refined hands that Tesla has said carry 22 degrees of freedom. The hand has been the hardest part of the program. Musk has called it the majority of the robot’s engineering difficulty, and Tesla’s patents describe a design driven by tendons with the actuators moved into the forearm.
Tesla Optimus V3 hand and arm details revealed in new patents
Optimus also has a Halloween track record. Last October the robot handed out candy in Times Square, and a costumed “zombie” Optimus shuffled around the Tesla Diner in Los Angeles on Halloween night.
On the software side, Tesla’s 2025 Holiday Update expanded Santa Mode with a Santa sleigh, snowmen, snow effects, and a festive lock chime, so it wouldn’t be too far fetched if we saw something similar but themed for a Halloween Mode.
Tesla Full Self-Driving is set to receive improvements to address its two biggest issues, according to a company engineer.
Director of Engineering at Tesla AI, Phil Duan, revealed in a post on X that improvements to both pothole avoidance and navigation “are coming,’ something we have heard many times in the past. However, there are a few things that seem to hint that things might be different this time around.
Improvements on both are coming.
— Phil Duan (@pduan) October 1, 2026
Pothole avoidance, navigation, speed control, and left lane camping are some of the most prevalent and frequently mentioned shortcomings of the Full Self-Driving suite. These are a few of the biggest issues that have kept Tesla Full Self-Driving as a Supervised suite, meaning drivers must remain attentive during operation.
Pothole Avoidance
Pothole avoidance was first mentioned as an “Upcoming Improvement” with the Tesla Full Self-Driving v14.3 update back in early April of this year. It was listed alongside “Expand reasoning to all behaviors beyond destination handling.”
It’s been six months since we first saw pothole avoidance explicitly mentioned, and it has not moved beyond that and joined the main release notes yet.
Tesla has not shed any light on why pothole avoidance has been such an issue for it to solve, but it also has issues identifying large bumps much of the time, so its modeling of sudden changes in road conditions is likely pretty weak at this particular point. I’ve had more issues with large bumps than potholes, personally, but both are issues that need to be resolved.
This is that big bounce that I mentioned in the quoted post.
It’s just a tad too drastic to take at the speed FSD wants to go over it. You can see me quickly swipe down into Sloth, but I intervened. https://t.co/K20PK9ysBg pic.twitter.com/81Oc82ZJcZ
— TESLARATI (@Teslarati) August 2, 2026
It makes sense that things might be pretty close to being released to the public, as we are going on such an extensive period of time between it being mentioned and it actually being deployed.
Navigation
Navigation is likely the most painful part of using Full Self-Driving, as it routinely takes strange routes, has trouble with local rules (like Except Right Turn Stop Signs in Pennsylvania), and sometimes does not realize that maneuvers it is suggesting are against the law. Turning out of my neighborhood, you cannot turn left, yet my Model Y still suggests it roughly 70 percent of the time when I’m leaving.
However, Tesla might be close to a breakthrough on this. With the Summer Update, Tesla added “Preferred Routes” alongside “Automatic Navigation.”
Preferred Routes prioritized roads that the driver had actually taken before, instead of always defaulting to what the vehicle believes is the most efficient path. This has already solved many of my issues. Formerly, I would turn off the Online Routing setting, and that would eliminate most of my complaints with routing, but then you lose out later on the Live Traffic Visualization.
Tesla’s Navigation has improved tremendously thanks to the Preferred Routes release with the Summer Update, but it still could use some polishing, as it still suggests strange routes from time to time, and it also has a lot of issues getting out of a parking lot. I find that those truly confuse FSD sometimes.
News
SpaceX’s midnight spy satellite launch quietly set a new record
Falcon Heavy launched its first NRO mission while SpaceX landed four boosters in one day.
SpaceX closed out one of its busiest days ever with a midnight Falcon Heavy launch from Florida, and the rocket’s two side boosters came home to finish off a landing record the company had never set before.
Falcon Heavy lifted off from Launch Complex 39A at NASA’s Kennedy Space Center at 11:54 p.m. ET Thursday carrying NROL-97, a classified payload for the National Reconnaissance Office. It was the first time the NRO has flown on Falcon Heavy after 22 missions on Falcon 9, and the first NRO mission bought through the National Security Space Launch Phase 3 Lane 2 contract awarded in 2025, according to Spaceflight Now.
Falcon Heavy lifts off from pad 39A in Florida for the 14th time! pic.twitter.com/uuZLKNTdD7
— SpaceX (@SpaceX) October 2, 2026
Roughly eight minutes after liftoff, side boosters B1104 and B1072 touched down at Landing Zones 1 and 2 at Cape Canaveral Space Force Station, setting off double sonic booms across Brevard County. B1104 was flying for the second time and B1072 for the fourth. Both last flew on August 30 on NASA’s Nancy Grace Roman Space Telescope, making NROL-97 the quickest turnaround between Falcon Heavy missions to date. The brand new center core, B1106, was expended in the Atlantic so the payload could reach its high energy orbit, and SpaceX’s mission page noted the fairing had previously flown on the NROL-95 mission in July.
The two landings capped a record for SpaceX. Earlier Thursday, Falcon 9 booster B1101 returned to Landing Zone 40 after sending the Crew-13 astronauts to the International Space Station, and another Falcon 9 launched the Transporter-18 rideshare with 130 payloads from Vandenberg Space Force Base in California. Spaceflight Now reported it was the first time SpaceX has landed four boosters in a single day, wrapping up the triple header Teslarati previewed on Wednesday.
Falcon Heavy’s side boosters land on LZ-1 and LZ-2 pic.twitter.com/dBPcNeggFx
— SpaceX (@SpaceX) October 2, 2026
The mission also brought Landing Zone 1 back for what may be its final landing. SpaceX first landed an orbital class booster there in December 2015, but its lease on the former Launch Complex 13 site ended in 2025 as the company moved Florida landings to new pads at its own launch complexes. With LZ-40 already holding the Crew-13 booster, SpaceX brought LZ-1 back into service for one more night. Launch tracker Next Spaceflight listed NROL-97 as the final expected landing at the site.
NROL-97 adds to a fast growing stack of national security work for SpaceX. The company has flown four Space Force missions from Vandenberg since mid August, several believed to carry Starshield satellites, pushing its Pentagon contract total for 2026 past $8 billion. Elon Musk was also named this week to help lead the Pentagon’s Project Meridian study on the future of warfare.
The Florida doubleheader stood out for another reason. The Space Coast saw only one launch in all of September as SpaceX shifts more of its East Coast infrastructure toward Starship, which reached orbit for the first time on Flight 14 just three days earlier.
Tesla teases “Halloween Mode” update with Optimus rising from a graveyard
Tesla says fixes on Full Self-Driving’s two biggest issues are on the way
