The Insurance Institute for Highway Safety (IIHS) tested Tesla Autopilot safeguards and found that drivers are pretty quick to adapt to the windows of opportunity the suite gives after warning them to pay attention.
The IIHS study sought to determine whether partially automated driving systems and their safeguards increase driver attentiveness. With the rollout of more advanced driver assistance systems (ADAS) and semi-autonomous driving functionalities, the goal is to increase safety.
However, these suites still require the driver to pay attention and be aware of any potential opportunity to take over if needed. These driving systems and features are designed to increase safety but still require the driver’s full attention, hence their semi-autonomous label.
Credit: Tesla
For the study, the IIHS tested both Tesla Autopilot safeguards and those available in Volvo’s Pilot Assist.
The study gave 14 drivers a month with a 2020 Tesla Model 3 and required them to travel on Autopilot, when available, over one month. The IIHS wanted to see how drivers behaved leading up to, during, and after attention reminders prompted by a lack of focus on their end.
The Autopilot study found that drivers could learn safeguard sequences and identify “windows of opportunity” to perform non-driving-related tasks. These vehicles still utilized an Autopilot nag and a torque sensor to monitor whether the driver was paying attention. Failure to keep hands on the steering wheel would result in attention reminders.
Failure to change after the reminders would result in suspension of the Autopilot system, commonly referred to as “Autopilot jail.”
The study found:
“In total, the volunteers drove a little more than 12,000 miles with Autopilot engaged. During that time, they triggered 3,858 attention-related warnings from the partial automation system. About half of those alerts occurred when they had at least one hand on the steering wheel but were apparently not moving it enough to satisfy the torque sensor.”
Most warnings did not go past the initial reminder, and only 72 instances resulted in the driver not responding fast enough to prevent the alerts from escalating.
The study found that while initial warnings increased by 26 percent over the first four weeks, showing drivers were prone to expect it, escalations fell by 64 percent, meaning they did not allow the system to continue warning them.
However, this does not mean that non-driving secondary activities stopped after the first warning. Instead, the study showed something interesting:
“The researchers found that the drivers did nondriving secondary activities, looked away from the road, and had both hands off the wheel more often during the alerts and in the 10 seconds before and after them as they learned how the attention reminders worked. The longer they used the system, the less time it took them to take their hands off the wheel again once the alerts stopped.”
The IIHS admits that the safety impact of the change is hard to measure. While the agency noted that some research shows the longer a driver allows their attention to wander, the more likely they will be involved in an accident, the study also said that “even short lapses of attention become so frequent that the periods of supposed engagement between them have little value.”
The study also said the safeguards can be beneficial to behavior immediately and in the longer term, and other patterns showed potentially unintended consequences:
“The current study has shown that driver interactions with partial automation are dynamic. Some of the changes we observed indicate that system safeguards can beneficially shape behavior both immediately and in the longer term, whereas other patterns revealed potentially unintended consequences. It is important to note that these findings are likely not unique to Tesla’s Autopilot, as many systems on the market have overtly similar safeguard designs. As such, some observations from this study maybe relevant to other driver assistance technology that still requires the driver to be engaged in the driving task.”
IIHS Senior Research Scientist Alexandra Mueller, who led the study, said:
“These results show that escalating, multimodal attention reminders are very effective in getting drivers to change their behavior. However, better safeguards are needed to ensure that the behavior change actually translates to more attentive driving.”
While this study provides evidence that perhaps better safeguards are needed, it is important to note that Tesla has upgraded the in-cabin camera to monitor driver attentiveness.
Tesla activates cabin-facing camera in bid to improve vehicle safety
Additionally, many cars are on the road without these driver assistance and safety features.
Distracted driving is going to occur whether a vehicle is equipped with modern technology or not.
Tesla and other automakers have brought their newest vehicles up to speed in the fight against distracted driving, and perhaps this study showed that warnings could and should come at varying rates to prevent anticipation from drivers.
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Tesla has reached a notable benchmark in its autonomous driving program after its Robotaxi fleet surpassed one million miles of unsupervised operation. The company made the announcement during its Cybercab event in Austin on September 3.
Tesla Vice President of AI Ashok Elluswamy told attendees he was happy to report the fleet had achieved one million miles of unsupervised Robotaxi operation as a testament to safety.
The new total marked a sharp increase from the 380,000 unsupervised miles Tesla disclosed during its second-quarter 2026 earnings update in late July.
In roughly six weeks, the company added about 620,000 miles. That acceleration followed Tesla’s decision to remove in-vehicle safety monitors from most of its operations outside the San Francisco Bay Area.
Credit: Tesla
Tesla first launched Robotaxi service in Austin in June 2025 with safety drivers present. It later began fully unsupervised rides and expanded into Dallas, Houston, Miami, Orlando, and Tampa. The San Francisco Bay Area remains the exception, where a safety monitor still rides in the vehicle under California permitting rules.
The company has not released a city-by-city breakdown of the one million unsupervised miles.
The milestone arrived as Tesla began offering public Cybercab rides in Austin. The purpose-built vehicle has no steering wheel or pedals and is designed only for autonomous ride-hailing. Production versions joined the existing fleet of modified Tesla vehicles already operating in the service.
Tesla’s unsupervised mileage is growing at a double-digit weekly rate according to earlier company comments, yet its fleet size remains modest compared with established competitors. Waymo has accumulated more than 200 million fully autonomous rider-only miles. Tesla has described its own unsupervised operations as having recorded zero notable incidents in the period leading up to the July update.
The one-million-mile figure reflects Tesla’s shift from supervised testing to broader driverless service in multiple states. It also highlights the company’s strategy of using both existing Model Y vehicles and the new Cybercab to scale its network.
Credit: Tesla
Whether the rapid recent growth continues will depend on further city expansions, regulatory approvals, and the performance of the purpose-built Cybercab in everyday paid rides. Tesla has not specified how many of the latest miles involved the new vehicle versus the rest of the fleet.
The announcement underscores Tesla’s progress toward a larger robotaxi network while illustrating the remaining gap in total autonomous experience relative to longer-operating rivals.
Tesla AI lead Ashok Elluswamy said this week that 24-hour Robotaxi service is close. Replying on X to a rider who wanted Cybercab trips all night, he wrote that the capability would arrive “next month or so” once “the next tech to merge on the v15 plan” is ready.
The comment landed on September 4, one day after Tesla opened public Cybercab rides in Austin. It is the clearest near-term timeline yet for overnight unsupervised operation. Tesla’s paid Robotaxi network currently runs from 6 a.m. to 10 p.m. seven days a week across Austin, Dallas, Houston, Miami, Orlando, and Tampa.
next month or so. the next tech to merge on the v15 plan will enable it.
— Ashok Elluswamy (@aelluswamy) September 4, 2026
That 16-hour window is shorter than the 6 a.m. to 2 a.m. schedule the company used for much of the prior year.
Elluswamy did not name the specific feature or say whether the change would apply first to purpose-built Cybercabs, the existing Model Y fleet, or both. He also offered no city-by-city rollout list. The link to Full Self-Driving v15 is nevertheless significant.
Tesla has described v15 as a step-change architecture with seven parallel improvement tracks and roughly ten times more parameters than earlier builds. Early versions of that software already operate on the Robotaxi fleet and contain about 40 percent of the planned gains.
By July 2026, the unsupervised fleet had logged more than 380,000 miles across six cities in two states with what the company called an impeccable safety record and no notable incidents caused by the vehicles themselves. Tesla has repeatedly argued that camera-based end-to-end neural networks, rather than extra sensors, are the core of the solution.
Overnight service would test that claim in lower-light conditions and would also raise vehicle utilization, a key variable for Robotaxi unit economics. The company has already begun using public Superchargers at night and is building dedicated Robotaxi charging sites.
Riders have asked why software must change if the cars already drive in the dark. The practical answer appears to be reliability and scale: Tesla has held back mass expansion until more of the v15 stack is merged, citing the need for higher confidence before putting thousands of unoccupied vehicles on streets around the clock.
If the next module arrives on the timetable Elluswamy sketched, 24-hour service could begin in October 2026 in at least some markets.
That would mark a shift from a daytime-bounded pilot to a service that can run whenever demand exists, including the late-night hours that have so far remained out of reach.
Tesla is beginning to roll out Full Self-Driving Supervised v14.3.9 with a new active safety layer that can take control even when the driver is operating the car manually.
Tesla AI said the software can activate FSD on the driver’s behalf when an imminent collision is detected and Automatic Emergency Braking may not be enough. It may also engage if the system detects heavy distraction or an accidental FSD disengagement.
FSD Supervised v14.3.9 starting to roll out shortly
This release includes a new active safety feature set: FSD Supervised can now activate on your behalf when an imminent collision is detected and Automatic Emergency Braking (AEB) may not be enough.
It may also engage if we…
— Tesla AI (@Tesla_AI) September 4, 2026
The capability is essentially Automatic Collision Evasion. However, unlike conventional AEB, which mainly applies the brakes in a straight line, this feature can use steering, braking, and acceleration together if the car calculates that stopping alone will not prevent impact and a safer path exists. The system may change lanes or move toward a shoulder when conditions allow, then continue driving after the immediate threat is handled rather than simply coming to a stop.
The intervention is meant as a last-resort safety net, not a replacement for attentive driving.
Tesla Full Self-Driving v14.3.7 early review: FSD saved me from an accident
Tesla’s own description still frames FSD as supervised assistance. Secondary reports on internal release notes say the feature can fire while the car is being driven manually if cabin-camera monitoring suggests the driver is not sufficiently attentive, such as reaching toward the back seat, or if FSD appears to have been turned off unintentionally.
After the emergency maneuver, the car is expected to alert the driver and request a return to manual control.
The safety case is straightforward. Many collisions happen in the last second because a driver is looking away, fumbles a control, or faces an obstacle that braking cannot fully solve. A system that can both recognize that AEB is insufficient and execute a coordinated evasive path can reduce those remaining high-severity events.
Re-engaging after accidental disengagement also addresses a practical failure mode: a small steering nudge that drops FSD at the worst moment. The advantage is a background safety net that uses the same vision stack already running in v14, instead of leaving the car solely to emergency braking once the driver is no longer in command.
The feature still depends on FSD being enabled and, according to reports, an active FSD purchase or subscription. It does not make the vehicle unsupervised. Drivers remain responsible, and Tesla has not published how often the system is expected to intervene or how it will handle false positives.
If the rollout is conservative and the false-alarm rate stays low, the update is a meaningful step: FSD is no longer only a feature the driver turns on. In the rare moments when disaster is already forming, it can step in.
Tesla crosses major Unsupervised Self-Driving milestone
Tesla Robotaxi will be a 24/7 service: here’s when
