Tesla avoids California sales ban by removing'autopilot' from marketing Tesla will avoid a 30-day suspension of its dealer and manufacturer licenses in California, its biggest market, after the US electric vehicle maker stopped using the term "autopilot" in the marketing of its vehicles in the state. Tesla now uses the term "supervised" in references to its full self-driving technology and has stopped using "autopilot" entirely in its marketing in the state. State regulators said Tuesday that Tesla had stopped misleading drivers about the safety of its cars, and so the state will not suspend its state sales license for 30 days, as had been threatened. The decision by the California department of motor vehicles comes after CEO Elon Musk's electric vehicle company was found by an administrative law judge last year to have misled drivers about the ability of Tesla cars to drive themselves in its use of the terms "autopilot" and "full self-driving". In 2022, the DMV had accused Tesla of misleading consumers by using "autopilot" and "full self-driving" for its advanced driver-assistance features.

Samsung Galaxy Unpacked 2026 is Feb. 25 Valve's Steam Machine: Everything we know Tesla stops using'Autopilot' to promote its EVs in California The company has avoided a 30-day suspension by making the change. Tesla has stopped using the term "Autopilot" to sell its cars in California, thereby avoiding a 30-day sales and manufacturing ban in the state. If you'll recall, a California administrative law judge ruled in December that the automaker misled consumers by using the terms "Autopilot" and "Full Self-Driving." The judge recommended the suspension, but the California DMV gave Tesla 60 days to remove any untrue and misleading language in its marketing materials. In its announcement, the DMV said Tesla has taken corrective action and has stopped using Autopilot for marketing.

Is texting behind the wheel of a self-driving Tesla crazy? As self-driving cars get closer to reality, Tesla is striving to remain a big player. But is it sacrificing safety to stay in the game? For the past few weeks, Geoff Perlman, a 61-year-old technology executive from Texas, has been testing a free trial of Tesla's latest self-driving software as he travels around Austin. He's impressed: it can handle confusing lane adjustments and park itself in busy lots better, he thinks, than the average human.

Unmanned aerial vehicle (UAV) research requires the integration of cutting-edge technology into existing autopilot frameworks. This process can be arduous, requiring extensive resources, time, and detailed knowledge of the existing system. ROSplane is a lean, open-source fixed-wing autonomy stack built by researchers for researchers. It is designed to accelerate research by providing clearly defined interfaces with an easily modifiable framework. Powered by ROS 2, ROSplane allows for rapid integration of low or high-level control, path planning, or estimation algorithms. A focus on lean, easily understood code and extensive documentation lowers the barrier to entry for researchers. Recent developments to ROSplane improve its capacity to accelerate UAV research, including the transition from ROS 1 to ROS 2, enhanced estimation and control algorithms, increased modularity, and an improved aerodynamic modeling pipeline. This aerodynamic modeling pipeline significantly reduces the effort of transitioning from simulation to real-world testing without requiring expensive system identification or computational fluid dynamics tools. ROSplane's architecture reduces the effort required to integrate new research tools and methods, expediting hardware experimentation.

No one doubts that our future will feature more automation than our past or present. The question is how we get from here to there, and how we do so in a way that is good for humanity. Sometimes it seems the most direct route is to automate wherever possible, and to keep iterating until we get it right. Here's why that would be a mistake: imperfect automation is not a first step toward perfect automation, anymore than jumping halfway across a canyon is a first step toward jumping the full distance. Recognizing that the rim is out of reach, we may find better alternatives to leaping--for example, building a bridge, hiking the trail, or driving around the perimeter. This is exactly where we are with artificial intelligence. AI is not yet ready to jump the canyon, and it probably won't be in a meaningful sense for most of the next decade. Rather than asking AI to hurl itself over the abyss while hoping for the best, we should instead use AI's extraordinary and improving capabilities to build bridges.

It was a Monday afternoon in June 2023 when Rita Meier, 45, joined us for a video call. Meier told us about the last time she said goodbye to her husband, Stefan, five years earlier. He had been leaving their home near Lake Constance, Germany, heading for a trade fair in Milan. Meier recalled how he hesitated between taking his Tesla Model S or her BMW. He had never driven the Tesla that far before. He checked the route for charging stations along the way and ultimately decided to try it. Rita had a bad feeling. She stayed home with their three children, the youngest less than a year old. At 3.18pm on 10 May 2018, Stefan Meier lost control of his Model S on the A2 highway near the Monte Ceneri tunnel. "The collision with the guardrail launches the vehicle into the air, where it flips several times before landing," investigators would write later. The car came to rest more than 70 metres away, on the opposite side of the road, leaving a trail of wreckage. Several passersby tried to open the doors and rescue the driver, but they couldn't unlock the car. When they heard explosions and saw flames through the windows, they retreated. Even the firefighters, who arrived 20 minutes later, could do nothing but watch the Tesla burn.

It turns out Tesla's camera-vision-only approach to self-driving is no match for a Wile E. Coyote-style fake wall. Earlier this week, former NASA engineer and YouTuber Mark Rober posted a video where he tried to see if he could trick a Tesla Model Y using its Autopilot driver-assist function into driving through a Styrofoam wall disguised to look like part of the road in front of it. The Tesla hurls towards the wall at 40 mph and, rather than stopping, plows straight through it, leaving a giant hole. "It turns out my Tesla is less Road Runner, more Wile E. Coyote," Rober says as he inspects the damage on the front hood. The video, posted only a couple days ago, had racked up over 20 million views by Wednesday morning.

Elon Musk has long railed against the U.S. government, saying a crushing number of federal investigations and safety programs have stymied Tesla, his electric car company, and its efforts to create fleets of robotaxis and other self-driving automobiles. Now, Musk's close relationship with President Donald Trump means many of those federal headaches could vanish within weeks or months. On the potential chopping block: crash investigations into Tesla's partially automated vehicles; a Justice Department criminal probe examining whether Musk and Tesla have overstated their cars' self-driving capabilities; and a government mandate to report crash data on vehicles using technology like Tesla's Autopilot. The consequences of such actions could prove dire, say safety advocates who credit the federal investigations and recalls with saving lives. "Musk wants to run the Department of Transportation," said Missy Cummings, a former senior safety adviser at the National Highway Traffic Safety Administration. "I've lost count of the number of investigations that are underway with Tesla.

While I am only guessing that performance may degrade as a function of dataset scale, it is not hard to imagine advances in GANs which could make that degradation smaller, hence make the proposed method more useful. Further, even in an adversarial setting, it may be possible to guess what kind of inputs are relevant, or extend the method to few-shot or some hybrid approach. I am positively surprised that features of the student have comparable transferability to the teacher, I was concerned that some sort of overfitting to a teacher's decision boundary was possible, but this does not seem to be the case. While I agree with the authors that, in most cases, those releasing research models will not go out of their way to vaccinate them against zero-shot distillation, the proposed method could be used to (somewhat) copy and repurpose information stored in hardware model. Take for example Tesla's autopilot which uses several neural networks in it and is trained on tens of billions of images which are not available to the world.

Vertical take-off and landing (VTOL) unmanned aerial vehicles (UAVs) are versatile platforms widely used in applications such as surveillance, search and rescue, and urban air mobility. Despite their potential, the critical phases of take-off and landing in uncertain and dynamic environments pose significant safety challenges due to environmental uncertainties, sensor noise, and system-level interactions. This paper presents an integrated approach combining vision-based sensor fusion with System-Theoretic Process Analysis (STPA) to enhance the safety and robustness of VTOL UAV operations during take-off and landing. By incorporating fiducial markers, such as AprilTags, into the control architecture, and performing comprehensive hazard analysis, we identify unsafe control actions and propose mitigation strategies. Key contributions include developing the control structure with vision system capable of identifying a fiducial marker, multirotor controller and corresponding unsafe control actions and mitigation strategies. The proposed solution is expected to improve the reliability and safety of VTOL UAV operations, paving the way for resilient autonomous systems.