Even experienced drivers can be forgiven for missing a roundabout exit once or twice, but a disoriented robotaxi in Arizona did it 36 times… in a row. While Waymo taxis are among the most advanced autonomous vehicles on the road today, in a video posted earlier this month on X, a confused AV appears to be quite literally stuck in a loop. Sorry I'm late, my WAYMO did 37 laps in the roundabout pic.twitter.com/GSR4sqChV2 And yet, even with blunders like these, there were more vehicles driving themselves this year than ever before. Once cordoned off to a few test tracks and small patches of land in Mountain View, AVs are now rearing their sensors-flapping heads in more than a dozen US cities. Tens of millions of drivers, cyclists, and pedestrians are learning how to coexist amongst these machines while their shared roads serve as real-world test-beds for full-scale AV deployment.

Previous work has shown that isolated non-canonical sentences with Object-before-Subject (OSV) order are initially harder to process than their canonical counterparts with Subject-before-Object (SOV) order. Although this difficulty diminishes with appropriate discourse context, the underlying cognitive factors responsible for alleviating processing challenges in OSV sentences remain a question. In this work, we test the hypothesis that dependency length minimization is a significant predictor of non-canonical (OSV) syntactic choices, especially when controlling for information status such as givenness and surprisal measures. We extract sentences from the Hindi-Urdu Treebank corpus (HUTB) that contain clearly-defined subjects and objects, systematically permute the preverbal constituents of those sentences, and deploy a classifier to distinguish between original corpus sentences and artificially generated alternatives. The classifier leverages various discourse-based and cognitive features, including dependency length, surprisal, and information status, to inform its predictions. Our results suggest that, although there exists a preference for minimizing dependency length in non-canonical corpus sentences amidst the generated variants, this factor does not significantly contribute in identifying corpus sentences above and beyond surprisal and givenness measures. Notably, discourse predictability emerges as the primary determinant of constituent-order preferences. These findings are further supported by human evaluations involving 44 native Hindi speakers. Overall, this work sheds light on the role of expectation adaptation in word-ordering decisions. We conclude by situating our results within the theories of discourse production and information locality.

U.S. safety regulators have pressured Tesla into recalling nearly 363,000 vehicles with its "Full Self-Driving" system because it can misbehave around intersections and doesn't always follow speed limits. The recall, part of part of a larger investigation by the National Highway Traffic Safety Administration into Tesla's automated driving systems, is the most serious action taken yet against the electric vehicle maker. It raises questions about CEO Elon Musk's claims that he can prove to regulators that cars equipped with "Full Self-Driving" are safer than humans, and that humans almost never have to touch the controls. Musk at one point had promised that a fleet of autonomous robotaxis would be in use in 2020. The latest action appears to push that development further into the future.

Washington, DC (CNN)Tesla (TSLA) is increasingly turning to machines rather than humans as it attempts to develop autonomous vehicles. As part of Tesla's plans to cut 10% of salaried staff, the company has laid off a significant number of its data annotation specialists. These specialists do grunt work that is critical to empowering artificial intelligence systems to handle complex tasks like driving safely down a city street. The layoffs were first reported by Bloomberg Tuesday and confirmed by CNN Business. Data annotation specialists use software tools to manually label objects in video clips collected from Tesla vehicles.

The government will soon release data on collisions involving vehicles with autonomous or partially automated driving systems that will likely single out Tesla for a disproportionately high number of such crashes. In coming days, the National Highway Traffic Safety Administration plans to issue figures it has been gathering for nearly a year. The agency said in a separate report last week that it had documented more than 200 crashes involving Teslas that were using Autopilot, "Full Self-Driving," Traffic-Aware Cruise Control or some other of the company's partially automated systems. Tesla's figure and its crash rate per 1,000 vehicles was substantially higher than the corresponding numbers for other automakers that provided such data to The Associated Press ahead of NHTSA's release. The number of Tesla collisions was revealed as part of a NHTSA investigation of Teslas on Autopilot that had crashed into emergency and other vehicles stopped along roadways.

The U.S. government has opened a formal investigation into Tesla's Autopilot partially automated driving system after a series of collisions with parked emergency vehicles. The investigation covers 765,000 vehicles, almost everything that Tesla has sold in the U.S. since the start of the 2014 model year. Of the crashes identified by the National Highway Traffic Safety Administration as part of the probe, 17 people were injured and one was killed. NHTSA says it has identified 11 crashes since 2018 in which Teslas on Autopilot or Traffic Aware Cruise Control have hit vehicles at scenes where first responders have used flashing lights, flares, an illuminated arrow board or cones warning of hazards. The agency announced the action Monday in a posting on its website.

At first glance, the forays Apple Inc., Google and other technology giants are making into the world of cars don't appear to be particularly lucrative. Building automobiles requires factories, equipment and an army of people to design and assemble large hunks of steel, plastic and glass. The world's top 10 carmakers had an operating margin of just 5.2% in 2020, a fraction of the 34% enjoyed by the tech industry's leaders, data compiled by Bloomberg show. But for Apple and other behemoths that are diving into self-driving tech or have grand plans for their own cars, that push isn't just about breaking into a new market -- it's about defending valuable turf. "Why are tech companies pushing into autonomous driving? Because they can, and because they have to," said Chris Gerdes, co-director of the Center for Automotive Research at Stanford University.

At first glance, the forays Apple Inc., Google and other technology giants are making into the world of cars don't appear to be particularly lucrative. Building automobiles requires factories, equipment and an army of people to design and assemble large hunks of steel, plastic and glass. The world's top 10 carmakers had an operating margin of just 5.2% in 2020, a fraction of the 34% enjoyed by the tech industry's leaders, data compiled by Bloomberg show. But for Apple and other behemoths that are diving into self-driving tech or have grand plans for their own cars, that push isn't just about breaking into a new market -- it's about defending valuable turf. "Why are tech companies pushing into autonomous driving? Because they can, and because they have to," said Chris Gerdes, co-director of the Center for Automotive Research at Stanford University.

NEW DELHI: Researchers at the Indian Institute of Technology (IIT) Madras are working on two projects that use artificial intelligence and data analytics to improve learning outcomes for students. They are analysing data from open-access learning portal NPTEL (National Programme on Technology Enhanced Learning) in order to reduce dropouts and improve understanding. NPTEL is a platform where teachers from the IITs put up videos on subjects generally related to engineering and science. Students can view them and get certifications for the courses they complete, at a nominal fee of Rs 1,000. In fact, engineering colleges have been mandated by AICTE (All India Council for Technical Education), the apex body for technical education in India, to cover 15-20% of their undergraduate syllabus through NPTEL.

Driving in a busy city, you have to get good at scrutinizing the body language of pedestrians. Your foot hovers somewhere between the gas and the brake, waiting for your brain to triangulate their intent: Is that one trying to cross the street, or just waiting for the bus? Still, a whole lot of the time you hit the brakes for nothing, ending up in a kind of dance with the pedestrian (you go, no you go, no YOU go). If you think that's frustrating, then you've never been a self-driving car. As human drivers slowly go extinct (and human pedestrians don't), autonomous vehicles will have to get better at decoding those unspoken intersection interactions.