Longtime Slashdot reader AmiMoJo shares a report from Electrek: Waymo co-CEO Dmitri Dolgov laid out the clearest technical case yet for why cameras alone can't take a self-driving system to full autonomy, arguing that "weak sensing" hits a safety ceiling long before it reaches superhuman performance. [...] Dolgov made the comments in a talk at Y Combinator's Startup School, walking through the lessons Waymo has learned building its driver over close to two decades. He put the sensor question on the table plainly: "there's been a long-standing debate about what kind of sensors do you actually need for autonomous driving." His answer draws the line that camera-only advocates tend to skip right past. "Humans of course can drive with just eyes, so there's that proof of existence," he said. "If the goal were to just approximately match human performance or to build an assist product, that's a very reasonable way to go." Then the catch. If you're targeting full autonomy and strongly superhuman performance, he said, "you find that weak sensing just leads to a safety curve that flattens out way too early." Dolgov said that cameras, lidar, and radar are complementary rather than redundant: "These different sensing modalities, they're not backups to each other," and combining them produces a view "vastly superior to what you get with any one sensor." He said multiple sensor types also protect against physical failures, such as a leaf or branch blocking a camera, while helping Waymo climb the "exponential ladder of nines" required for fully driverless safety. Dolgov warned that camera-only systems may improve quickly before plateauing "way before the performance that is required by your product." He also pushed back on the cost argument against lidar, calling it "a number that has a fairly short shelf life" as hardware costs continue to fall.

Read more of this story at Slashdot.

To design invisible drones, researchers have tried camouflage, transparent materials and light-bending optical systems. But engineers at Northwestern University used "motion blur," which an announcement from the school notes is the effect that makes fast-spinning fans seem to disappear. "The drone spins up to 25 times per second, which is too fast for the human eye to see clearly. While it isn't completely invisible, it morphs into a ghostly smudge that seamlessly blends into the background... " To design the drone, the Northwestern team first used a computational model to generate roughly 20,000 drone configurations capable of stable flight. Then, they used artificial intelligence (AI) and optimization algorithms to repeatedly rearrange the drones' major components, including a motor, propeller, circuit board, counterweight and batteries. After sifting through many different configurations, the algorithms determined the ideal placement of the drone's components to minimize its visibility from virtually every viewing angle while allowing for stable flight. After selecting promising candidates, the engineers simulated each drone spinning in flight and overlaid those images a hundred real-world backgrounds. Then, they used a perception model that approximates human vision to determine how noticeable each design appeared. Designs that blended into their surroundings received lower visibility scores. The team selected the 500 lowest-scoring designs and applied the optimization algorithm, which repeatedly adjusted the positions of components to further minimize those scores. "The design process was fully automated," [said Northwestern's associate CS professor Michael Rubenstein, who led the work]. "Then, when we were confident that a drone met all our criteria, we built it." They've named the drone "Phantom Twist"," and plan to design future iterations with more transparent materials or quieter propulsion to make it even less noticeable. The Northwestern team presented the work on July 16 at Robotics: Science and Systems 2026 in Sydney, Australia... Thanks to long-time Slashdot reader fahrbot-bot for sharing the news.

Read more of this story at Slashdot.

Electric vehicles have historically been "notorious" for losing their resale value, reports CNBC. But this year prices for used EVs in the U.S. "are up 5.1% from January to June 2026, according to a Recurrent analysis published this month." The trend continued into the second half of the year: Prices are up 7% year to date through mid-July, it said. Recurrent compared EVs according to the same make and model year across 108 combinations and weighted price growth according to inventory volume. "Used EVs are appreciating, which almost never happens," according to an e-mailed Recurrent statement about the analysis. Other auto analysts found a similar trend... Price growth for used EVs has been broad-based, said Stephanie Valdez Streaty, the director of industry insights at Cox Automotive, a market research firm. Twenty-one of the 25 used EV models with the highest sales volume increased in price between January and June this year, she said... [T]he price growth for used EVs this year has been all the more surprising because it has happened despite a high supply of used EVs hitting the market — which, all else being equal, would generally cause prices to fall, experts said... There are several factors juicing consumer demand for used EVs, experts said. Among them are high gasoline prices due to the Iran war, which have pushed more consumers to consider fuel-efficient options, experts said... Overall affordability is another big factor, against a backdrop of inflation that has remained above policymakers' target of 2% for five or so years, auto experts said. The dynamic has pushed more consumers toward the used car market more broadly. Two interesting statistics from the article: "Used EV sales were up 20% in June versus a year earlier, while sales of new EVs were down about 28%, according to Cox Automotive data." New EVs accounted for 5.4% of total new-vehicle sales in June, while the market share for used EVs was just 2.4%, according to Cox Automotive data.

Read more of this story at Slashdot.