MIT researchers have developed a new imaging system that could allow autonomous vehicles to see through dense fog, writes Andrew Liszewski of Gizmodo. The laser-based system, which used a new processing algorithm, was able “to clearly see objects 21 centimeters further away than human eyes could discern,” Liszewski writes.
Research scientist Bryan Reimer speaks to WBUR about the ramifications for the autonomous vehicle industry in response to the recent fatality caused by a self-driving Uber. “As we look forward…we need to work together in ways through policy, technology development, advocacy, to set a pathway to safety,” Reimer says.
Researchers in CSAIL are developing a steering program for drones that allows them to process uncertainty and avoid hitting objects while flying autonomously. Called Nanomap, the drone uses depth measurements to determine the safest path. “This technique creates an on the fly map that lets the drone handle uncertainty as opposed to being ready in every situation,” writes John Biggs for TechCrunch.
Skydio, an autonomous drone startup founded by a group of MIT alumni, has showcased a new drone that can lock-on, follow and record its subject, writes Lucas Matney of TechCrunch. One possible use for the device is to “launch the drone, lock onto yourself, and ski down a mountain while the R1 tracked you to the bottom while capturing 4K footage,” Matney explains.
CSAIL researchers have developed a new navigation method that allows drones to process less data, have faster reaction times, and dodge obstacles without creating a map of the environment they’re in, writes James Vincent of The Verge. “Because we’re not taking hundreds of measurements and fusing them together, it’s really fast,” said graduate student Peter Florence.
A drone navigation system developed by CSAIL researchers doesn’t rely on intricate maps that show the location of obstacles, but adjusts for uncertainties, reports Martin Finucane of The Boston Globe. The system could be used in “in fields from search-and-rescue and defense to package delivery,” notes Finucane.
CSAIL researchers have developed a mapping system for autonomous drones, writes Andrew Tarantola of Endgadet. Using depth-sensing technology to measure their immediate surroundings, the drone can “understand generally where it is in relation to obstacles and anticipate how it will need to change course to avoid them,” Tarantola explains.
Graduate student Achuta Kadambi speaks with the BBC’s Gareth Mitchell about the new depth sensors he and his colleagues developed that could eventually be used in self-driving cars. “This new approach is able to obtain very high-quality positioning of objects that surround a robot,” Kadambi explains.
MIT researchers have developed an autonomous tricycle that can transport people and packages, writes David Silverberg for Motherboard. “The innovation created by MIT is dubbed PEV (Persuasive Electric Vehicle), and sports a 250W electric motor and 10Ah battery pack. It can run on 25 miles per charge with a top speed of 20 miles per hour.”
Reuters Video visits MIT to learn more about how researchers have developed a new robot, dubbed Jackal, which can navigate pedestrian traffic. Graduate student Michael Everett explains that the robot was designed to operate, “just like people do, so [it] fits in with the flow of traffic.”
Fortune reporter David Morris writes that MIT researchers have tricked an artificial intelligence system into thinking that a photo of a machine gun was a helicopter. Morris explains that, “the research points towards potential vulnerabilities in the systems behind technology like self-driving cars, automated security screening systems, or facial-recognition tools.”
In an article for The Wall Street Journal, Visiting Lecturer Irving Wladawsky-Berger spotlights MIT’s AI and the Future of Work Conference. Wladawsky-Berger writes that participants, “generally agreed that AI will have a major impact on jobs and the very nature of work. But, for the most part, they viewed AI as mostly augmenting rather than replacing human capabilities.”
Abigail Beall of New Scientist writes that MIT researchers have developed an algorithm that can trick an AI system, highlighting potential weaknesses in new image-recognition technologies used in everything from self-driving cars to facial recognition systems. “If a driverless car failed to spot a pedestrian or a security camera misidentified a gun the consequences could be incredibly serious.”
CSAIL researchers have tricked a machine-learning algorithm into misidentifying an object, reports Louise Matsakis for Wired. The research, “demonstrates that attackers could potentially create adversarial examples that can trip up commercial AI systems,” explains Matsakis.
CSAIL researchers have discovered that some traffic jams are caused by tailgating, writes Thomas Tamblyn for HuffPost. Maintaining an equal distance in front of and behind a vehicle, “could have a dramatic effect in reducing travel time and fuel consumption without having to build more roads or make other changes to infrastructure,” explains Prof. Berthold Horn.