Spannender Beitrag heute über eine technische Alternativlösung für Karten für das Autonome Fahren.
Companies like Google only test their fleets in major cities where they've spent countless hours meticulously labeling the exact 3D positions of lanes, curbs, off-ramps and stop signs.
Indeed, if you live along the millions of miles of U.S. roads that are unpaved, unlit or unreliably marked, you're out of luck. Such streets are often much more complicated to map, and get a lot less traffic, so companies are unlikely to develop 3D maps for them anytime soon. From California's Mojave Desert to Vermont's White Mountains, there are huge swaths of America that self-driving cars simply aren't ready for.
One way around this is to create systems advanced enough to navigate without these maps. In an important first step, a team from MIT's Computer Science and Artificial Intelligence Laboratory (CSAIL) has developed MapLite, a new framework that allows self-driving cars to drive on roads they've never been on before without 3D maps.
MapLite combines simple GPS data that you'd find on Google Maps with a series of sensors that observe the road conditions. In tandem, these two elements allowed the team to autonomously drive on multiple unpaved country roads in Devens, Massachusetts, and reliably detect the road more than 100 feet in advance. (As part of a collaboration with the Toyota Research Institute, researchers used a Toyota Prius that they outfitted with a range of LIDAR and IMU sensors.)
"The reason this kind of 'map-less' approach hasn't really been done before is because it is generally much harder to reach the same accuracy and reliability as with detailed maps," says CSAIL graduate student Teddy Ort, who was a lead author on a related paper. "A system like this that can navigate just with on-board sensors shows the potential of self-driving cars being able to actually handle roads beyond the small number that tech companies have mapped."
