Autonomous Car Learns To Powerslide Into Parking Spot
IEEE SpectrumCited 3× on HN, 2010–2017
demo · general · 2010–2017 on HN
A short IEEE Spectrum clip of Stanford's autonomous car Junior learning to powerslide into a parking spot. It shows several approaches to the maneuver.
Cited as a simple example of automated driving maneuvers beyond what human drivers can do.
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3 comments from Hacker News
it was probably trying to drift park a la stanford's 'Junior'. ▶ this video But that trademark Volvo understeer doesn't like to powerslide.
Exactly. And I can see a new sport/hobby of semi-automated driving where you can perform maneuvers that are difficult or impossible for the best humans today. ▶ this video is a simple example; imagine what you could do with a high-performance vehicle.
As others have pointed out, a lot of things that "work" stop being considered AI. But here are a few recent advances off the top of my head: The DARPA Grand Challenge. Robot cars that drive themselves. link ▶ this video Google's Machine Translation system. Far from perfect, but it can usually do something reasonable between over 40 different pairs of languages. They added Latin on Thursday. CMU researchers have developed a classifier that can look at your MRI scans and tell what you're thinking. (Still in its infancy, but still.) link People working in my research lab are working to automatically reconstruct words from the ProtoAustronesian language using just the modern words. Our results are typically extraordinarily close to what historical linguists have done by hand. People in a neighboring lab are working on a system that can automatically distinguish between a nuclear detonation and an earthquake as part of the Test Ban Treaty Organization. They're making good progress. Take a look at PhotoSynth: link which can automatically reconstruct 3D scenes from disparate photos taken from different cameras with no prior information needed to stitch them together. link The face detection that is now standard on many cameras was one of the great problems in computer vision.