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Jan 15, 2017, 11:16 PM
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Autonomous car navigating dirt road with shadows


Self Driving BASE 1 Rover - MountainTrack (5 min 1 sec)



This nugget caught the lion kingdom's attention last month. It seems to be doing the impossible: navigating a mountain dirt road with lots of shadows using only machine vision. For the 1st time in 11 years of UAV blog posts, there was no mention of GPS even being offered in the product. Of course, it didn't stay to the right like a running pacer has to do.


Knowing a convolutional neural network is just a storage & recall mechanism for images, it could have been done by training the neural network on many manual drives through the same 5 minute path. There's enough detail besides the path for the rover to ignore the shadows, determine where it is & steer to prerecorded headings. How the rover determined where it was in relation to the center of the path would be another trick. It could be a neural network trained on a bunch of offset camera views or it could be simple optical flow.


The key is the vertical detail from the trees & mountainside adding extra edges besides the sides of the path. It wouldn't do well on a bare path shaded by trees outside the camera view.


In other news, keeping with the modern slogan that the programming language is the computer, Tesla hired the guy who invented Swift & invented the LLVM as the VP of autopilot. 20 years ago, they would have hired a networking expert & 10 years ago, they would have hired a video expert. It would make sense for Tesla to invent its own programming language, since that's what everyone else is doing. Perhaps the world needs a programming language for neural networks instead of a library.


The LLVM has a good chance of replacing GCC. GCC has enjoyed a 3 decade reign, but ever since running it for the 1st time on an HP, it was slower than any commercial compiler.
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