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What does that data give you? Raw sensor data + steering angle + acceleration/braking is not particularly useful in of itself. Nobody is trying to shove large a
by argonaut 10y ago
What does that data give you? Raw sensor data + steering angle + acceleration/braking is not particularly useful in of itself. Nobody is trying to shove large amounts of data through a blackbox model and directly trying to predict car controls from images. This is not MobileEye's approach and almost certainly isn't Tesla's approach. The bottleneck is data that is decomposed and labeled in greater detail (lane markings, obstacles, bicyclist hand signs, etc) - and you need human annotators for that.
- astrange 10y agoGeohot is pretty much claiming to do that! http://www.theverge.com/2016/6/6/11866868/comma-ai-george-hotz-interview-self-driving-cars http://www.theverge.com/2016/6/6/11866868/comma-ai-george-ho...
- argonaut 10y agoSure, some people are exploring the approach. This does not mean it works.
- Meegul 10y agoI recommend watching this [0] ted talk on the subject. In it, you'll see that Google, last year, already had solved bicyclist hand signs, lane markings, unforeseen obstacles, etc. And they did it __without__ human annotators. Just some straight up convolution neural networks. [0]: https://www.ted.com/talks/chris_urmson_how_a_driverless_car_sees_the_road?language=en https://www.ted.com/talks/chris_urmson_how_a_driverless_car_...
- argonaut 10y agoYou're mistaken. Convolutional neural networks require annotated data. It's a TED talk; they're obviously not going to mention the very boring details of their annotation protocols.