Lane Following Autopilot with Keras and Tensorflow

Post by mhanus » Mon, Jan 23, 2017, 9:14 PM UTC

argonaut wrote: post #56

Two major errors: 1) This doesn't seem to be controlling overfitting on the right validation set. 2) There isn't a test set at all (separate from validation). Using Keras' "validation_split" parameter will just randomly select a validation set. This is not the right thing to do when your data is image sequences , because you will get essentially identical data in training and validation. Because of this, the numbers/…

Post by Drdrdrq » Mon, Jan 23, 2017, 9:25 PM UTC

mulmen wrote: post #48

grandalf wrote: post #31

Consider the fairly massive changes to the competitive landscape ushered in by the combined factors of self-driving and electric vehicles: - For liability reasons, most of the algorithmic IP will likely be open sourced. Either because it's required by regulators or because it's the most efficient way for car makers to socialize risk of an algorithmic failure. - Electric vehicles have many fewer moving parts, which me…

How do you see car makers differentiating their products in a world where all the parts including the frame are commoditized and the software is open source? Also, GM built an electric car back in the 90s called the EV-1. I wonder how much innovation was in that car vs the Volt.

Post by rsp1984 » Mon, Jan 23, 2017, 11:48 PM UTC

grandalf wrote: post #31

Consider the fairly massive changes to the competitive landscape ushered in by the combined factors of self-driving and electric vehicles: - For liability reasons, most of the algorithmic IP will likely be open sourced. Either because it's required by regulators or because it's the most efficient way for car makers to socialize risk of an algorithmic failure. - Electric vehicles have many fewer moving parts, which me…

Post by wroscoe » Tue, Jan 24, 2017, 12:44 AM UTC

agumonkey wrote: post #4

I remember seeing keras in commaai source. Who else uses it ?

Post by billyzs » Tue, Jan 24, 2017, 1:53 AM UTC

wroscoe wrote: post #66

billyzs wrote: post #51

Not to put down the OP's work (I think it's a great project), but I'm just wondering what advantages might an ML approach have over "traditional" CV algorithms. In a really well controlled environment lanes will be easy to detect, and computing the difference between the current heading and lane direction should be doable; maybe if we're talking about complex outdoor environments and poor sensors then ML would have a…

Traditional CV approaches are much easier to debug as well. I chose the ML approach with the assumption that it would be easier to build a robust autopilot that would work in many lighting conditions. Actually my short term goal is to get the car to drive around my block on the sidewalk (no lines). From my experience CV approaches have many parameters that need to be tuned specifically for each environment. While ML…