Lane Following Autopilot with Keras and Tensorflow
wroscoe.github.io
Lane Following Autopilot with Keras and Tensorflow
1–10 of 78 posts
Re: Lane Following Autopilot with Keras and Tensorflow
#2This is nice work, but anyone wanting to try it for themselves should be warned that you shouldn't unpickle data received from an untrusted source.
Re: Lane Following Autopilot with Keras and Tensorflow
#3Nicely done! But I'm assuming that this is more of an exercise rather than a real-world application of ML? I say this because the task of keeping a car between two lines is trivially done using control algorithms. Of course, the CV part -- "seeing" the lines -- requires some form of ML to work in the real world.
Re: Lane Following Autopilot with Keras and Tensorflow
#4I remember seeing keras in commaai source. Who else uses it ?
Re: Lane Following Autopilot with Keras and Tensorflow
#5Nicely done! But I'm assuming that this is more of an exercise rather than a real-world application of ML? I say this because the task of keeping a car between two lines is trivially done using control algorithms. Of course, the CV part -- "seeing" the lines -- requires some form of ML to work in the real world.
Obviously, "Lane Following Autopilot using my brain and controls theory" would not make it to the top of HN. Welcome to the new era where Tensorflow replaces Lyapunov and ML spares you the need of understanding hard problems... until you need guarantees and safety... but but it's ok let's add more data.
Re: Lane Following Autopilot with Keras and Tensorflow
#6Nicely done! But I'm assuming that this is more of an exercise rather than a real-world application of ML? I say this because the task of keeping a car between two lines is trivially done using control algorithms. Of course, the CV part -- "seeing" the lines -- requires some form of ML to work in the real world.
> trivially done using control algorithms
Is it really trivial? Honest question... Which control algorithms are you speaking of?
Re: Lane Following Autopilot with Keras and Tensorflow
#7This is nice work, but anyone wanting to try it for themselves should be warned that you shouldn't unpickle data received from an untrusted source. https://blog.nelhage.com/2011/03/exploiting-pickle/
glad to see this as the first comment
Re: Lane Following Autopilot with Keras and Tensorflow
#8Nicely done! But I'm assuming that this is more of an exercise rather than a real-world application of ML? I say this because the task of keeping a car between two lines is trivially done using control algorithms. Of course, the CV part -- "seeing" the lines -- requires some form of ML to work in the real world.
> trivially done using control algorithms Is it really trivial? Honest question... Which control algorithms are you speaking of?
umm...say, a PID loop?
Re: Lane Following Autopilot with Keras and Tensorflow
#9X is in the range 0, 255. They don't show code converting it to a much saner range for the network they've chosen. Is the full source somewhere?
Re: Lane Following Autopilot with Keras and Tensorflow
#10Nicely done! But I'm assuming that this is more of an exercise rather than a real-world application of ML? I say this because the task of keeping a car between two lines is trivially done using control algorithms. Of course, the CV part -- "seeing" the lines -- requires some form of ML to work in the real world.
> trivially done using control algorithms Is it really trivial? Honest question... Which control algorithms are you speaking of?
As u/gumby said, I was thinking of a PID controller. Basically, the car would continuously measure how far away it is from the line and compare that with the "expected" (computed) value. Based on the error between the two values, the controller would adjust some variable (e.g., wheel angle).
Computing how much of an adjustment is required is where the PID part comes in. The controller uses the Derivative (rate of change) of the error as well the the Integral of the error to improve its estimate. These two values can intuitively be thought of as the predicted error and history of the error, respectively.