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Lane Following Autopilot with Keras and Tensorflow

wroscoe.github.io

71–78 of 78 posts

Re: Lane Following Autopilot with Keras and Tensorflow

#71
post #39

Earlier quoted context omitted.

Rather than demeaning someone's effort, learn why specific methods are considered appropriate/state-of-the-art when solving certain problems. This is an unbelievably wrong comment. All the Lyapunov and traditional Process control theory in the world won't help you solve autonomous driving. Also regarding "Guarantees and Safety" they don't magically appear out of thin air when you use traditional process control espec…

Obviously, my message was slightly provocative, deep learning methods and classical controls (which by the way are able to quantify robustness to plant uncertainties and noisy signals) are all very useful but shall be used in combination. End-to-end techniques that bundle perception, planning and control in an opaque net are fun to play with (like in this article), it just very sad to see people believing this produc…

I agree with you in the sense that if a known and reliable way to map knowledge and information from one domain to another (e.g. from desired trajectory + perceived current position to steering inputs), I'd much prefer that than black box ish neural nets. Neural nets aren't meant to be the silver bullet.

But in this case though, any kind of state space control also requires rather precise knowledge of the physical laws that govern the dynamics of the vehicles. When such information is not available, can neural nets do a decent job at mimicking an analytical control algorithm? I think that's an interesting problem worth exploring.

Re: Lane Following Autopilot with Keras and Tensorflow

#72
post #61

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/…

Keras uses the end of the data set as validation, and only randomizes it if the "shuffle" argument is set to True [1]. [1]: https://keras.io/getting-started/faq/#how-is-the-validation-...

Except the second half of the data is the flipped of the first half (X = np.concatenate([X, X_flipped]))

Re: Lane Following Autopilot with Keras and Tensorflow

#73
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…

My first thought was something that used several PID mechanisms.

Re: Lane Following Autopilot with Keras and Tensorflow

#74
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…

Because ML approaches can adapt to different environments like a forest trail. While this can probably be achieved with OpenCV, this just feels natural: https://www.youtube.com/watch?v=umRdt3zGgpU

Re: Lane Following Autopilot with Keras and Tensorflow

#75
post #65

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…

I like your analysis here but how is this related to the article?

This DIY lane following algorithm made me think about it, and after reading Elon Musk's tweet about retrofitting I'd been thinking about the market and figured this thread was as good a place as any to deposit my thoughts.

Re: Lane Following Autopilot with Keras and Tensorflow

#76
post #61

Earlier quoted context omitted.

Keras uses the end of the data set as validation, and only randomizes it if the "shuffle" argument is set to True [1]. [1]: https://keras.io/getting-started/faq/#how-is-the-validation-...

Except the second half of the data is the flipped of the first half (X = np.concatenate([X, X_flipped]))

Well shit. Thanks for pointing that out. I'll revise.
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