11–20 of 38 posts
Earlier quoted context omitted.
What aspect of "deep learning" are you concerned about? Would you prefer obstruction detection be done with shallow NNs? Or a linear classifier?
With something that measures depth and can profile the ground. Probably a LIDAR.
That's scary. Using deep learning to recognize "obstruction" using cameras. Discriminate "car", "bike", "pedestrian", and "bus", sure. Recognize signs and traffic lights, fine. But "obstruction"? No. That's a geometry problem. If it's not flat road, it's an obstacle. Deep learning is for deciding what kind of obstacle. Because deep learning just isn't that good. It's going to be badly wrong a few percent of the time.…
If DNNs are unreliable, why is it ok to use them to discriminate a car from a pedestrian? The planning that you do in order to avoid hitting a car is very different from what you need in order to avoid a pedestrian. If you erroneously treat a pedestrian as a car, you are very likely to cause a tragedy.
Earlier quoted context omitted.
If DNNs are unreliable, why is it ok to use them to discriminate a car from a pedestrian? The planning that you do in order to avoid hitting a car is very different from what you need in order to avoid a pedestrian. If you erroneously treat a pedestrian as a car, you are very likely to cause a tragedy.
As a non-driver, I'm curious, how different is the planning? Isn't it just a matter of size?
If it's a car, you will NOT do whatever is necessary to avoid it (I'm speaking about low speed crashes here). You will prefer a "soft" crash that doing certain super dangerous maneuvers. Like, you will brake and "accept" a soft crash instead of doing a ultra heavy turn to the sidewalk.
That's scary. Using deep learning to recognize "obstruction" using cameras. Discriminate "car", "bike", "pedestrian", and "bus", sure. Recognize signs and traffic lights, fine. But "obstruction"? No. That's a geometry problem. If it's not flat road, it's an obstacle. Deep learning is for deciding what kind of obstacle. Because deep learning just isn't that good. It's going to be badly wrong a few percent of the time.…
That quote jumped off the page for me as well. This sounds like the start of a new blog post: Falsehoods Programmers Believe About Driving
You can throw a few more in there.
- "Pedestrians will use crosswalks"
- "Pedestrians will be walking"
- "Traffic signals will always illuminate one of the three lamps"
- "Traffic signals have three lamps"
- "Lane markings are either white or yellow"
- "Lane markings exist"
- "There are lanes"
Until there's a breakthrough in our ability to understand what a NN "thinks", I don't think we should place too much trust in them.
Or, I guess, we can wait and see if the imperfect NNs are doing better than humans in the long run. From what I read, that may be true today only in ideal circumstances (sunny, dry, maintained roads, etc.)
No comment on why this is the case?
Or are these augmentations default-on options in deep learning frameworks?
That's scary. Using deep learning to recognize "obstruction" using cameras. Discriminate "car", "bike", "pedestrian", and "bus", sure. Recognize signs and traffic lights, fine. But "obstruction"? No. That's a geometry problem. If it's not flat road, it's an obstacle. Deep learning is for deciding what kind of obstacle. Because deep learning just isn't that good. It's going to be badly wrong a few percent of the time.…
> "The car will almost never be on the left side of the road, and the cameras will never flip angles, so training on flipped data forces the network to overgeneralize to situations it will never see." What could possibly go wrong? That quote jumped off the page for me as well. This sounds like the start of a new blog post: Falsehoods Programmers Believe About Driving You can throw a few more in there. - "Pedestrians…
We've already been down this road with Uber; apparently that's not an issue.
https://www.regit.cars/car-news/how-to-pass-horses-on-the-ro...
Earlier quoted context omitted.
If DNNs are unreliable, why is it ok to use them to discriminate a car from a pedestrian? The planning that you do in order to avoid hitting a car is very different from what you need in order to avoid a pedestrian. If you erroneously treat a pedestrian as a car, you are very likely to cause a tragedy.
As a non-driver, I'm curious, how different is the planning? Isn't it just a matter of size?