Quote:
Originally Posted by Jared
I have a big list of about 3,000 or so points that I iterate through.
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You probably already know this (and implemented it) but it doesn't hurt to say this for those reading along. (If you take a data science class, you'll learn how to deal with massive amounts of data efficiently.) Anyways, say you have 2xn matrix of points. Instead of stepping through each point in the matrix, that is, line by line, make it a linear algebra problem. Compute on the entire matrix instead of one row at a time. It greatly reduces runtime.
Kevin, +1 for using gradient descent. I love that algorithm. Noob question: why not use the normal equation: (XX')^-1 X'y to solve for you thetas?
I am too lazy to add in your quote the clean way, but...
"The result is a bunch of straight lines, which appears smooth globally, and has smooth transitions."
I don't think I understand how mathematically this produces a smooth transition. I guess this doesn't matter if your outputted paths are smooth. That's a really clever approach to the problem.
You mentioned this was part of you PhD thesis (first of all, that's really impressive. Some of my lab mates are working on theirs and it looks incredibly stressful). For your project of "developing a controller for an Autonomous Car," are you given a path already? I'm curious because I wrote a pathfinding algorithm (really just an adaption of a*) over the summer and I'm taking the path generated from that and putting it into my program that generates velocity profiles of the left and right side for the robot. I am just wondering if you're doing a similar thing.
"If the algorithm does not converge, the program will simply never finish, so it is pretty easy to identify." Luckily for an autonomous period, everything will be calculated long before the match starts, unless you develop a new path moments before the match, so you'll have ample time investigate why it fails to converge.