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<rss version="2.0" xmlns:atom="http://www.w3.org/2005/Atom"><channel><title>Autonomous Racing using Learning Model Predictive Control · HackerPapers discussions</title><link>https://hackerpaper.kaipfister.de/p/1610.06534/</link><description>Collected HN comments and submissions linking this paper.</description><language>en</language><lastBuildDate>Mon, 02 Nov 2026 11:16:22 GMT</lastBuildDate><atom:link href="https://hackerpaper.kaipfister.de/p/1610.06534/feed.xml" rel="self" type="application/rss+xml" /><item><title>Autonomous Racing using Learning Model Predictive Control — mentioned by a1k0n</title><link>https://news.ycombinator.com/item?id=18461167</link><guid isPermaLink="false">urn:hackerpapers:mention:1610.06534:18461167</guid><pubDate>Thu, 15 Nov 2018 17:19:20 GMT</pubDate><description>&lt;p&gt;I&amp;#x27;m aware of one paper which tries to fit a local polynomial model to the lateral dynamics, and they have a different model for different positions on the track (it requires really good localization to work): https://arxiv.org/abs/1610.06534 I had some of the vehicle parameters as part of my EKF for a while, but it led to some instabilities in the control. The main problem is that it&amp;#x27;s really hard to know what the car&amp;#x27;s instantaneous lateral velocity is. I think the best you can do is check the difference of velocity*gyro yaw rate and lateral accelerometer measurement to get a noisy lateral…&lt;/p&gt;&lt;p&gt;&lt;a href="https://hackerpaper.kaipfister.de/p/1610.06534/"&gt;Paper and discussion history&lt;/a&gt;. A mention is not an endorsement.&lt;/p&gt;</description><category>cs.LG</category></item></channel></rss>
