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<rss version="2.0" xmlns:atom="http://www.w3.org/2005/Atom"><channel><title>Imitating Driver Behavior with Generative Adversarial Networks · HackerPapers discussions</title><link>https://hackerpaper.kaipfister.de/p/1701.06699/</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/1701.06699/feed.xml" rel="self" type="application/rss+xml" /><item><title>Imitating Driver Behavior with Generative Adversarial Networks — mentioned by strangecosmos</title><link>https://news.ycombinator.com/item?id=19016128</link><guid isPermaLink="false">urn:hackerpapers:mention:1701.06699:19016128</guid><pubDate>Mon, 28 Jan 2019 09:09:38 GMT</pubDate><description>&lt;p&gt;Key excerpt: “Tesla’s cars collect so much camera and other sensor data as they drive around, even when Autopilot isn’t turned on, that the Autopilot team can examine what traditional human driving looks like in various driving scenarios and mimic it, said the person familiar with the system. It uses this information as an additional factor to plan how a car will drive in specific situations—for example, how to steer a curve on a road or avoid an object. Such an approach has its limits, of course: behavior cloning, as the method is sometimes called… But Tesla’s engineers believe that by…&lt;/p&gt;&lt;p&gt;&lt;a href="https://hackerpaper.kaipfister.de/p/1701.06699/"&gt;Paper and discussion history&lt;/a&gt;. A mention is not an endorsement.&lt;/p&gt;</description><category>cs.AI</category></item></channel></rss>
