<?xml version="1.0" encoding="utf-8" standalone="yes"?>
<rss version="2.0" xmlns:atom="http://www.w3.org/2005/Atom" xmlns:content="http://purl.org/rss/1.0/modules/content/">
  <channel>
    <title>🛡️ 回归防御 on Elon&#39;s AD Insight</title>
    <link>https://auto-driving-blog.vercel.app/tags/%EF%B8%8F-%E5%9B%9E%E5%BD%92%E9%98%B2%E5%BE%A1/</link>
    <description>Recent content in 🛡️ 回归防御 on Elon&#39;s AD Insight</description>
    <image>
      <title>Elon&#39;s AD Insight</title>
      <url>https://auto-driving-blog.vercel.app/images/share.png</url>
      <link>https://auto-driving-blog.vercel.app/images/share.png</link>
    </image>
    <generator>Hugo</generator>
    <language>zh-cn</language>
    <lastBuildDate>Sun, 19 Jul 2026 00:00:00 +0000</lastBuildDate>
    <atom:link href="https://auto-driving-blog.vercel.app/tags/%EF%B8%8F-%E5%9B%9E%E5%BD%92%E9%98%B2%E5%BE%A1/index.xml" rel="self" type="application/rss+xml" />
    <item>
      <title>场景理解与Failure Analysis：定位自动驾驶的边界</title>
      <link>https://auto-driving-blog.vercel.app/posts/knowledge/%E5%9C%BA%E6%99%AF%E7%90%86%E8%A7%A3%E4%B8%8Efailure-analysis/</link>
      <pubDate>Sun, 19 Jul 2026 00:00:00 +0000</pubDate>
      <guid>https://auto-driving-blog.vercel.app/posts/knowledge/%E5%9C%BA%E6%99%AF%E7%90%86%E8%A7%A3%E4%B8%8Efailure-analysis/</guid>
      <description>自动驾驶系统的每次路测都会产生大量bad case，如何从海量数据中系统化地分析模型失败的根因是一项关键工程能力。本文讲解failure analysis的方法论框架：场景分类taxonomy（感知失败/预测失败/规划失败/控制失败）、故障归因的ablation策略、场景还原与仿真复现、长尾场景的systematic discovery（基于聚类/基于异常检测/基于对抗搜索）、以及建立持续性的bad case追踪与回归防御体系。</description>
    </item>
  </channel>
</rss>
