https://auto-driving-blog.vercel.app/tags/%E5%BC%80%E7%8E%AF/ Recent content in 开环 on Elon's AD Insight https://auto-driving-blog.vercel.app/images/share.png https://auto-driving-blog.vercel.app/images/share.png Hugo zh-cn Sun, 19 Jul 2026 00:00:00 +0000 https://auto-driving-blog.vercel.app/posts/knowledge/%E5%BC%80%E7%8E%AF%E8%AF%84%E6%B5%8Bvs%E9%97%AD%E7%8E%AF%E8%AF%84%E6%B5%8B%E6%B7%B1%E5%BA%A6%E8%A7%A3%E6%9E%90/ Sun, 19 Jul 2026 00:00:00 +0000 https://auto-driving-blog.vercel.app/posts/knowledge/%E5%BC%80%E7%8E%AF%E8%AF%84%E6%B5%8Bvs%E9%97%AD%E7%8E%AF%E8%AF%84%E6%B5%8B%E6%B7%B1%E5%BA%A6%E8%A7%A3%E6%9E%90/ <h2 id="一引言">一、引言</h2> <p>在自动驾驶规划算法的研发流程中，评估方法的选择直接影响研究者对模型能力的判断。目前两类主流的评估范式——开环评测（Open-loop Evaluation）与闭环评测（Closed-loop Evaluation）——各有理论基础和适用范围，但二者之间存在显著的鸿沟。</p> <p>NAVSIM 论文《Can We Also Drive in Closed-loop?》通过系统性实证研究揭示了这一鸿沟的严重程度：开环评测中的 L2 误差与闭环驾驶质量之间的 Pearson 相关系数仅约 0.3。这意味着一个在开环评测中排名第一的模型，在真实闭环部署中可能完全不合格。越来越多的研究者开始呼吁抛弃&quot;开环迷信&quot;，转向更可靠的闭环评估或混合评估策略。</p> <p>本文将从原理、方法、指标和实证分析四个维度，全面解析这两种评测范式，帮助研究者在论文写作和模型选择中做出更准确的判断。</p> <h2 id="二开环评测">二、开环评测</h2> <h3 id="21-基本定义">2.1 基本定义</h3> <p>开环评测的核心流程如下：</p> <ol> <li>从已录制的驾驶数据集中取出一个片段</li> <li>将历史观测数据（图像、点云、高精地图等）输入规划模型</li> <li>模型输出未来一段时间的 EGO 预测轨迹</li> <li>将预测轨迹与人类驾驶员在 log 中的实际轨迹（ground truth）进行比较</li> <li>计算误差指标</li> </ol> <p>关键特征在于：<strong>评测过程中，模型不会接收到自身决策的反馈</strong>。每一步的输入都来自真实 log 数据，与模型的预测无关。</p> <h3 id="22-数据流示意">2.2 数据流示意</h3> <div class="goat svg-container "> <svg xmlns="http://www.w3.org/2000/svg" font-family="Menlo,Lucida Console,monospace" viewBox="0 0 384 57" > <g transform='translate(8,16)'> <text text-anchor='middle' x='0' y='4' fill='currentColor' style='font-size:1em'>t</text> <text text-anchor='middle' x='0' y='20' fill='currentColor' style='font-size:1em'>t</text> <text text-anchor='middle' x='0' y='36' fill='currentColor' style='font-size:1em'>t</text> <text text-anchor='middle' x='8' y='4' fill='currentColor' style='font-size:1em'>=</text> <text text-anchor='middle' x='8' 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