发表机构
Bosch Research; University of Lübeck（博世研究院; 吕贝克大学）
机构由 AI 辅助整理，请以论文原文为准。AI 中文总结
该研究通过替换自动驾驶模型的相机输入为同一地点的历史驾驶记忆，发现NAVSIM上仅靠记忆即可达到甚至超过领先端到端方法的性能，提示NAVSIM分数需谨慎对待，且该效应存在基准依赖性。
AI 中文摘要
端到端自动驾驶模型从原始传感器输入中规划未来轨迹。早期驾驶基准通常衡量与人类轨迹的偏差，而当前如NAVSIM和Bench2Drive等基准采用更丰富的基于仿真的指标，旨在捕捉安全合规的驾驶行为。高基准分数应反映模型能理解前方场景并做出相应动作，但该分数中究竟有多少来自对场景动态部分的反应？为探究此问题，我们移除模型的相机输入，代之以同一地点先前驾驶的记忆。检索到的记忆可提供持久的场景信息，包括道路布局和位置相关规律，但无法提供当前交通状态。令人惊讶的是，在NAVSIM上，记忆几乎足够，甚至达到或超过领先的端到端方法的性能，而无需实际观察被评估的场景。我们的结果表明，高NAVSIM分数并不要求规划器对当前交通场景做出反应，应谨慎对待。该效应具有基准依赖性：在Bench2Drive和RealEngine上，基于记忆的驾驶会导致性能大幅下降。我们的代码在此httpsURL提供。
英文摘要
End-to-end autonomous driving models plan future trajectories from raw sensor input. While earlier driving benchmarks often measured deviation from the human trajectory, current benchmarks such as NAVSIM and Bench2Drive evaluate models with richer simulation-based metrics intended to capture safe and compliant driving. A high benchmark score should reflect that a model can understand the scene in front of it and act accordingly. But how much of that score specifically comes from reacting to the dynamic part of that scene? To probe this, we remove a model's camera input and replace it with memories from prior drives at the same location. The retrieved memories can provide persistent scene information, including road layout and location-conditioned regularities, but not the current traffic state. Surprisingly, memory is nearly sufficient on NAVSIM, reaching or even exceeding the performance of leading end-to-end methods without actually observing the evaluated scene. Our results suggest that a high NAVSIM score does not require a planner to react to the current traffic scene and should be treated with caution. This effect is benchmark-dependent: driving from memory causes substantially larger performance drops on Bench2Drive and RealEngine. We provide our code at https://github.com/boschresearch/MemoryDrivoR .
