发表机构
Noertheastern University; Bosch North America（东北大学; 博世北美）
机构由 AI 辅助整理，请以论文原文为准。AI 中文总结
本研究审计了自动驾驶中基于内部表示的故障监控器，发现仅用可观察输出即可达到相当甚至更好的预测性能，潜特征无显著增量价值，并提出了评估协议。
AI 中文摘要
运行时故障监控器可以利用模型的内部表示来预测故障。我们在两个自动驾驶任务中审计了这种监控策略：使用LaneSegNet进行在线矢量化地图生成，以及使用VAD进行端到端规划。我们发现，在这两个任务中，帧级错误在推理时是可预测的。对于LaneSegNet，监督式潜空间探针在高Chamfer误差下达到了接收者操作特征曲线下面积（AUROC）0.780；据我们所知，这是首个针对在线矢量化地图生成的事后帧级故障监控器。对于VAD，监督式规划潜空间探针在平均ADE故障上达到了AUROC 0.868。我们的审计表明，内部访问对于强故障预测并非必要。仅使用LaneSegNet预测输出的监控器达到了AUROC 0.825，而对于VAD，自我状态、驾驶命令和规划器的预测轨迹在相同的平均ADE端点上达到了0.924。将潜特征添加到任一基线中并未产生统计上显著的改进。这一观察在广泛的规划故障端点集合中持续存在，包括那些标签依赖于非潜空间基线无法获得的几何信息的端点。因此，从内部表示预测故障并不能证明该表示提供了超出可观察输入和输出的有用信息。我们提出了一种评估协议，用于测试潜空间访问的增量价值，并发布了我们的逐帧故障端点标签。
英文摘要
Runtime failure monitors can use a model's internal representations to anticipate failures. We audit this monitoring strategy across two autonomous-driving tasks: online vectorized map generation with LaneSegNet and end-to-end planning with VAD. We find that frame-level errors are predictable at inference in both tasks. For LaneSegNet, a supervised latent probe reaches Area Under the Receiver Operating Characteristic curve (AUROC) 0.780 for high Chamfer error; to our knowledge, this is the first post-hoc frame-level failure monitor for online vectorized map generation. For VAD, a supervised planning-latent probe reaches AUROC 0.868 for mean-ADE failure. Our audit shows that internal access is not necessary for strong failure prediction. A monitor using only LaneSegNet's prediction outputs reaches AUROC 0.825, while for VAD, ego state, driving command, and the planner's predicted trajectory reach 0.924 on the same mean-ADE endpoint. Adding latent features to either baseline yields no statistically resolved improvement. This observation persists across a broad suite of planning failure endpoints, including endpoints whose labels depend on geometry unavailable to the non-latent baseline. Thus, predicting failure from an internal representation does not establish that the representation provides useful information beyond observable inputs and outputs. We propose an evaluation protocol for testing the incremental value of latent access and release our per-frame failure endpoint labels.
