发表机构
Seoul National University（首尔大学）
机构由 AI 辅助整理，请以论文原文为准。AI 中文总结
针对越野导航中人类早期安全判断未被运行时警报捕获的问题，提出CUSP模型，利用CUSUM控制图累积离散时间生存模型预测的感知起始风险，在五个未见场地以相同误报率下检测到85个事件，远超基线。
AI 中文摘要
越野导航使机器人在行进途中面临潜在危险地形。尽管基于学习的导航使用安全监督来选择行驶路径，但当机器人沿该路径行驶并即将陷入危险时，它不会提供运行时警报。此类警报必须从现场日志中学习，而在这些日志中，人工干预先于故障发生，因此故障本身从未被观察到。人类判断驾驶不安全的时间较早，但通常仅在故障明显临近时才进行干预，因此干预标记了这一判断的滞后。较早的判断才是运行时警报必须检测的，然而此前没有任何基于干预监督的方法针对这一目标。为解决此问题，我们提出了CUSP（感知起始时刻的CUSUM控制生存模型），这是一种模型无关的运行时危险警报，可从干预终止的日志中学习该时刻。“Cusp”一词指一种状态即将转变为另一种状态的那个点，而我们针对的时刻正是这样一个临界点：在人类判断中，安全驾驶转变为不安全驾驶的那个点。我们将该点称为感知起始时刻，并区别于干预单独对其进行标注。一个视觉危险头在标注的起始时刻上使用离散时间生存目标进行训练，使得有起始时刻和无起始时刻的驾驶都能监督该头，同时CUSUM将预测的起始风险累积为警报。我们在五个未见过的场地（两个自主场地和三个远程操作场地）上评估CUSP，共142个事件，每种方法均调整为每小时十次误报的相同速率。CUSP检测到85个事件，而九个适配基线中最佳方法仅检测到26个，这一优势来自于导航模型中所有信号均无法感知的危险。
英文摘要
Off-road navigation exposes a robot to potentially hazardous terrain en route. Although learning-based navigation uses safety supervision to choose which path to drive, it provides no runtime alarm when the robot following that path is heading into danger. Such an alarm must be learned from field logs, where human intervention preempts the failure and the failure itself is therefore never observed. The human judges driving unsafe early but typically intervenes only once failure is clearly near, so the intervention marks that judgment late. That earlier judgment is what a runtime alarm must detect, yet no prior intervention-supervised method has targeted it. To address this problem, we introduce CUSP (CUSUM-governed Survival model of the Perception onset), a model-agnostic runtime hazard alarm that learns this moment from intervention-terminated logs. "Cusp" is a word for the point at which one state is about to turn into another, and the moment we target is exactly such a cusp: the point at which safe driving turns unsafe in a human's judgment. We call this point the perception onset and annotate it separately from the intervention. A visual hazard head is trained on the annotated onset with a discrete-time survival objective so that driving with and without an onset both supervise the head, and a CUSUM accumulates the predicted onset risk into alarms. We evaluate CUSP at five unseen sites, two autonomous and three teleoperated, with 142 events and every method tuned to the same rate of ten false alarms per hour. CUSP detected 85 events compared to 26 for the best of nine adapted baselines, and the margin comes from hazards to which every signal in the navigation model is blind.
Comments8 pages, 4 figures. Project page: https://in-uk.github.io/cusp-project/
