让无人车在感知不确定时能自知能力边界，安全避障。
Competency-Aware Planning for Probabilistically Safe Navigation Under Perception Uncertainty
- 用重建误差和概率建模评估模型对图像整体及局部的熟悉程度。
- 相比无感知的基线，碰撞率显著降低，尤其在陌生障碍物场景中。
- 适合需要高安全性的无人车导航，如复杂地形巡检与救援任务。
基于感知的导航系统在复杂地形中对无人地面车辆（UGV）导航很有价值，传统深度导航方法在此类场景下表现不足。然而，这些数据驱动的方法高度依赖训练数据，在出现意外情况时可能毫无预警地失效。为保障车辆及周边环境的安全，导航系统必须能识别感知模型的预测不确定性，并在不确定性面前做出安全有效的响应。为此，本文提出一种基于概率与重建的熟练度评估方法（PaRCE），用于评估模型对输入图像整体及其特定区域的熟悉程度。实验表明，整体熟练度评分可准确预测正确分类、错误分类及分布外（OOD）样本；区域熟练度图可有效区分图像中的熟悉与不熟悉区域。基于此熟练度信息，我们设计了一种规划与控制方案，实现低出错率下的有效导航。结果表明，该熟练度感知方案显著减少了与陌生障碍物的碰撞次数，且区域熟练度信息对高效导航具有重要价值。
原文摘要 · Abstract (English)
Perception-based navigation systems are useful for unmanned ground vehicle (UGV) navigation in complex terrains, where traditional depth-based navigation schemes are insufficient. However, these data-driven methods are highly dependent on their training data and can fail in surprising and dramatic ways with little warning. To ensure the safety of the vehicle and the surrounding environment, it is imperative that the navigation system is able to recognize the predictive uncertainty of the perception model and respond safely and effectively in the face of uncertainty. In an effort to enable safe navigation under perception uncertainty, we develop a probabilistic and reconstruction-based competency estimation (PaRCE) method to estimate the model's level of familiarity with an input image as a whole and with specific regions in the image. We find that the overall competency score can correctly predict correctly classified, misclassified, and out-of-distribution (OOD) samples. We also confirm that the regional competency maps can accurately distinguish between familiar and unfamiliar regions across images. We then use this competency information to develop a planning and control scheme that enables effective navigation while maintaining a low probability of error. We find that the competency-aware scheme greatly reduces the number of collisions with unfamiliar obstacles, compared to a baseline controller with no competency awareness. Furthermore, the regional competency information is very valuable in enabling efficient navigation.
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