Quantifying a model's predictive uncertainty is essential for safety-critical applications such as autonomous driving. We consider quantifying such uncertainty for multi-object detection. In particular, we leverage conformal prediction to obtain uncertainty intervals with guaranteed coverage for object bounding boxes. One challenge in doing so is that bounding box predictions are conditioned on the object's class label. Thus, we develop a novel two-step conformal approach that propagates uncertainty in predicted class labels into the uncertainty intervals of bounding boxes. This broadens the validity of our conformal coverage guarantees to include incorrectly classified objects, thus offering more actionable safety assurances. Moreover, we investigate novel ensemble and quantile regression formulations to ensure the bounding box intervals are adaptive to object size, leading to a more balanced coverage. Validating our two-step approach on real-world datasets for 2D bounding box localization, we find that desired coverage levels are satisfied with practically tight predictive uncertainty intervals.

翻译：在自动驾驶等安全关键应用中，量化模型的预测不确定性至关重要。本文研究多目标检测任务中的不确定性量化问题。具体而言，我们利用保形预测方法为物体边界框构建具有统计覆盖保证的不确定性区间。实现过程中的一个核心挑战在于：边界框预测是以物体类别标签为条件的。为此，我们提出了一种新颖的两步保形预测方法，将预测类别标签的不确定性传播至边界框的不确定性区间中。这一机制将保形覆盖保证的有效性扩展至误分类物体，从而提供更具可操作性的安全保障。此外，我们研究了基于集成学习与分位数回归的新颖建模方法，确保边界框区间能够自适应物体尺寸，实现更均衡的覆盖效果。通过在真实世界二维边界框定位数据集上的验证，我们发现所提出的两步方法在满足预设覆盖水平的同时，能够生成实际应用中足够紧凑的预测不确定性区间。