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 for the bounding boxes. This broadens the validity of our conformal coverage guarantees to include incorrectly classified objects, ensuring their usefulness when maximal safety assurances are required. 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 across sizes. Validating our two-step approach on real-world datasets for 2D bounding box localization, we find that desired coverage levels are satisfied with actionably tight predictive uncertainty intervals.

翻译：量化模型预测不确定性对于自动驾驶等安全关键应用至关重要。本研究针对多目标检测场景下的不确定性量化问题展开研究，具体利用保形预测方法为目标检测中的边界框提供具有保证覆盖率的预测不确定性区间。由于边界框预测需以目标类别标签为条件，我们提出了一种新颖的两步骤保形预测方法，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。该方法扩展了保形覆盖保证的有效性，使其涵盖分类错误的检测目标，确保在需要最高安全保障时仍具实用性。此外，我们研究了新型集成分位数回归方法，使边界框区间能自适应目标尺寸变化，从而在各尺寸目标间实现更均衡的覆盖率。在二维边界框定位的真实数据集上验证表明，该方法能在满足预设覆盖率要求的同时，给出具备实际操作效用的紧凑预测不确定性区间。