  The fusion of raw features from multiple sensors on an autonomous vehicle to
create a Bird's Eye View (BEV) representation is crucial for planning and
control systems. There is growing interest in using deep learning models for
BEV semantic segmentation. Anticipating segmentation errors and improving the
explainability of DNNs is essential for autonomous driving, yet it is
under-studied. This paper introduces a benchmark for predictive uncertainty
quantification in BEV segmentation. The benchmark assesses various approaches
across three popular datasets using two representative backbones and focuses on
the effectiveness of predicted uncertainty in identifying misclassified and
out-of-distribution (OOD) pixels, as well as calibration. Empirical findings
highlight the challenges in uncertainty quantification. Our results find that
evidential deep learning based approaches show the most promise by efficiently
quantifying aleatoric and epistemic uncertainty. We propose the
Uncertainty-Focal-Cross-Entropy (UFCE) loss, designed for highly imbalanced
data, which consistently improves the segmentation quality and calibration.
Additionally, we introduce a vacuity-scaled regularization term that enhances
the model's focus on high uncertainty pixels, improving epistemic uncertainty
quantification.
