Uncertainty Quantification for Bird's Eye View Semantic Segmentation: Methods and Benchmarks

• URL: http://arxiv.org/abs/2405.20986v1
• Date: Fri, 31 May 2024 16:32:46 GMT
• Title: Uncertainty Quantification for Bird's Eye View Semantic Segmentation: Methods and Benchmarks
• Authors: Linlin Yu, Bowen Yang, Tianhao Wang, Kangshuo Li, Feng Chen,
• Abstract summary: This paper introduces a benchmark for predictive uncertainty quantification in BEV segmentation. It focuses on the effectiveness of predicted uncertainty in identifying misclassified and out-of-distribution pixels, as well as calibration. We propose the Uncertainty-Focal-Cross-Entropy loss, designed for highly imbalanced data, which consistently improves the segmentation quality and calibration.
• Score: 10.193504550494486
• License: http://arxiv.org/licenses/nonexclusive-distrib/1.0/
• Abstract: 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.

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