Probabilistic Segmentation for Robust Field of View Estimation

Bibliographic Details
Title: Probabilistic Segmentation for Robust Field of View Estimation
Authors: Hallyburton, R. Spencer, Hunt, David, He, Yiwei, He, Judy, Pajic, Miroslav
Publication Year: 2025
Collection: Computer Science
Subject Terms: Computer Science - Computer Vision and Pattern Recognition, Computer Science - Machine Learning
More Details: Attacks on sensing and perception threaten the safe deployment of autonomous vehicles (AVs). Security-aware sensor fusion helps mitigate threats but requires accurate field of view (FOV) estimation which has not been evaluated autonomy. To address this gap, we adapt classical computer graphics algorithms to develop the first autonomy-relevant FOV estimators and create the first datasets with ground truth FOV labels. Unfortunately, we find that these approaches are themselves highly vulnerable to attacks on sensing. To improve robustness of FOV estimation against attacks, we propose a learning-based segmentation model that captures FOV features, integrates Monte Carlo dropout (MCD) for uncertainty quantification, and performs anomaly detection on confidence maps. We illustrate through comprehensive evaluations attack resistance and strong generalization across environments. Architecture trade studies demonstrate the model is feasible for real-time deployment in multiple applications.
Document Type: Working Paper
Access URL: http://arxiv.org/abs/2503.07375
Accession Number: edsarx.2503.07375
Database: arXiv
More Details
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