[2603.19852] Failure Modes for Deep Learning-Based Online Mapping: How to Measure and Address Them

Computer Science > Computer Vision and Pattern Recognition arXiv:2603.19852 (cs) [Submitted on 20 Mar 2026] Title:Failure Modes for Deep Learning-Based Online Mapping: How to Measure and Address Them Authors:Michael Hubbertz, Qi Han, Tobias Meisen View a PDF of the paper titled Failure Modes for Deep Learning-Based Online Mapping: How to Measure and Address Them, by Michael Hubbertz and 2 other authors View PDF HTML (experimental) Abstract:Deep learning-based online mapping has emerged as a cornerstone of autonomous driving, yet these models frequently fail to generalize beyond familiar environments. We propose a framework to identify and measure the underlying failure modes by disentangling two effects: Memorization of input features and overfitting to known map geometries. We propose measures based on evaluation subsets that control for geographical proximity and geometric similarity between training and validation scenes. We introduce Fréchet distance-based reconstruction statistics that capture per-element shape fidelity without threshold tuning, and define complementary failure-mode scores: a localization overfitting score quantifying the performance drop when geographic cues disappear, and a map geometry overfitting score measuring degradation as scenes become geometrically novel. Beyond models, we analyze dataset biases and contribute map geometry-aware diagnostics: A minimum-spanning-tree (MST) diversity measure for training sets and a symmetric coverage measure to...