Stability Under Scrutiny: Benchmarking Representation Paradigms for Online HD Mapping
🤖 AI Summary
Online high-definition (HD) maps for autonomous driving suffer from temporal instability caused by sensor pose drift, yet existing work focuses solely on per-frame accuracy while neglecting cross-frame stability. To address this gap, we introduce the first comprehensive benchmark dedicated to evaluating temporal stability of online HD maps. We propose a multi-dimensional stability assessment framework, defining three novel metrics—existence stability, localization stability, and shape stability—and a unified mean Average Stability (mAS) score. Extensive experiments across 42 models and variants demonstrate that accuracy (measured by mAP) and stability (mAS) constitute largely orthogonal performance dimensions. Our analysis identifies key architectural and training design factors—such as temporal consistency regularization, pose-aware feature alignment, and stability-augmented loss functions—that jointly enhance both mAP and mAS. These findings provide both theoretical insights and practical guidelines for developing more robust online HD mapping systems.
Application Category
📝 Abstract
As one of the fundamental modules in autonomous driving, online high-definition (HD) maps have attracted significant attention due to their cost-effectiveness and real-time capabilities. Since vehicles always cruise in highly dynamic environments, spatial displacement of onboard sensors inevitably causes shifts in real-time HD mapping results, and such instability poses fundamental challenges for downstream tasks. However, existing online map construction models tend to prioritize improving each frame's mapping accuracy, while the mapping stability has not yet been systematically studied. To fill this gap, this paper presents the first comprehensive benchmark for evaluating the temporal stability of online HD mapping models. We propose a multi-dimensional stability evaluation framework with novel metrics for Presence, Localization, and Shape Stability, integrated into a unified mean Average Stability (mAS) score. Extensive experiments on 42 models and variants show that accuracy (mAP) and stability (mAS) represent largely independent performance dimensions. We further analyze the impact of key model design choices on both criteria, identifying architectural and training factors that contribute to high accuracy, high stability, or both. To encourage broader focus on stability, we will release a public benchmark. Our work highlights the importance of treating temporal stability as a core evaluation criterion alongside accuracy, advancing the development of more reliable autonomous driving systems. The benchmark toolkit, code, and models will be available at https://stablehdmap.github.io/.
Problem
Research questions and friction points this paper is trying to address.
Evaluating temporal stability in online HD mapping models
Analyzing mapping instability caused by sensor spatial displacement
Developing stability metrics and benchmark for autonomous driving systems
Innovation
Methods, ideas, or system contributions that make the work stand out.
Proposed multi-dimensional stability evaluation framework with novel metrics
Introduced unified mean Average Stability score for mapping
Analyzed architectural and training factors for stability
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