Vector Map Quality Metrics for Contextual Autonomous Driving Systems

Abstract:Ensuring safety in autonomous driving requires continuous map maintenance supported by reliable quality indicators. In this context, it is crucial to identify when and where map updates should be triggered, for instance through crowdsourced data, and under which conditions a new map compilation should be deployed. This paper focuses on effective metrics for assessing the quality of vector maps and guiding such decisions. We present a new metric called GOSPAM designed to measure map discrepancies in terms of location errors, existence, and completeness. Through detailed simulations on both point and polyline feature maps, we analyze its sensitivity to common map degradation such as bias, false positives, false negatives, and coordinate errors. The results demonstrate that GOSPAM offers a unified and interpretable measure that effectively captures various forms of map deviation, making it a strong candidate for map quality assessment in automotive applications.

Robotics (cs.RO)

arXiv:2610.08043 [cs.RO]

(or arXiv:2610.08043v1 [cs.RO] for this version)

https://doi.org/10.48550/arXiv.2610.08043 Focus to learn more arXiv-issued DOI via DataCite (pending registration)

https://doi.org/10.1109/ICVES65691.2025.11376526 Focus to learn more DOI(s) linking to related resources

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