Vector Map Quality Metrics for Contextual Autonomous Driving Systems

Marie-Ngoïe Badibanga Kalenda; Philippe Bonnifait; Marie-Anne Mittet

cs.ROOct 6, 2026

Vector Map Quality Metrics for Contextual Autonomous Driving Systems

Authors: Marie-Ngoïe Badibanga Kalenda, Philippe Bonnifait, Marie-Anne Mittet

Organizations: Université de Technologie de Compiègne, CNRS, Heudiasyc, France · Ampere Software Technology, Guyancourt, France

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.

Figures & tables

Fig. 1 : HD vector Map example (from Here)

Fig. 2 : Illustration of the problem with a map of points. The crosses (+) are the map to be evaluated and the solid discs correspond to the real world.

	Map feature	No Map feature
Truly exists	TP	FN
Does not exist	FP	TN

TABLE I : Map features evaluation logic

Fig. 3 : Example of tile with point features. The circles correspond to the reference map, the diamonds to a map to be evaluated, the dotted circles show association gating and the black lines show associations. Units are meters.

Panel of Figure 5 — Fig. 4 : From top to bottom and left to right: effects of the bias, FN, FP, and coordinate errors (Monte Carlo box plot) on $Gospa_{M}$ for for a map containing only points

Fig. 5 : Illustration of the two polyline maps. The map to be evaluated is in dotted lines. The colors show the existence of 3 lanes groups here.

Fig. 6 :

\text{GOSPA}_{M}

as a function of the coordinate errors for a map containing only polylines.

Explore similar work

Assessing the Impact of Fleet Size on Crowdsourced Mapping Using a Dissimilarity Measure
Sep 24, 2026Marie-Ngoïe Badibanga Kalenda, Philippe Bonnifait, Marie-Anne MittetLane-Level Map Generation Traffic Sign Recognition
Heudiasyc · Université de Technologie de Compiègne, CNRS, Heudiasyc, Renault, Guyancourt, France · Université de Technologie de Compiègne, CNRS, Heudiasyc, France +1
Beyond Chamfer Distance: Granular Order-aware Evaluation Metric For Online Mapping
May 21, 2026Chouaib Bencheikh Lehocine, Adam Lilja, Junsheng Fu +1Lane-Level Map Generation Mean Average Precision
1Zenseact AB · 2Chalmers University of Technology Gothenburg, Sweden
OptiMVMap: Offline Vectorized Map Construction via Optimal Multi-vehicle Perspectives
Apr 18, 2026Zedong Dan, Zijie Wang, Wei Zhang +6Lane-Level Map Generation Bird'S-Eye View
Sun Yat-sen University · Zhongguancun Academy · Shenzhen Loop Area Institute +2

Sep 24, 2026, cs.ROJ/K move · Enter open · S save

Assessing the Impact of Fleet Size on Crowdsourced Mapping Using a Dissimilarity Measure

Marie-Ngoïe Badibanga Kalenda, Philippe Bonnifait, Marie-Anne Mittet

Heudiasyc · Université de Technologie de Compiègne, CNRS, Heudiasyc, Renault, Guyancourt, France · Université de Technologie de Compiègne, CNRS, Heudiasyc, France+1

Lane-Level Map Generation Traffic Sign Recognition Autonomous Driving Detection Performance Impact Analysis

Open paper AskPDF arXiv

Accurate digital maps are essential for Advanced Driver Assistance Systems (ADAS) or Autonomous Driving (AD), providing critical information such as road geometry, traffic signs and speed limits required by safety functions including Intelligent Speed Assistance (ISA). Maintaining these map layers using traditional surveying methods is costly and difficult to scale. Crowdsourced approaches based on fleets provide a promising alternative for continuously validating and updating map information. However, the relationship between the number of contributing vehicles and the quality of the resulting map remains poorly understood. To address this gap, this paper presents a simulation-based framework for evaluating crowdsourced traffic sign maintenance using a dissimilarity measure called GOSPAM (Generalized Optimal SubPattern Assignment for Maps), which combines localization errors with detection performance by accounting for False Positives (FP) and False Negatives (FN). The proposed system models multivehicle observations with representative sensor noise, detection errors, and semantic recognition uncertainties. Observations from multiple vehicles are aggregated using spatial clustering and semantic filtering to estimate traffic sign locations. Using simulated trajectories generated from data carried out by an experimental vehicle in an area containing ground-truth traffic signs, we assess the influence of fleet size on the performance of crowdsourced mapping. The number of vehicles ranges from 5 to 50, and performance is analyzed using standard evaluation metrics which are compared to the GOSPAM . The results show that GOSPAM can be used to effectively assess the quality of crowdsourced mapping, such as the contributions made by the first vehicles or the improvements made by numerous vehicles.

Abstract

Explore similar work

Assessing the Impact of Fleet Size on Crowdsourced Mapping Using a Dissimilarity Measure

Beyond Chamfer Distance: Granular Order-aware Evaluation Metric For Online Mapping

OptiMVMap: Offline Vectorized Map Construction via Optimal Multi-vehicle Perspectives