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

2026-09-24

AI Summary

This study addresses the unclear relationship between fleet size and mapping quality in crowdsourced mapping, as well as the high cost and limited scalability of traditional surveying methods. To this end, it proposes a multi-vehicle cooperative perception simulation framework based on the GOSPAM metric. By integrating localization errors with detection performance (false positives and false negatives), and incorporating spatial clustering, semantic filtering, and real-world trajectory simulation, the framework effectively quantifies individual fleet contributions. Experimental evaluations across fleet sizes ranging from 5 to 50 vehicles demonstrate that GOSPAM precisely assesses early vehicle contributions and accuracy gains in large-scale fleets. Consequently, this work provides a scalable, quantitative evaluation approach for maintaining crowdsourced maps.

Abstract

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.

Problem

• crowdsourced mapping
• fleet size
• map quality
• traffic signs
• ADAS

Innovation

• Crowdsourced Mapping
• GOSPAM
• Dissimilarity Measure
• Fleet Size
• Traffic Sign Maintenance