外观
论文arXiv 2609.291981 个月前
基于差异度量评估车队规模对众包制图的影响
原文链接：Assessing the Impact of Fleet Size on Crowdsourced Mapping Using a Dissimilarity Measure（arXiv 2609.29198 · 发布日期：2026-09-28）
Assessing the Impact of Fleet Size on Crowdsourced Mapping Using a Dissimilarity Measure
本文用含GOSPAM差异度量的仿真框架评估车队规模对众包交通标志维护质量的影响，并在实验车数据生成的轨迹上验证。
核心要点
- 动机是传统地图测绘成本高且难扩展，车队众包可持续验证和更新地图信息。
- 方法以GOSPAM结合定位误差与检测性能，计入误报和漏报并建模多车观测不确定性。
- 系统通过空间聚类和语义过滤聚合多车观测，用于估计交通标志的位置。
- 实验使用实验车辆数据生成的仿真轨迹，车队规模为5到50，并将标准指标与GOSPAM比较。
验证范围与局限：验证范围为基于实验车辆数据生成的仿真轨迹，场景为含真值交通标志的区域。
英文摘要 (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 multi-vehicle 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.
作者：Marie-Ngoïe Badibanga Kalenda, Philippe Bonnifait, Marie-Anne Mittet
来源
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