评估车队规模对众包地图构建的影响：基于一种差异度量方法
Assessing the Impact of Fleet Size on Crowdsourced Mapping Using a Dissimilarity Measure
- Université de Technologie de Compiègne（贡比涅技术大学）
- CNRS（法国国家科学研究中心）
- Renault（雷诺集团）
机构由 AI 辅助整理，请以论文原文为准。
AI总结：
本文提出基于GOSPAM差异度量的仿真框架，评估车队规模（5至50辆）对众包交通标志地图构建质量的影响，验证其能有效衡量首批车辆贡献及多车改进效果。
AI中文摘要：
精确的数字地图对于高级驾驶辅助系统（ADAS）或自动驾驶（AD）至关重要，它们提供道路几何、交通标志和限速等关键信息，这些信息是包括智能速度辅助（ISA）在内的安全功能所必需的。使用传统测量方法维护这些地图图层成本高昂且难以扩展。基于车队的众包方法为持续验证和更新地图信息提供了一种有前景的替代方案。然而，贡献车辆数量与生成地图质量之间的关系仍然鲜为人知。为解决这一空白，本文提出了一种基于仿真的框架，用于评估众包交通标志维护，采用一种称为GOSPAM（地图广义最优子模式分配）的差异度量，该度量通过考虑假阳性（FP）和假阴性（FN）将定位误差与检测性能相结合。所提出的系统对多车辆观测进行建模，包含代表性的传感器噪声、检测误差和语义识别不确定性。来自多辆车的观测通过空间聚类和语义过滤进行聚合，以估计交通标志位置。利用由实验车辆在包含地面真实交通标志的区域中采集的数据生成的模拟轨迹，我们评估了车队规模对众包地图构建性能的影响。车辆数量从5辆到50辆不等，并使用标准评估指标分析性能，并与GOSPAM进行比较。结果表明，GOSPAM可有效评估众包地图构建的质量，例如首批车辆的贡献或众多车辆带来的改进。
英文摘要：
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.
