面向情境化自动驾驶系统的矢量地图质量指标
Vector Map Quality Metrics for Contextual Autonomous Driving Systems
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中文总结 AI 辅助
本文提出GOSPAM指标，用于评估矢量地图质量，通过仿真验证其能有效捕捉位置误差、存在性和完整性等地图偏差，为自动驾驶地图更新决策提供统一可解释的度量。
中文摘要 AI 辅助
确保自动驾驶安全性需要持续的地图维护，并依赖可靠的质量指标来支撑。在此背景下，识别何时何地应触发地图更新（例如通过众包数据）以及在新地图编译部署时应满足哪些条件至关重要。本文聚焦于评估矢量地图质量并指导此类决策的有效指标。我们提出了一种名为GOSPAM的新指标，旨在测量地图在位置误差、存在性和完整性方面的差异。通过对点和折线特征地图进行详细仿真，我们分析了该指标对常见地图退化（如偏差、假阳性、假阴性和坐标误差）的敏感性。结果表明，GOSPAM提供了一种统一且可解释的度量，能有效捕捉各种形式的地图偏差，使其成为汽车应用中地图质量评估的有力候选方案。
英文摘要
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.
发表机构
- Université de Technologie de Compiègne（贡比涅技术大学）
- Ampere Software Technology（安培软件技术公司）
机构由 AI 辅助整理，请以论文原文为准。
