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首页> 外文会议>IEEE International Conference on Intelligent Transportation Systems >Estimating localization uncertainty using multi-hypothesis map-matching on high-definition road maps
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Estimating localization uncertainty using multi-hypothesis map-matching on high-definition road maps

机译：在高清路线图上使用多假设地图匹配估计定位不确定性

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Lane-level road maps are necessary for Advanced Driving Assistance Systems (ADAS) and autonomous vehicles, providing prior contextual information on the road network. At this level of detail, map-matching becomes complex: much ambiguity can arise due to the high number of likely hypotheses (especially in dense urban areas) and due to the varying performance of GNSS positioning systems. This paper presents a method measuring the confidence in the information provided by the positioning system using a metric called Map Aided Horizontal Uncertainty Level (MA-HUL). It relies on a map-matching algorithm designed to keep all likely hypotheses that are coherent with the vehicle odometry. Additionally, exteroceptive information is compared to the map data to remove ambiguities (i.e. lane marking detection from a camera). Some hypotheses are then trimmed if a mismatch is detected with sufficient confidence, keeping their number to a minimum. The use of a reference frame linked to the road is also studied to represent uncertainty independently in the along-track and cross-track axes. The performance of the proposed approach is illustrated using data recorded from an experimental vehicle operating on public roads.
机译：车道级路线图对于高级驾驶辅助系统（ADAS）和自动驾驶汽车是必不可少的，可提供有关路网的事先上下文信息。在这种详细程度下，地图匹配变得复杂：由于大量可能的假设（尤其是在人口稠密的城市地区）以及GNSS定位系统的性能变化，可能会引起很多歧义。本文提出了一种使用称为“地图辅助水平不确定度”（MA-HUL）的度量标准来测量定位系统所提供信息的可信度的方法。它依赖于地图匹配算法，该算法设计为保持与车辆里程表一致的所有可能假设。另外，将外在感受性信息与地图数据进行比较以消除歧义（即，从摄像机检测车道标记）。如果以足够的置信度检测到不匹配，则对某些假设进行修整，从而将其数目保持为最小。还研究了使用链接到道路的参考系来代表沿轨迹和交叉轨迹轴的不确定性。使用从在公共道路上运行的实验车辆记录的数据说明了所提出方法的性能。

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