Mining GPS data to augment road models
Mining GPS data to augment road models
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DOI：
10.1145/312129.312208
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发表时间：
1999-08
期刊：
影响因子：
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通讯作者：
Seth Rogers;P. Langley;Christopher Wilson
中科院分区：
文献类型：
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作者：
Seth Rogers;P. Langley;Christopher Wilson
车辆中的许多高级安全和导航应用需要精确、详细的数字地图，但手动车道测量既昂贵又耗时，因此需要自动化技术。本文介绍了一种数据挖掘方法，地图精化，使用来自全球定位系统接收器的差分校正的位置轨迹。所计算的车道模型实现了诸如车道保持的安全应用和诸如车道改变建议的便利应用。实验表明，从商业上可用的基线地图开始，我们的车道模型从特定路段的少量通过中以高精度预测车辆的车道。多个位置轨迹是一个强大的新数据源，它可以实现廉价的自动化方法来诱导车道模型以及其他地理知识，如交通信号和海拔，并可能影响任何需要与实际行为相关的地理信息系统。
Many advanced safety and navigation applications in vehicles require accurate, detailed digital maps, but manual lane measurements are expensive and time-consuming, making automated techniques desirable. This paper describes a data-mining approach to map refinement, using position traces that come from Global Positioning System receivers with differential corrections. The computed lane models enable safety applications, such as lanekeeping, and convenience applications, such as lane-changing advice. Experiments show that, starting from a baseline map that is commercially available, our lane models predict a vehicle’s lane with high accuracy from a small number of passes over a particular road segment. Multiple position traces are a powerful new source of data that enables cheap, automated methods of inducing lane models, as well as other geographic knowledge, like traffic signals and elevations, and potentially impacts any geographic information system with a need to relate to actual behavior.