Evaluation of a mapping strategy based on smooth arc splines for different road types
Evaluation of a mapping strategy based on smooth arc splines for different road types
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DOI：
10.1109/itsc.2013.6728227
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发表时间：
2013-10
期刊：
影响因子：
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通讯作者：
Stephan Brummer;F. Janda;G. Maier;A. Schindler
中科院分区：
文献类型：
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作者：
Stephan Brummer;F. Janda;G. Maier;A. Schindler
数字地图通过提供有关车辆当地环境的信息，丰富了先进的驾驶员辅助系统。本文给出了一种使用光滑圆弧样条线作为几何模型的映射策略的各种结果。对于任何给定的公差，曲线逼近方法(SMAP)会生成具有最小可能数量的曲线段的平滑圆弧样条线。评价结果表明，该方法在精度、数据量和显著的曲率特征方面都表现出了良好的性能，在农村公路和公路上都是如此。可以说，弧样条近似在信息量和分段数量方面通常优于多边形表示，这直接影响到地图计算的计算复杂性和地图存储所需的数据量。这些特性对于诸如自动驾驶之类的许多驾驶员辅助系统应用是有益的。此外，研究还表明，在较大的近似公差范围内，曲线尖点处的曲率估计是广泛稳定的，这对于曲线速度警告是至关重要的。
Digital maps enrich advanced driver assistance systems by providing information on the local environment of a vehicle. This paper presents various results of a mapping strategy which uses smooth arc splines as geometric model. For any given tolerance, the curve approximation method (SMAP) generates a smooth arc spline with the minimally possible number of curve segments. The evaluation shows the performance of this method regarding the accuracy, the data volume and significant curvature characteristics on both rural and highway roads. It can be stated that the arc spline approximation generally outperforms polygonal representations regarding the information content and the number of segments which has a direct influence on the computational complexity of map calculations and the required data volume for the map storage. These properties are beneficial for many driver assistance system applications like autonomous driving. Furthermore, it is shown that the curvature estimation in the curve apexes is widely stable for a broad range of approximation tolerance values which is crucial for curve speed warnings.