ABSTRACT Maintaining an up-to-date repository of horizontal curve attributes is extremely important due to the role curves play in the safe operation of highways. Such attributes are typically collected using traditional surveying techniques which have been shown to be time-consuming, traffic disruptive, and potentially unsafe methods. This burden is further aggravated when the data collection is required on a large highway network across North America. To overcome this burden, this paper proposes a framework for network-level detection and extraction of horizontal curve elements from Light Detection and Ranging (LiDAR) data in a fully automated manner. The proposed technique was validated and then tested on LiDAR data collected on 242 km of highways in Alberta, Canada. The algorithm was successful in detecting all curves on the test highways and estimated their attributes with accuracies ranging from 96% to 100% demonstrating the robustness of the extraction method and the feasibility of performing the extraction on such a large scale. The proposed method is an alternative approach that could help transportation agencies maintain an updated inventory of horizontal alignment information on a large-scale.

中文翻译：

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从遥感点云检测水平曲线并评估其几何特性的框架

摘要 由于曲线在高速公路安全运营中的作用，保持水平曲线属性的最新存储库极为重要。这些属性通常是使用传统的勘测技术收集的，这些技术已被证明是耗时、交通中断和潜在不安全的方法。当需要在整个北美的大型高速公路网络上收集数据时，这种负担会进一步加重。为了克服这一负担，本文提出了一种以全自动方式从光检测和测距 (LiDAR) 数据中提取水平曲线元素的网络级检测框架。所提出的技术经过验证，然后在加拿大艾伯塔省 242 公里高速公路上收集的 LiDAR 数据上进行测试。该算法成功地检测了测试高速公路上的所有曲线，并以 96% 到 100% 的准确度估计了它们的属性，证明了提取方法的鲁棒性和在如此大规模执行提取的可行性。所提出的方法是一种替代方法，可以帮助运输机构大规模维护水平路线信息的更新清单。