A framework to detect horizontal curves and assess their geometric properties from remotely sensed point clouds

Abstract

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.