Robust Inference of Principal Road Paths for Intelligent Transportation Systems

Over the last few years, electronic vehicle guidance systems have become increasingly more popular. However, despite their ubiquity, performance will always be subject to availability of detailed digital road maps. Most current digital maps are still inadequate for advanced applications in unstructured environments. Lack of up-to-date information and insufficient refinement of the road geometry are among the most important shortcomings. The massive use of inexpensive Global Positioning System (GPS) receivers, combined with the rapidly increasing availability of wireless communication infrastructure, suggests that large amounts of data combining both modalities will be available in the near future. The approach presented here draws on machine-learning techniques and processes logs of position traces to consistently build a detailed and fine-grained representation of the road network by extracting the principal paths followed by the vehicles. Although this work addresses the road-building problem in dynamic environments such as open-pit mines, it is also applicable to urban environments. New contributions include a fully unsupervised segmentation method for sampling roads and inferring the network topology, which is a general technique for extracting detailed information about road splits, merges, and intersections, as well as a robust algorithm that articulates these two. Experimental results with data from large mining operations are presented to validate the new algorithm.