Robust Inference of Principal Road Paths for Intelligent Transportation Systems

Abstract—Over the last few years, electronic vehicle guidance systems have become increasingly more popular. However, despite their ubiquity, performance will always be subject to the 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 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 a 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...

• Type: Journal
• Year: 2011
• Where: TITS
• Authors: Gabriel Agamennoni, Juan Nieto, Eduardo Mario Nebot
• Keywords: Detailed Digital Road, Electronic Vehicle Guidance, Road Building Problem, TITS 2011