Lane-level map-matching based on optimization
Autor: | Martin Meinke, Christoph Stiller, Marc Necker, Johannes Rabe |
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Rok vydání: | 2016 |
Předmět: |
050210 logistics & transportation
Computer science business.industry Reliability (computer networking) 010401 analytical chemistry 05 social sciences ComputerApplications_COMPUTERSINOTHERSYSTEMS Map matching ComputerSystemsOrganization_PROCESSORARCHITECTURES RANSAC 01 natural sciences 0104 chemical sciences law.invention Odometry law 0502 economics and business Trajectory Global Positioning System Computer vision Road map Artificial intelligence Radar business |
Zdroj: | ITSC |
DOI: | 10.1109/itsc.2016.7795702 |
Popis: | We propose a method for ego-lane estimation that can robustly determine the currently used lane as required by future lane-precise navigation systems. It employs a lane-level map-matching on a digital road map through least-squares optimization and only requires sensors available in current production vehicles, such as GPS, odometry, visual lane-marking detection and radars. Radar data is used in a RANSAC-based filtering step for lane hypotheses and, together with camera data, in the determination of the reliability of each lane hypothesis. Detailed evaluation in actual traffic in urban scenarios shows very low error rates below 0.2%. |
Databáze: | OpenAIRE |
Externí odkaz: |
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