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Learning from the crowd: Road infrastructure monitoring system

Masino, Johannes 1; Thumm, Jakob 1; Frey, Michael Z. ORCID iD icon 1; Gauterin, Frank ORCID iD icon 1
1 Institut für Fahrzeugsystemtechnik (FAST), Karlsruher Institut für Technologie (KIT)


The condition of the road infrastructure has severe impacts on the road safety, driving comfort, and on the rolling resistance. Therefore, the road infrastructure must be monitored comprehensively and in regular intervals to identify damaged road segments and road hazards.
Methods have been developed to comprehensively and automatically digitize the road infrastructure and estimate the road quality, which are based on vehicle sensors and a supervised machine learning classification. Since different types of vehicles have various suspension systems with different response functions, one classifier cannot be taken over to other vehicles. Usually, a high amount of time is needed to acquire training data for each individual vehicle and classifier.
To address this problem, the methods to collect training data automatically for new vehicles based on the comparison of trajectories of untrained and trained vehicles have been developed. The results show that the method based on a k-dimensional tree and Euclidean distance performs best and is robust in transferring the information of the road surface from one vehicle to another. Furthermore, this method offers the possibility to merge the output and road infrastructure information from multiple vehicles to enable a more robust and precise prediction of the ground truth.

Verlagsausgabe §
DOI: 10.5445/IR/1000076070
Veröffentlicht am 20.11.2017
DOI: 10.1016/j.jtte.2017.06.003
Zitationen: 24
Zitationen: 19
Cover der Publikation
Zugehörige Institution(en) am KIT Institut für Fahrzeugsystemtechnik (FAST)
Publikationstyp Zeitschriftenaufsatz
Publikationsjahr 2017
Sprache Englisch
Identifikator ISSN: 2095-7564
KITopen-ID: 1000076070
Erschienen in Journal of traffic and transportation engineering
Verlag Elsevier
Band 4
Heft 5
Seiten 451-463
Schlagwörter Road infrastructure condition, Monitoring, Tree graphs, Euclidean distance, Machine learning, Classification
Nachgewiesen in Dimensions
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