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Verlagsausgabe
DOI: 10.5445/IR/1000076070#verlagsausgabe
Originalveröffentlichung
DOI: 10.1016/j.jtte.2017.06.003

Learning from the crowd: Road infrastructure monitoring system

Masino, Johannes; Thumm, Jakob; Frey, Michael Z.; Gauterin, Frank

Abstract:
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 ... mehr


Zugehörige Institution(en) am KIT Institut für Fahrzeugsystemtechnik (FAST)
Publikationstyp Zeitschriftenaufsatz
Jahr 2017
Sprache Englisch
Identifikator ISSN: 2095-7564
URN: urn:nbn:de:swb:90-760704
KITopen ID: 1000076070
Erschienen in Journal of traffic and transportation engineering
Band 4
Heft 5
Seiten 451-463
Schlagworte Road infrastructure condition, Monitoring, Tree graphs, Euclidean distance, Machine learning, Classification
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