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Verlagsausgabe
DOI: 10.5445/IR/1000082020
Veröffentlicht am 12.04.2018
Originalveröffentlichung
DOI: 10.1080/23311916.2018.1449428

Multiple vehicle fusion for a robust road condition estimation based on vehicle sensors and data mining

Hofmockel, Julia; Masino, Johannes; Thumm, Jakob; Sax, Eric; Gauterin, Frank

Abstract:
The road condition is an important factor for driving comfort and has impact on safety, economy and health. Delayed detection of defects lead to renewals which yields to complete roadblocks or traffic jams. Therefore, an early identification of road defects is desirable. Novel condition monitoring systems employ vehicles as sensor platforms and apply machine learning methods to predict the road condition based on the sensor data. The paper addresses the question how to combine the classification results from different vehicles to improve the final prediction. Different fusion strategies are investigated in various scenarios in a novel simulation. It is demonstrated that the performance of the classification can be improved compared to a majority vote or only considering one vehicle by taking the probability for the prediction of each vehicle into account. The probabilities follow a multinomial distribution and the precision matrix of the classifiers provide the best parameters. Overall, the results show that the application of the presented fusion strategies on road condition estimation greatly improve the performance and guarantee ... mehr


Zugehörige Institution(en) am KIT Institut für Fahrzeugsystemtechnik (FAST)
Institut für Technik der Informationsverarbeitung (ITIV)
Publikationstyp Zeitschriftenaufsatz
Jahr 2018
Sprache Englisch
Identifikator ISSN: 2331-1916
URN: urn:nbn:de:swb:90-820207
KITopen ID: 1000082020
Erschienen in Cogent Engineering
Band 5
Heft 1
Seiten Article: 14494278
Bemerkung zur Veröffentlichung Gefördert durch den KIT-Publikationsfonds
Schlagworte road infrastructure monitoring; multiple expert problem; multinomial distribution; classification; vehicle sensors
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