Extracting information from driving data using k-means clustering

Research output: Chapter in Book/Report/Conference proceedingConference paperpeer-review

Abstract

There is an increasing availability of data, but for making decisions and other tasks we need information. Hence, we require to analyze the data and extract parts or come up with relations between different pieces. In this paper, we focus on information extraction within the automotive industry. In particular, we report on applying k-means clustering for identifying episodes in vehicle data. An episode is considered to be a time interval where a vehicle is performing an activity worth being distinguished. The underlying idea is to cluster the data such that we are able to extract such similar situations like breaking before a crossing only considering vehicle data. We discuss a method that allows extracting such episodes capturing actuator and sensor readings over time. Besides introducing the underlying method, we present obtained empirical results making use of a freely available dataset showing that the extracted episodes have indeed a meaningful interpretation.

Original languageEnglish
Title of host publicationProceedings - SEKE 2021
Subtitle of host publication33rd International Conference on Software Engineering and Knowledge Engineering
PublisherKnowledge Systems Institute Graduate School
Pages610-615
Number of pages6
ISBN (Electronic)1891706527
DOIs
Publication statusPublished - 2021
Externally publishedYes
Event33rd International Conference on Software Engineering and Knowledge Engineering: SEKE 2021 - Pittsburgh, United States
Duration: 1 Jul 202110 Jul 2021

Publication series

NameProceedings of the International Conference on Software Engineering and Knowledge Engineering, SEKE
Volume2021-July
ISSN (Print)2325-9000
ISSN (Electronic)2325-9086

Conference

Conference33rd International Conference on Software Engineering and Knowledge Engineering
Country/TerritoryUnited States
CityPittsburgh
Period1/07/2110/07/21

ASJC Scopus subject areas

  • Software

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