dc.contributor.author | Wahlstrom, J | |
dc.contributor.author | Skog, I | |
dc.contributor.author | Handel, P | |
dc.contributor.author | Bradley, B | |
dc.contributor.author | Madden, S | |
dc.contributor.author | Balakrishnan, H | |
dc.date.accessioned | 2020-07-22T14:22:56Z | |
dc.date.issued | 2020-02-18 | |
dc.description.abstract | Smartphone-based driver monitoring is quickly gaining ground as a feasible alternative to competing in-vehicle and aftermarket solutions. Currently the main challenges for data analysts studying smartphone-based driving data stem from the mobility of the smartphone. In this paper, we use kernel-based k-means clustering to infer the placement of smartphones within vehicles. The trip segments are mapped into fifteen different placement clusters. As a part of the presented framework, we discuss practical considerations concerning e.g., trip segmentation, cluster initialization, and parameter selection. The proposed method is evaluated on more than 10 000 kilometers of driving data collected from approximately 200 drivers. To validate the interpretation of the clusters, we compare the data associated with different clusters and relate the results to real-world knowledge of driving behavior. The clusters associated with the label “Held by hand” are shown to display high gyroscope variances, low maximum speeds, low correlations between the measurements from smartphone-embedded and vehicle-fixed accelerometers, and short segment durations. | en_GB |
dc.identifier.citation | Vol. 21, pp. 669 - 679 | en_GB |
dc.identifier.doi | 10.1109/tits.2019.2896708 | |
dc.identifier.uri | http://hdl.handle.net/10871/122083 | |
dc.language.iso | en | en_GB |
dc.publisher | Institute of Electrical and Electronics Engineers (IEEE) | en_GB |
dc.rights | © 20xx IEEE. Personal use of this material is permitted. Permission from IEEE must be
obtained for all other uses, in any current or future media, including
reprinting/republishing this material for advertising or promotional purposes, creating new
collective works, for resale or redistribution to servers or lists, or reuse of any copyrighted
component of this work in other works. | en_GB |
dc.subject | Telematics | en_GB |
dc.subject | inertial sensors | en_GB |
dc.subject | smartphones | en_GB |
dc.subject | kernel-based k-means clustering | en_GB |
dc.title | Smartphone placement within vehicles | en_GB |
dc.type | Article | en_GB |
dc.date.available | 2020-07-22T14:22:56Z | |
dc.identifier.issn | 1524-9050 | |
dc.description | This is the author accepted manuscript. The final version is available from the publisher via the DOI in this record | en_GB |
dc.identifier.journal | IEEE Transactions on Intelligent Transportation Systems | en_GB |
dc.rights.uri | http://www.rioxx.net/licenses/all-rights-reserved | en_GB |
dcterms.dateAccepted | 2019-01-28 | |
rioxxterms.version | AM | en_GB |
rioxxterms.licenseref.startdate | 2020-02-18 | |
rioxxterms.type | Journal Article/Review | en_GB |
refterms.dateFCD | 2020-07-22T14:21:44Z | |
refterms.versionFCD | AM | |
refterms.dateFOA | 2020-07-22T14:23:02Z | |
refterms.panel | B | en_GB |