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Detection of Dangerous Cornering in GNSS-Data-Driven Insurance Telematics
KTH, School of Electrical Engineering (EES), Signal Processing. KTH, School of Electrical Engineering (EES), Centres, ACCESS Linnaeus Centre.
KTH, School of Electrical Engineering (EES), Signal Processing. KTH, School of Electrical Engineering (EES), Centres, ACCESS Linnaeus Centre.ORCID iD: 0000-0002-3054-6413
KTH, School of Electrical Engineering (EES), Signal Processing. KTH, School of Electrical Engineering (EES), Centres, ACCESS Linnaeus Centre.ORCID iD: 0000-0002-2718-0262
2015 (English)In: IEEE transactions on intelligent transportation systems (Print), ISSN 1524-9050, E-ISSN 1558-0016, Vol. 16, no 6Article in journal (Refereed) PublishedText
Abstract [en]

We propose a framework for the detection of dangerous vehicle cornering events, based on statistics related to the no-sliding and no-rollover conditions. The input variables are estimated using an unscented Kalman filter applied to global navigation satellite system (GNSS) measurements of position, speed, and bearing. The resulting test statistic is evaluated in a field study where three smartphones are used as measurement probes. A general framework for performance evaluation and estimator calibration is presented as depending on a generic loss function. Furthermore, we introduce loss functions designed for applications aiming to either minimize the number of missed detections and false alarms, or to estimate the risk level in each cornering event. Finally, the performance characteristics of the estimator are presented as depending on the detection threshold, as well as on design parameters describing the driving behavior. Since the estimation only uses GNSS measurements, the framework is particularly well suited for smartphone-based insurance telematics applications, aiming to avoid the logistic and monetary costs associated with, e.g., on-board-diagnostics or black-box dependent solutions. The design of the estimation algorithm allows for instant feedback to be given to the driver and, hence, supports the inclusion of real-time value-added services in usage-based insurance programs.

Place, publisher, year, edition, pages
IEEE , 2015. Vol. 16, no 6
Keyword [en]
UBI, insurance telematics, GNSS, vehicle lateral forces, unscented Kalman filtering
National Category
Electrical Engineering, Electronic Engineering, Information Engineering
Identifiers
URN: urn:nbn:se:kth:diva-180244DOI: 10.1109/TITS.2015.2431293ISI: 000365958500009ScopusID: 2-s2.0-84961056641OAI: oai:DiVA.org:kth-180244DiVA: diva2:891905
Note

QC 20160108

Available from: 2016-01-08 Created: 2016-01-08 Last updated: 2016-01-08Bibliographically approved

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Wahlström, JohanSkog, IsaacHändel, Peter
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Signal ProcessingACCESS Linnaeus Centre
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Electrical Engineering, Electronic Engineering, Information Engineering

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