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Risk assessment of vehicle cornering events in GNSS data driven insurance telematics
KTH, School of Electrical Engineering (EES), Signal Processing.
KTH, School of Electrical Engineering (EES), Signal Processing.ORCID iD: 0000-0002-3054-6413
KTH, School of Electrical Engineering (EES), Signal Processing.ORCID iD: 0000-0002-2718-0262
2014 (English)In: Intelligent Transportation Systems (ITSC), 2014 IEEE 17th International Conference on, IEEE conference proceedings, 2014, 3132-3137 p.Conference paper (Refereed)
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. Further, 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, performance characteristics of the estimator is presented as depending on the detection threshold, and 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-basedinsurance programs.

Place, publisher, year, edition, pages
IEEE conference proceedings, 2014. 3132-3137 p.
Keyword [en]
UBI, Insurance telematics, GNSS, Vehicle lateral forces, Unscented Kalman filtering.
National Category
Signal Processing
URN: urn:nbn:se:kth:diva-151384DOI: 10.1109/ITSC.2014.6958194ISI: 000357868703024ScopusID: 2-s2.0-84937111891OAI: diva2:748415
IEEE 17th International Conference on Intelligent Transportation Systems (ITSC), 8-11 Oct. 2014 Qingdao

QC 20150316

Available from: 2014-09-19 Created: 2014-09-19 Last updated: 2015-08-14Bibliographically approved

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