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An Integrated Solution Based Irregular Driving Detection [electronic resource] / by Rui Sun.

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dc.contributor.author Sun, Rui. author.
dc.contributor.author SpringerLink (Online service)
dc.date.accessioned 2017-12-02T10:53:39Z
dc.date.available 2017-12-02T10:53:39Z
dc.date.created 2017.
dc.date.issued 2017
dc.identifier.isbn 9783319449265
dc.identifier.uri http://dspace.conacyt.gov.py/xmlui/handle/123456789/19235
dc.description XXVIII, 127 p. 84 illus., 75 illus. in color.
dc.description.abstract This thesis introduces a new integrated algorithm for the detection of lane-level irregular driving. To date, there has been very little improvement in the ability to detect lane level irregular driving styles, mainly due to a lack of high performance positioning techniques and suitable driving pattern recognition algorithms. The algorithm combines data from the Global Positioning System (GPS), Inertial Measurement Unit (IMU) and lane information using advanced filtering methods. The vehicle state within a lane is estimated using a Particle Filter (PF) and an Extended Kalman Filter (EKF). The state information is then used within a novel Fuzzy Inference System (FIS) based algorithm to detect different types of irregular driving. Simulation and field trial results are used to demonstrate the accuracy and reliability of the proposed irregular driving detection method.
dc.description.tableofcontents Table of Contents -- Acknowledgements -- Declaration of Contribution -- Copyright Declaration -- Abstract.-Chapter 1 Introduction -- Chapter 2 Road Safety and Intelligent Transport Systems -- Chapter 3 State-of-the-art in Irregular Driving Detection -- Chapter 4 A New System for Lane Level Irregular Driving Detection.-Chapter 5 Testing, Analysis and Performance Validation -- Chapter 6 Conclusion and Recommendations for Future Work -- Publications Related to This Thesis -- Reference -- APPENDIX 1. Field Test Risk Assessment.
dc.language eng
dc.publisher Cham : Springer International Publishing : Imprint: Springer, 2017.
dc.relation.ispartofseries Springer eBooks
dc.relation.ispartofseries Springer Theses, Recognizing Outstanding Ph.D. Research, 2190-5053
dc.relation.ispartofseries Springer Theses, Recognizing Outstanding Ph.D. Research, 2190-5053
dc.relation.uri http://cicco.idm.oclc.org/login?url=http://dx.doi.org/10.1007/978-3-319-44926-5
dc.subject Engineering.
dc.subject Application software.
dc.subject Control engineering.
dc.subject Quality control.
dc.subject Reliability.
dc.subject Industrial safety.
dc.subject Transportation engineering.
dc.subject Traffic engineering.
dc.subject Engineering.
dc.subject Transportation Technology and Traffic Engineering.
dc.subject Signal, Image and Speech Processing.
dc.subject Quality Control, Reliability, Safety and Risk.
dc.subject Control.
dc.subject Computer Applications.
dc.subject.ddc 629.04 23
dc.subject.lcc TA1001-TA1280
dc.subject.lcc HE331-HE380
dc.subject.other Engineering (Springer-11647)
dc.title An Integrated Solution Based Irregular Driving Detection [electronic resource] / by Rui Sun.
dc.type text
dc.identifier.doi 10.1007/978-3-319-44926-5
dc.identifier.bib 978-3-319-44926-5
dc.format.rdamedia computer
dc.format.rdacarrier online resource
dc.format.rda text file PDF


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