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020 _a9783319449265
_9978-3-319-44926-5
024 7 _a10.1007/978-3-319-44926-5
_2doi
050 4 _aTA1001-1280
050 4 _aHE331-380
072 7 _aTNH
_2bicssc
072 7 _aTEC009020
_2bisacsh
072 7 _aTNH
_2thema
082 0 4 _a629.04
_223
100 1 _aSun, Rui.
_eauthor.
_4aut
_4http://id.loc.gov/vocabulary/relators/aut
_954136
245 1 3 _aAn Integrated Solution Based Irregular Driving Detection
_h[electronic resource] /
_cby Rui Sun.
250 _a1st ed. 2017.
264 1 _aCham :
_bSpringer International Publishing :
_bImprint: Springer,
_c2017.
300 _aXXVIII, 127 p. 84 illus., 75 illus. in color.
_bonline resource.
336 _atext
_btxt
_2rdacontent
337 _acomputer
_bc
_2rdamedia
338 _aonline resource
_bcr
_2rdacarrier
347 _atext file
_bPDF
_2rda
490 1 _aSpringer Theses, Recognizing Outstanding Ph.D. Research,
_x2190-5061
505 0 _aTable 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.
520 _aThis 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.
650 0 _aTransportation engineering.
_93560
650 0 _aTraffic engineering.
_915334
650 0 _aSignal processing.
_94052
650 0 _aSecurity systems.
_931879
650 0 _aControl engineering.
_931970
650 0 _aApplication software.
_954137
650 1 4 _aTransportation Technology and Traffic Engineering.
_932448
650 2 4 _aSignal, Speech and Image Processing .
_931566
650 2 4 _aSecurity Science and Technology.
_931884
650 2 4 _aControl and Systems Theory.
_931972
650 2 4 _aComputer and Information Systems Applications.
_954138
710 2 _aSpringerLink (Online service)
_954139
773 0 _tSpringer Nature eBook
776 0 8 _iPrinted edition:
_z9783319449258
776 0 8 _iPrinted edition:
_z9783319449272
776 0 8 _iPrinted edition:
_z9783319831640
830 0 _aSpringer Theses, Recognizing Outstanding Ph.D. Research,
_x2190-5061
_954140
856 4 0 _uhttps://doi.org/10.1007/978-3-319-44926-5
912 _aZDB-2-ENG
912 _aZDB-2-SXE
942 _cEBK
999 _c79295
_d79295