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An integrated solution based irregul...

Sun, Rui.

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An integrated solution based irregular driving detection

Record Type:	Electronic resources : Monograph/item
Title/Author:	An integrated solution based irregular driving detection/ by Rui Sun.
Author:	Sun, Rui.
Published:	Cham :Springer International Publishing : : 2017.,
Description:	xxviii, 127 p. :ill. (some col.), digital ;24 cm.
[NT 15003449]:	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.
Contained By:	Springer eBooks
Subject:	Intelligent transportation systems. -
Online resource:	http://dx.doi.org/10.1007/978-3-319-44926-5
ISBN:	9783319449265

An integrated solution based irregular driving detection
Sun, Rui.

An integrated solution based irregular driving detection[electronic resource] /by Rui Sun. - Cham :Springer International Publishing :2017. - xxviii, 127 p. :ill. (some col.), digital ;24 cm. - Springer theses,2190-5053. - Springer theses..

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.

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.

ISBN: 9783319449265

Standard No.: 10.1007/978-3-319-44926-5doiSubjects--Topical Terms:

1066364
Intelligent transportation systems.

LC Class. No.: TE228.3

Dewey Class. No.: 388.312

An integrated solution based irregular driving detection
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245 1 3 $a An integrated solution based irregular driving detection $h [electronic resource] / $c by Rui Sun.
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505 0 $a 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.
520 $a 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.
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