Integrated solution for anomalous driving detection based on BeiDou/GPS/IMU measurements

File Description SizeFormat 
manuscript_vKH.pdfFile embargoed until 11 December 20171 MBAdobe PDF
Title: Integrated solution for anomalous driving detection based on BeiDou/GPS/IMU measurements
Author(s): Sun, R
Han, K
Hu, J
Wang, Y
Hu, M
Ochieng, W
Item Type: Journal Article
Abstract: © 2016 Elsevier Ltd.There has been an increasing role played by Global Navigation Satellite Systems (GNSS) in Intelligent Transportation System (ITS) applications in recent decades. In particular, centimeter/decimetre positioning accuracy is required for some safety related applications, such as lane control, collision avoidance, and intelligent speed assistance. Lane-level Anomalous driving detection underpins these safety-related ITS applications. The two major issues associated with such detection are (1) accessing high accuracy vehicle positioning and dynamic parameters; and (2) extraction of irregular driving patterns from such information. This paper introduces a new integrated framework for detecting lane-level anomalous driving, by combining Global Positioning Systems (GPS), BeiDou, and Inertial Measurement Unit (IMU) with advanced algorithms. Specifically, we use Unscented Particle Filter (UPF) to perform data fusion with different positioning sources. The detection of different types of Anomalous driving is achieved based on the application of a Fuzzy Inference System (FIS) with a newly introduced velocity-based indicator. The framework proposed in this paper yield significantly improved accuracy in terms of positioning and Anomalous driving detection compared to state-of-the-art, while offering an economically viable solution for performing these tasks.
Publication Date: 11-Jun-2016
Date of Acceptance: 7-Jun-2016
URI: http://hdl.handle.net/10044/1/33412
DOI: http://dx.doi.org/10.1016/j.trc.2016.06.006
ISSN: 1879-2359
Publisher: Elsevier
Start Page: 193
End Page: 207
Journal / Book Title: Transportation Research Part C: Emerging Technologies
Volume: 69
Copyright Statement: © 2016 Elsevier. Licensed under the Creative Commons Attribution-NonCommercial-NoDerivatives 4.0 International http://creativecommons.org/licenses/by-nc-nd/4.0/
Keywords: Engineering
Information And Computing Sciences
Commerce, Management, Tourism And Services
Publication Status: Published
Embargo Date: 2017-12-11
Appears in Collections:Faculty of Engineering
Civil and Environmental Engineering



Items in Spiral are protected by copyright, with all rights reserved, unless otherwise indicated.

Creative Commons