Imperial College London
Portrait Picture

Professor Washington Yotto Ochieng, EBS, FREng

Faculty of Engineering, Department of Civil and Environmental Engineering

Head of Department of Civil and Environmental Engineering
 
 
 
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Contact

 

+44 (0)20 7594 6104w.ochieng Website

 
 
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Assistant

 

Ms Maya Mistry +44 (0)20 7594 6100

 
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Location

 

441/442Skempton BuildingSouth Kensington Campus

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Summary

 

Publications

Citation

BibTex format

@article{Sun:2016:10.1016/j.trc.2016.06.006,
author = {Sun, R and Han, K and Hu, J and Wang, Y and Hu, M and Ochieng, W},
doi = {10.1016/j.trc.2016.06.006},
journal = {Transportation Research Part C: Emerging Technologies},
pages = {193--207},
title = {Integrated solution for anomalous driving detection based on BeiDou/GPS/IMU measurements},
url = {http://dx.doi.org/10.1016/j.trc.2016.06.006},
volume = {69},
year = {2016}
}
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RIS format (EndNote, RefMan)

TY  - JOUR
AB  - There has been an increasing role played by Global Navigation Satellite Systems (GNSS) in Intelligent Transportation System (ITS) applications in recent decades. In particular, centimetre/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.
AU  - Sun,R
AU  - Han,K
AU  - Hu,J
AU  - Wang,Y
AU  - Hu,M
AU  - Ochieng,W
DO  - 10.1016/j.trc.2016.06.006
EP  - 207
PY  - 2016///
SN  - 1879-2359
SP  - 193
TI  - Integrated solution for anomalous driving detection based on BeiDou/GPS/IMU measurements
T2  - Transportation Research Part C: Emerging Technologies
UR  - http://dx.doi.org/10.1016/j.trc.2016.06.006
UR  - http://hdl.handle.net/10044/1/33412
VL  - 69
ER  -
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