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A New Algorithm for Lane Level Irregular Driving Identification

Published online by Cambridge University Press:  07 July 2015

Rui Sun ,
Washington Ochieng ,
Cheng Fang  and
Shaojun Feng
Rui Sun*
Affiliation:
(College of Civil Aviation, Nanjing University of Aeronautics and Astronautics, China) (Centre for Transport Studies, Imperial College London, SW7 2AZ, United Kingdom)
Washington Ochieng
Affiliation:
(Centre for Transport Studies, Imperial College London, SW7 2AZ, United Kingdom)
Cheng Fang
Affiliation:
(School of Civil Engineering and Geosciences, Newcastle University, Newcastle upon Tyne, NE1 7RU, United Kingdom)
Shaojun Feng
Affiliation:
(Centre for Transport Studies, Imperial College London, SW7 2AZ, United Kingdom)
*
(E-mail: [email protected])
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Abstract

Global Navigation Satellite Systems (GNSS) are used widely in the provision of Intelligent Transportation System (ITS) services. Today, there is an increasing demand on GNSS to support applications at lane level. These applications required at lane level include lane control, collision avoidance and intelligent speed assistance. In lane control, detecting irregular driving behaviour within the lane is a basic requirement for safety related lane level applications. There are two major issues involved in lane level irregular driving identification: access to high accuracy positioning and vehicle dynamic parameters, and extraction of erratic driving behaviour from this and other related information. This paper proposes an integrated algorithm for lane level irregular driving identification. Access to high accuracy positioning is enabled by GNSS and its integration with an Inertial Navigation System (INS) using filtering with precise vehicle motion models and lane information. The identification of irregular driving behaviour is achieved by algorithms developed for different types of events based on the application of a Fuzzy Inference System (FIS). The results show that decimetre level accuracy can be achieved and that different types of lane level irregular driving behaviour can be identified.

Keywords

TrajectoryAlgorithmHigh accuracyGNSS
Type
Research Article
Information
The Journal of Navigation , Volume 68 , Issue 6 , November 2015 , pp. 1173 - 1194
Copyright
Copyright © The Royal Institute of Navigation 2015 

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References

REFERENCES

Albu, A. B., Widsten, B., Wang, T., Lan, J. and Mah, I. (2008). A Computer Vision-Based System for Real-Time Detection of Sleep Onset in Fatigued Drivers. 2008 IEEE Intelligent Vehicles Symposium, 25–30.CrossRefGoogle Scholar
Aljaafreh, A., Alshabatat, N. and Najim Al-Din, M.S. (2012). Driving Style Recognition Using Fuzzy Logic. 2012 IEEE International Conference on Vehicular Electronics and Safety (ICVES), 460–463.CrossRefGoogle Scholar
Aljaafresh, A. (2012). Web Driving Performance Monitoring System. World Academy of Science, Engineering and Technology, 70.Google Scholar
Barrios, C., Himberg, H., Motai, Y. and Sadek, A. (2006). Multiple Model Framework of Adaptive Extended Kalman Filtering for Predicting Vehicle Location. Proceedings of IEEE ITSC, Toronto, ON, Canada, 2006, 1053–1059.CrossRefGoogle Scholar
Chang, T. H., Hsu, C. S., Wang, C. and Yang, L. K. (2008). Onboard Measurement and Warning Module for Irregular Vehicle Behavior. IEEE Transactions on Intelligent Transportation Systems, 9, 501–513.CrossRefGoogle Scholar
Clanton, J., Bevly, D. and Hodel, A. (2009). A Low-cost Solution for an Integrated Multisensory Lane Departure Warning System. IEEE Transactions on Intelligent Transportation Systems, 10, 47–59.CrossRefGoogle Scholar
Dai, J., Teng, J., Bai, X., Shen, Z. and Xuan, D. (2010). Mobile Phone Based Drunk Driving Detection, 2010 4th International Conference on Pervasive Computing Technologies for Healthcare (Pervasive Health), 1.CrossRefGoogle Scholar
Desai, A. V. and Haque, M. A. (2006). Vigilance Monitoring for Operator Safety: A Simulation Study on Highway Driving. Journal of Safety Research, 37, 139–147.CrossRefGoogle Scholar
Eriksson, M. and Papanikolopoulos, N. P. (2001). Driver fatigue: A Vision-based Approach to Automatic Diagnosis. Transport Research Part C, Emerging Technologies, 9, 399–413.CrossRefGoogle Scholar
Gustafsson, F., Gunnarsson, F., Bergman, N., Forssell, U., Jansson, J., Karlsson, R. and Nordlund., P-J. (2002). Particle Filters for Positioning, Navigation and Tracking. IEEE Transactions on Signal Processing, 50, 425–435.CrossRefGoogle Scholar
Heitmann, A., Cuttkuhn, R., Aguirre, A., Trutschel, U. and Moore-Ede, M. (2001). Technologies for The Monitoring and Prevention of Driver Fatigue. Proceedings of the Fifth International Driving Symposium on Human Factors in Driver Assessment, Training and Vehicle Design, 81–86.CrossRefGoogle Scholar
Imkamon, T., Saensom, P., Tangamchit, P. and Pongpaibool, P. (2008). Detection of hazardous driving behavior using fuzzy logic. 5th International Conference on Electrical Engineering/Electronics, Computer, Telecommunications and Information Technology ECTI-CON 2008, 2, 657.CrossRefGoogle Scholar
Kilbey, P. (2013). Reported Road Casualties in Great Britain: 2012 Annual Report. Department of Transport.Google Scholar
Krajewski, J., Sommer, D., Trutschel, U., Edwards, D. and Golz, M. (2009). Steering Wheel Behavior Based Estimation of Fatigue. Proceedings of the Fifth International Driving Symposium on Human Factors in Driver Assessment, Training and Vehicle Design, 118–124.CrossRefGoogle Scholar
Lecce, V. D. and Calabrese, M. (2008). Experimental System to Support Real Time Driving Pattern Recognition. Advanced Intelligent Computing Theories and Applications with Aspects of Artificial Intelligence Annals of Emergency Medicine, 1192–1199.Google Scholar
Lee, J. D., Li, J. D., Liu, L. C. and Chen, C. M. (2006). A Novel Driving Pattern Recognition and Status Monitoring System. Advances in Image and Video Technology, ser. Lecture Notes in Computer Science, Chang, L.-W. and Lie, W.-N., Eds. Springer Berlin / Heidelberg, 4319, 504–512.CrossRefGoogle Scholar
Mohinder, S. G. and Angus, P. A. (2008). Kalman Filtering: Theory and Practice Using MATLAB, Wiley-IEEE Press, 592.Google Scholar
Mohamad, I., Ali, M. and Ismail, M. (2011). Abnormal Driving Detection Using Real Time Global Positioning System Data. 2011 IEEE International Conference on Space Science and Communication (IconSpace), 1.CrossRefGoogle Scholar
NHTSA. (2012). from http://www.nhtsa.gov/Google Scholar
Omidyeganeh, M., Javadtalab, A. and Shirmohammadi, S. (2011). Intelligent Driver Drowsiness Detection through Fusion of Yawning and Eye Closure. 2011 IEEE International Conference on Virtual Environments Human-Computer Interfaces and Measurement Systems (VECIMS), 1–6.CrossRefGoogle Scholar
PTV. (2014). from http://vision-traffic.ptvgroup.com/en-uk/products/ptv-vissim/Google Scholar
Sandberg, D., Akerstedt, T., Anund, A., Kecklund, G. and Wahde, M. (2011). Detecting Driver Sleepiness Using Optimized Nonlinear Combinations of Sleepiness Indicators. IEEE Transactions on Intelligent Transportation Systems, 12, 97–108.CrossRefGoogle Scholar
Sultani, W. and Choi, J. Y. (2010). Abnormal Traffic Detection using Intelligent Driver Model. 2010 International Conference on Pattern Recognition.CrossRefGoogle Scholar
Toledo-Moreo, R., Bétaille, D. and Peyret, F. (2010). Lane-level Integrity Provision for Navigation and Map Matching with GNSS, Dead Reckoning and Enhanced Maps. IEEE Transactions on Intelligent Transportation Systems, 11, 100–112.CrossRefGoogle Scholar
Tsogas, M., Polychronopoulos, A. and Amditis, A. (2005). Unscented Kalman Filter Design for Curvilinear Motion Models Suitable for Automotive Safety Applications. Proceedings of 7th International IEEE Conference on Information Fusion, 1295–1302.CrossRefGoogle Scholar
Zhu, Z. and Ji, Q. (2004). Real Time and Non-intrusive Driver Fatigue Monitoring. Proceedings of 7th International IEEE Conference on Intelligent Transportation Systems, 657–662.Google Scholar