Neural Network Based Vehicular Location Prediction Model for Cooperative Active Safety Systems

  • Murat Dörterler Gazi University, Faculty of Technology, Department of Computer Engineering
  • Ömer Faruk Bay Gazi University, Faculty of Technology, Department of Electrical - Electronics Engineering
Keywords: cooperative active safety systems, inter-vehicular communication, vehicular location prediction, artificial neural networks,

Abstract

Safety systems detect unsafe conditions and provide warnings for travellers to take action and avoid crashes. Estimation of the geographical location of a moving vehicle as to where it will be positioned next with high precision and short computation time is crucial for identifying dangers. To this end, navigational and dynamic data of a vehicle are processed in connection with the data received from neighbouring vehicles and infrastructure in the same vicinity. In this study, a vehicular location prediction model was developed using an artificial neural network for cooperative active safety systems. The model is intended to have a constant, shorter computation time as well as higher accuracy features. The performance of the proposed model was measured with a real-time testbed developed in this study. The results are compared with the performance of similar studies and the proposed model is shown to deliver a better performance than other models.

Author Biographies

Murat Dörterler, Gazi University, Faculty of Technology, Department of Computer Engineering
Murat DÖRTERLER, Received the BS degree in Departmant of Electronics and Computer from Gazi University in 2005. He received MSc and PhD degrees in Electronics-Computer from Gazi University 2008 and 2013 respectively. He is currently working as researcher at  Department of Computer, EngineeringFaculty of Technology, Gazi University.

His research interests in Intelligent Transportation Systems, Artificial Intelligence, Cloud computing system, Embedded Systems, Internet of Things, Internet Technologies, Vehicular Ad Hoc Networks.
Ömer Faruk Bay, Gazi University, Faculty of Technology, Department of Electrical - Electronics Engineering
Omer Faruk BAY, Received the BS degree in Electrical - Electronics from Gazi University in 1985.  He studied Electrical Engineering Technology at Purdue University in USA for 9 months in 1990. He received MSc and PhD degrees in Electrical - Electronics Engineering from Erciyes University in 1992 and 1996 respectively. He is currently professor at Department of Electrical - Electronics, Faculty of Technology, Gazi University.

References

[1] Kawashima H. Japanese perspective of driver information systems. Transportation. 1990;17(3): 263-284.

[2] Baldessari R, Bödekker B, Deegener M, et al. Car to Car Communication Consortium Manifesto: Overview of the C2C-CC System. Car to Car Communication Consortium; 2007.

[3] Harding J, Powell G, Yoon R, et al. Vehicle-to-vehicle communications: Readiness of V2V technology for application. Washington, DC: National Highway Traffic Safety Administration; 2014. p. 1-14.

[4] Jarasuniene A, Jakubauskas G. Improvement of road safety using passive and active intelligent vehicle safety systems. Transport. 2007;22(4): 284-289.

[5] Andersen J, Kalra N, Stanley K, Sorensen P, Samaras C, Oluwatola O. Autonomous vehicle technology. 1st ed. Santa Monica, CA: RAND; 2016.

[6] Misener JA. Vehicle-infrastructure integration (VII) and safety: rubber and radio meets the road in California. Intellimotion. 2005:11(2): 1-3.

[7] Vanajakshi L, Subramanian SC, Sivanandan R. Travel time prediction under heterogeneous traffic conditions using global positioning system data from buses. IET Intelligent Transport Systems. 2009;3(1): 1-9.

[8] Barimani N, Rahimi KA, Moshiri B. Real time adaptive non-linear estimator/predictor design for traffic systems with inadequate detectors. IET Intelligent Transport Systems. 2014;8(3): 308-321.

[9] Lu G, Kong L, Wang Y, Tian D. Vehicle trajectory extraction by simple two-dimensional model matching at low camera angles in intersection. IET Intelligent Transport Systems. 2014;8(7): 631-638.

[10] Dhaouadi R, Mohan N, Norum L. Design and implementation of an extended Kalman filter for the state estimation of a permanent magnet synchronous motor. IEEE Transactions on Power Electronics. 1991;6 (3): 491-497.

[11] Hartenstein H, Laberteaux K. Vehicular applications and inter-networking technologies. 1st ed. Oxford: Wiley- Blackwell; 2010.

[12] Caveney D. Collision avoidance enabled through geospatial positioning and inter-vehicular communications. IEEE Control Systems Magazine. 2010;30(4): 38-53.

[13] Lefèvre S, Vasquez D, Laugier C. A survey on motion prediction and risk assessment for intelligent vehicles. Robomech Journal. 2014;1(1): 1-14.

[14] Sorenson HW. Kalman Filtering: Theory and Application. 1st ed. New York: IEEE Press; 1985.

[15] Lin CF, Ulsoy AG, LeBlanc DJ. Vehicle dynamics and external disturbance estimation for vehicle path prediction. IEEE Transactions on Control Systems Technology. 2000;8(3): 508-518.

[16] Huang J, Tan HS. Vehicle Future Trajectory Prediction with A DGPS/INS-Based Positioning System. Proceedings of the American Control Conference, Jun 2006, Minneapolis. USA. IEEE; 2006.

[17] Tan H-S, Huang J. DGPS-based vehicle-to-vehicle cooperative collision warning: Engineering feasibility Viewpoints. IEEE Transactions on Intelligent Transportation Systems. 2006;7(4): 415-428.

[18] Hsu L, Chen T. Vehicle Full-State Estimation and Prediction System Using State Observers. Vehicular Technology. 2009;58(6): 2651-2662.

[19] Barrios C, Motai Y. Improving estimation of vehicle's trajectory using the latest global positioning system with Kalman filtering. IEEE Transactions on Instrumentation and Measurement. 2011;60(12): 3747-3755.

[20] Feng H, Liu C, Shu Y, Yang OW. Location prediction of vehicles in VANETs using a Kalman filter. Wireless Personal Communications. 2015;80(2): 543-559.

[21] Jaiswal RK, Jaidhar CD. Location prediction algorithm for a nonlinear vehicular movement in VANET using extended Kalman filter. Wireless Networks. 2017;23(7): 2021-2036.

[22] Haklay MM, Weber P. Openstreetmap: User-generated street maps. IEEE Pervasive Computing. 2008;7(4): 12-18.

[23] Härri J, Filali F, Bonnet C, Fiore M. Vanetmobisim: Generating realistic mobility patterns for VANETs. Proceedings of the 3rd International workshop on vehicular ad hoc networks, Sep 24-29 2006, Los Angeles, CA, USA. New York: ACM, 2006.

[24] Caveney D. Stochastic Path Prediction using the Unscented Transform with Numerical Integration, Proceedings of the IEEE Intelligent Transportatıon Systems Conference, Sep 30-Oct 3 2007, Bellevue, WA, USA. Los Alamitos: IEEE; 2007.

[25] Turkmen I, Guney K, Karaboga D. Genetic Tracker with Neural Network for Single and Multiple Target Tracking. Neurocomputing. 2006;69(16-18): 2309-2319.

[26] Duh FB, Lin CT. Tracking a maneuvering target using neural fuzzy network systems. Man and Cybernetics Part B: Cybernetics. 2004;34(1): 16-33.

[27] Zhu AF, Jing ZR, Chen WJ. Maneuvering Target Tracking Based on ANFIS and UKF. Proceedings of Intelligent Computation Technology and Automation, Oct 20-22 2008, Hunan, China. Los Alamitos: IEEE; 2008.

[28] Wu W, Min W. The Mobile Robot GPS Position Based on Neural Network Adaptive Kalman Filter. Proceedings of Computational Intelligence and Natural Computing, June 6-7 2009, Wuhan, China. Los Alamitos: IEEE; 2009.

[29] Lin C, Henty BE, Cooper R, et al. A Measurement Study of Time-Scaled 802.11a Waveforms Over the Mobile-to-Mobile Vehicular Channel at 5.9 GHz. IEEE Communications Magazine. 2008;46(5): 84-91.

[30] Al-Sultan S, Al-Doori MM, Al-Bayatti AH, Zedan H. A comprehensive survey on vehicular ad hoc network. Journal of Network and Computer Applications. 2014;37: 380-392.

[31] Hagan MT, Menhaj MB. Training Feedforward Networks with the Marquardt Algorithm. IEEE Transactions on Neural Networks. 1994;5(6): 989-993.

[32] Ampazis N, Perantonis SJ. Two highly efficient second-order algorithms for training feedforward networks. IEEE Transactions on Neural Networks. 2002;13(5): 1064-1074.

[33] Demuth H, Beale M, Hagan M. Neural network toolbox. 9st ed. Natick, Mass.: MathWorks; 2010.

[34] Caveney D. Cooperative Vehicular Safety Applications. IEEE Control Systems. 2010;30(4): 38-53.

[35] Ahmed-Zaid F, Bai F, Bai S, et al. Vehicle Safety Communications – Applications (VSC-A). Final Report: Appendix
Volume 1 System Design and Objective Test. CAMP DOT HS 811 492B. Washington, DC: US National Highway Traffic Safety Administration; 2014.
Published
2018-04-20
How to Cite
1.
Dörterler M, Bay Ömer. Neural Network Based Vehicular Location Prediction Model for Cooperative Active Safety Systems. Promet - Traffic & Transportation [Internet]. 20Apr.2018 [cited 23Oct.2018];30(2):205-1. Available from: http://www.fpz.unizg.hr/traffic/index.php/PROMTT/article/view/2500
Section
Articles