Neural Network Based Vehicular Location Prediction Model for Cooperative Active Safety Systems
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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.
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Kawashima H. Japanese perspective of driver information systems. Transportation. 1990;17(3): 263-284.
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.
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.
Jarasuniene A, Jakubauskas G. Improvement of road safety using passive and active intelligent vehicle safety systems. Transport. 2007;22(4): 284-289.
Andersen J, Kalra N, Stanley K, Sorensen P, Samaras C, Oluwatola O. Autonomous vehicle technology. 1st ed. Santa Monica, CA: RAND; 2016.
Misener JA. Vehicle-infrastructure integration (VII) and safety: rubber and radio meets the road in California. Intellimotion. 2005:11(2): 1-3.
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.
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.
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.
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.
Hartenstein H, Laberteaux K. Vehicular applications and inter-networking technologies. 1st ed. Oxford: Wiley- Blackwell; 2010.
Caveney D. Collision avoidance enabled through geospatial positioning and inter-vehicular communications. IEEE Control Systems Magazine. 2010;30(4): 38-53.
Lefèvre S, Vasquez D, Laugier C. A survey on motion prediction and risk assessment for intelligent vehicles. Robomech Journal. 2014;1(1): 1-14.
Sorenson HW. Kalman Filtering: Theory and Application. 1st ed. New York: IEEE Press; 1985.
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.
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.
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.
Hsu L, Chen T. Vehicle Full-State Estimation and Prediction System Using State Observers. Vehicular Technology. 2009;58(6): 2651-2662.
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.
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.
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.
Haklay MM, Weber P. Openstreetmap: User-generated street maps. IEEE Pervasive Computing. 2008;7(4): 12-18.
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.
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.
Turkmen I, Guney K, Karaboga D. Genetic Tracker with Neural Network for Single and Multiple Target Tracking. Neurocomputing. 2006;69(16-18): 2309-2319.
Duh FB, Lin CT. Tracking a maneuvering target using neural fuzzy network systems. Man and Cybernetics Part B: Cybernetics. 2004;34(1): 16-33.
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.
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.
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.
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.
Hagan MT, Menhaj MB. Training Feedforward Networks with the Marquardt Algorithm. IEEE Transactions on Neural Networks. 1994;5(6): 989-993.
Ampazis N, Perantonis SJ. Two highly efficient second-order algorithms for training feedforward networks. IEEE Transactions on Neural Networks. 2002;13(5): 1064-1074.
Demuth H, Beale M, Hagan M. Neural network toolbox. 9st ed. Natick, Mass.: MathWorks; 2010.
Caveney D. Cooperative Vehicular Safety Applications. IEEE Control Systems. 2010;30(4): 38-53.
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.
