Driving in fog condition is dangerous. Fog
causes poor visibility on roads leading to road traffic accident (RTA). RTA in
Albaha is common because of its rough terrain, in addition to the climate that
is mainly rainy and foggy. The rain season in Albaha region begins in October
to February characterized by rainfall and fog. Many studies have reported the
adverse effects of the rain on RTA which results in an increased rate of
crashes. On the other hand, Albaha region is not supported by a proper
intelligent transportation system and infrastructure. Thus, a Driver Assistance
System (DAS) that requires minimum infrastructure is needed. A DAS under fog
called No_Collision has been developed by a researcher in Albaha University.
This paper discusses an implementation of adaptive Kalman Filter by utilizing
Fuzzy logic system with the aim to improve the accuracy of position and
velocity prediction of the No_Collision system. The experiment results show a
promising adaptive system that reduces the error percentage of the prediction
up to 56.58%.
References
[1]
Fahad (2019) Infrastructureless Kalman Filter Based Driving Assistance System under Fog Condition. Journal of Basic and Applied Sciences, 3, 1-6.
[2]
Cruz, S.B., Abrudan, T.E., Xiao, Z., Trigoni, N. and Barros, J. (2017) Neighbor-Aided Localization in Vehicular Networks. IEEE Transactions on Intelligent Transportation Systems, 18, 2693-2702. https://doi.org/10.1109/TITS.2017.2655146
[3]
Negru, M., Benea, V. and Nedevschi, S. (2014) Fog Assistance on Smart Mobile Devices. Proceedings of IEEE International Conference on Intelligent Computer Communication and Processing (ICCP), Cluj Napoca, 4-6 September 2014. https://doi.org/10.1109/ICCP.2014.6936995
[4]
Negru, M., Nedevschi, S. and Peter, R.I. (2015) Exponential Contrast Restoration in Fog Conditions for Driving Assistance. IEEE Transactions on Intelligent Transportation Systems, 16, 2257-2268. https://doi.org/10.1109/TITS.2015.2405013
[5]
Bertozzi, M., Broggi, A., Cellario, M., Fascioli, A., Lombardi, P. and Porta, M. (2002) Artificial Vision in Road Vehicles. Proceedings of the IEEE, 90, 1258-1271. https://doi.org/10.1109/JPROC.2002.801444
[6]
Acosta, N. and Toloza, J. (2012) Techniques to Improve the GPS Precision. International Journal of Advanced Computer Science and Applications, 3, 125-130. https://doi.org/10.14569/IJACSA.2012.030820
[7]
Ben Ali, O.W., Al-Harthei, H. and Garib, A. (2013) Real-Time Fog Warning System for the Abu Dhabi Emirate (UAE). Journal of Traffic and Logistics Engineering, 1, 213-217. https://doi.org/10.12720/jtle.1.2.213-217
[8]
He, Y., Yu, H. and Fang, H. (2005) Study on Improving GPS Measurement Accuracy. Proceedings of Instrumentation and Measurement Technology Conference, Ottawa, 17-19 May 2005, 1476-1479.
[9]
Wang, E., Zhao, W. and Cai, M. (2013) Research on Improving Accuracy of GPS Positioning Based on Particle Filter. Proceedings of 8th IEEE Conference on Industrial Electronics and Applications (ICIEA), Melbourne, 19-21 June 2013.
[10]
Jongsintawee, S., Runraengwajiake, S., Supnithi, P. and Panachart, C. (2016) Improvement of GPS Positioning Accuracy When Utilizing Klobuchar Model with Ionospheric Conditions in Thailand. Proceedings of 13rd International Conference on Electrical Engineering/Electronics, Computer, Telecommunications and Information Technology (ECTI-CON), Chiang Mai, 1-5. https://doi.org/10.1109/ECTICon.2016.7561391
[11]
Tzoreff, E. and Bobrovsky, B.-Z. (2012) A Novel Approach for Modeling Land Vehicle Kinematics to Improve GPS Performance under Urban Environment Conditions. IEEE Transactions on Intelligent Transportation Systems, 13, 344-353. https://doi.org/10.1109/TITS.2011.2171051
[12]
Delaney, K. (2015) GPS Error Rejection Improves Accuracy of Offshore Platform Watch Circle Kalman Filter-Based Technique Prevents False Alarms. Sea Technology, 56, 49-50.
[13]
Marry, K. and Bettinger, P. (2019) Smartphone GPS Accuracy Study in an Urban Environment. PLoS ONE, 14, e0219890. https://doi.org/10.1371/journal.pone.0219890
[14]
Kume, H., Taketomi, T., Sato, T. and Yokoya, N. (2010) Extrinsic Camera Parameter Estimation Using Video Images and GPS Considering GPS Positioning Accuracy. Proceedings of 20th International Conference on Pattern Recognition, Istanbul, 23-26 August 2010, 3923-3926. https://doi.org/10.1109/ICPR.2010.954
[15]
Ross, R. and Hoque, R. (2020) Augmenting GPS with Geolocated Fiducials to Improve Accuracy for Mobile Robot Applications. Applied Science, 10, 146. https://doi.org/10.3390/app10010146
[16]
Miroslav, T., Grozev, G. and Alexandrov, C. (2013) One Way to Improve GSP Accuracy Using Local Meteorological Data. Journal of Marine Technology and Environment, 2, 95-100.
[17]
Welch, G. and Bishop, G. (2001) An Introduction to the Kalman Filter. University of North Carolina, Chapel Hill.
[18]
Grewal, M.S. and Andrews, A.P. (2001) Kalman Filtering-Theory and Practice Using MATLAB. Wiley, Hoboken.
[19]
Ma, Z., Liu, Z. and Chen, M. (2006) Application of Adaptive Kalman Filtering Based on Fuzzy Logic to the Integrated GPS/INS Navigation. Information and Control, 35, 457-461.
[20]
Zhong, F. and Zhon, Y. (2005) The Application Research of Mamdani and Sugeno Style Fuzzy Inference. Journal of Hubei Polytechnic University, 20, 28-30.
[21]
Shi, X. and Hao, Z. (2008) Fuzzy Control and Its Simulation in Matlab. Tsinghua University Press, Beijing.
[22]
Liu, M.W. and Xiong, F.F. (2011) A Fuzzy Adaptive GPS/INS Integrated Navigation Algorithm. Procedia Engineering, 15, 660-664. https://doi.org/10.1016/j.proeng.2011.08.123
[23]
Doxsey (2012) An Introduction to Programming in Go. O’Reilly.
