Abstract
Road waterlogging affects the behavior of driver-vehicle unit, thus road waterlogging perception technique is highly important to traffic safety. In this work, a pressure-guiding waterlogging perception method is introduced, and this sensor is modeled based on the principle of differential pressure to realize the real-time measurement of the road waterlogging level. In order to decrease non-linear measurement error of this waterlogging sensor under complex road environment, this paper proposed an adaptive correction algorithm according to the principle of data fusion. The experimental results show that this proposed method has much higher stability and measurement accuracy than typical measurement methods of the road waterlogging level.
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References
Shi Y (2012) Risk analysis of rainstorm waterlogging on residences in Shanghai based on scenario simulation. Nat Hazards 62(2):677–689
Huang Z, Wang S (2013) Based on the water depth sensor network highway measurement system design. Comput Meas Control 21(2):352–354
Xu Z, Feng J, Chang H (2017) Research survey of road-water depth measurements. Electron Meas Technol
Iwahashi M, Udomsiri S, Imai Y, et al (2007) Water level detection for functionally layered video coding. In: IEEE international conference on image processing. IEEE, pp II-321–II-324
Kim J, Han Y, Hahn H (2011) Embedded implementation of image-based water-level measurement system. IET Comput Vision 5(2):125–133
Mousa M, Claudel C (2014) Water level estimation in urban ultrasonic/passive infrared flash flood sensor networks using supervised learning. In: IPSN-14 proceedings of the 13th international symposium on information processing in sensor networks. IEEE, pp 277–278
Xu H, Liu L (2010) Application of electronic water gauge in monitoring system for urban road waterlogging. Water Resour Informatization 3:015
Xia Z (2013) Design of monitoring and alarm system for city stagnant water. Electron Test 5:030
Zhang X, Yu S, Zhang J (2016) Lead pressure formula fluviograph:, CN 205561978 U [P]
Rumelhart DE, Hinton GE, Williams RJ (1986) Learning representations by back-propagating errors. Nature 323(6088):533
Hecht-Nielsen R (1992) Theory of the backpropagation neural network. In: Neural networks for perception. pp 65–93
Xia S, Zhao L (2015) Research of multi-sensor information fusion based on BP neural networks. Comput Meas Control
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This work was supported by a grant from Anhui Province Foreign Science and Technology Cooperation Project.
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Li, Qj., Chen, F., Guo, Hq. (2020). An Intelligent Road Waterlogging Sensor for Traffic Safety: Principle and Algorithm. In: Wang, W., Baumann, M., Jiang, X. (eds) Green, Smart and Connected Transportation Systems. Lecture Notes in Electrical Engineering, vol 617. Springer, Singapore. https://doi.org/10.1007/978-981-15-0644-4_46
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DOI: https://doi.org/10.1007/978-981-15-0644-4_46
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