Abstract
The driving reaction was recognized and predicted was complicated. The driving reaction samples of various driving reactions were gained, by means of 3 settings of driving reactions including driving action of automobile in front, roadblocks cutting off in other lanes were planed and simulated in the real road environment. Experimental data of driving reactions including automobile velocity, reaction velocity, and reaction time were acquired by the driving action collecting system. Taking advantage of fuzzy aggregation analysis, the driving reaction samples were unified for the fuzzy cluster making (FCM) and probabilistic neural network (PNN). Under different groups of driving reaction data selected from driving reaction samples, the PNN driving reaction network was constructed and trained. The analytical results show that the hit rate is 95.3% when the training driving reaction sample number is 46. Meanwhile, the results show that the PNN and FCM are useful with sufficient driving reaction samples. The driving reaction PNN with FCM is a valid way for the driving reaction time forecast.
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Acknowledgements
The driving reaction work is supported by 2019 Tianjin transportation science and technology projects (2017A-24) and the research projects of Tianjin University of Technology and Education (XJKC031429).
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Xiao, J., Weng, Y., Xie, Y. (2021). Study on the Forecast of Driving Reaction Based on PNN and FCM. In: WU, C.H., PATNAIK, S., POPENTIU VLÃDICESCU, F., NAKAMATSU, K. (eds) Recent Developments in Intelligent Computing, Communication and Devices. ICCD 2019. Advances in Intelligent Systems and Computing, vol 1185. Springer, Singapore. https://doi.org/10.1007/978-981-15-5887-0_12
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DOI: https://doi.org/10.1007/978-981-15-5887-0_12
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