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• W4311528069 abstract "In this paper, we propose the use of a neural network to identify lateral skidding events of road vehicles used during winter driving conditions. Firstly, data from a simulation model was used to identify the essential vehicle dynamics variables needed and to create the network structure. Then this network was retrained to classify real-world vehicle skidding events. The final network consists of a 3 layer network with 10, 5 and 1 output neurons 13 inputs, 4 outputs and a 5 step time delay. The retrained network was used on a limited set of real vehicle data and confirmed the effectiveness of the network classifying lateral skidding events." @default.
• W4311528069 created "2022-12-26" @default.
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• W4311528069 date "2022-12-02" @default.
• W4311528069 modified "2023-09-26" @default.
• W4311528069 title "Toward Synthetic Data Generation to Enhance Skidding Detection in Winter Conditions" @default.
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• W4311528069 doi "https://doi.org/10.3390/wevj13120231" @default.
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