Toward synthetic data generation to enhance skidding detection in winter conditions

McKenzie, B., Kelouwani, S. et Gaudreau, M.-A. (2022). Toward synthetic data generation to enhance skidding detection in winter conditions. World Electric Vehicle Journal, 13 (12). Article 231. ISSN 2032-6653 DOI 10.3390/wevj13120231

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Résumé

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.

Type de document:	Article
Mots-clés libres:	Side-slip Winter driving Artificial neural network
Date de dépôt:	22 mai 2024 18:38
Dernière modification:	28 juill. 2025 13:55
Version du document déposé:	Version officielle de l'éditeur
URI:	https://depot-e.uqtr.ca/id/eprint/11320

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