2026-06-02T11:54:25Zhttps://riuma.uma.es/rest/oai/request

oai:riuma.uma.es:10630/229692026-02-03T12:07:20Zcom_10630_2254col_10630_37959

Sánchez-Andrades, Ignacio Velasco García, Juan María Sánchez Lozano, Miguel Cabrera-Carrillo, Juan Antonio Castillo-Aguilar, Juan Jesús 2021-10-08T08:33:33Z 2021-10-08T08:33:33Z 2021-08-18 https://hdl.handle.net/10630/22969 Determining the surface on which a vehicle is moving is vital information for im-proving active safety systems. Performing the surface classification or estimating adherence through tire slippage can lead to late action in possible risk situations. Currently, approaches based on image, sound, or vibration analysis are emerging as a viable alternative, though sometimes complex. This work proposes a methodology based on the use of low-cost accelerometers combined with Deep Learning tech-niques. The performance of the proposed system is evaluated with real tests, where high percentages of accuracy are obtained in the classification task. eng open access Datos - Adquisición Aprendizaje automático (Inteligencia artificial) Automóviles Low-Cost Surface Classification System Supported by Deep Neural Models conference output