RT Conference Proceedings
T1 Low-Cost Surface Classification System Supported by Deep Neural Models
A1 Sánchez-Andrades, Ignacio
A1 Velasco García, Juan María
A1 Sánchez Lozano, Miguel
A1 Cabrera-Carrillo, Juan Antonio
A1 Castillo-Aguilar, Juan Jesús
K1 Datos - Adquisición
K1 Aprendizaje automático (Inteligencia artificial)
K1 Automóviles
AB 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.
YR 2021
FD 2021-08-18
LK https://hdl.handle.net/10630/22969
UL https://hdl.handle.net/10630/22969
LA eng
NO Universidad de Málaga. Campus de Excelencia Internacional Andalucía Tech.
DS RIUMA. Repositorio Institucional de la Universidad de Málaga
RD 19 ene 2026