In automotive applications, the knowledge of the vehicle load is a crucial factor that can bring significant improvement on safety and performance, e.g. in ABS or semiactive suspensions control. In narrow-track vehicles, this aspect is even more important, considering that the mass variation - w.r.t to the vehicle one - is higher than in standard vehicles. The objective of this work is to present an on-line data-based mass classifier based on inertial sensors only. The effectiveness of the approach is assessed on experimental data taken from a real vehicle.

On-Line Data-Based Load Classification in Narrow-Track Vehicles

Formentin, Simone;Panzani, Giulio;Savaresi, Sergio M.
2018-01-01

Abstract

In automotive applications, the knowledge of the vehicle load is a crucial factor that can bring significant improvement on safety and performance, e.g. in ABS or semiactive suspensions control. In narrow-track vehicles, this aspect is even more important, considering that the mass variation - w.r.t to the vehicle one - is higher than in standard vehicles. The objective of this work is to present an on-line data-based mass classifier based on inertial sensors only. The effectiveness of the approach is assessed on experimental data taken from a real vehicle.
2018
IEEE Conference on Intelligent Transportation Systems, Proceedings, ITSC
9781728103235
Automotive Engineering; Mechanical Engineering; Computer Science Applications1707 Computer Vision and Pattern Recognition
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Utilizza questo identificativo per citare o creare un link a questo documento: https://hdl.handle.net/11311/1074420
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