Alessandro Allisiardi
Longitudinal speed estimation in vehicles and road condition identification using Artificial Neural Networks.
Rel. Andrea Tonoli, Nicola Amati, Angelo Bonfitto. Politecnico di Torino, Corso di laurea magistrale in Automotive Engineering (Ingegneria Dell'Autoveicolo), 2018
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Abstract
The longitudinal speed in vehicles is a fundamental dynamic parameter that is widely used to understand the behaviour of the vehicle: it is employed in order to control the dynamics of the vehicle and for this reason is one of the main inputs for Anti-Blocking System (ABS), Electronic Stability Program (ESP) and other intelligent devices, that are fundamental to guarantee safe driving conditions. Thus, the correct and precise estimation of the longitudinal speed is fundamental for the correct working conditions of these active control systems. The vehicle speed estimation is generally computed using only data coming from the speedometer mounted on-board, which represents the velocity computed as the mean speed of the vehicle’s wheels.
This method is not sufficient, because of the possibility to produce an error in the speed estimation is really high
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