A Data-Driven Method for Vehicle Speed Estimation

https://doi.org/10.26552/com.C.2021.3.B165-B177

Keywords: vehicle speed, artificial neural networks, estimation, regression, classification, road identification

Abstract

This paper presents a method based on Artificial Neural Networks for estimation of the vehicle speed. The technique exploits the combination of two tasks: a) speed estimation by means of regression neural networks dedicated to different road conditions (dry, wet and icy); b) identification of the road condition with a pattern recognition neural network. The training of the networks is conducted with experimental datasets recorded during the driving sessions performed with a vehicle on different tracks. The effectiveness of the proposed approach is validated experimentally on the same car by deploying the algorithm on a dSPACE computing platform. The estimation accuracy is evaluated by comparing the obtained results to the measurement of an optical sensor installed on the vehicle and to the output of another estimation method, based on the mean value of velocity of the four wheels.

Author Biographies

Angelo Bonfitto, Politecnico di Torino

LIM - Laboratorio Interdisciplinare di Meccatronica, Dipartimento di Ingegneria Meccanica e Aerospaziale (DIMEAS), Politecnico di Torino, Torino, Italy

Stefano Feraco, Politecnico di Torino

LIM - Laboratorio Interdisciplinare di Meccatronica, Dipartimento di Ingegneria Meccanica e Aerospaziale (DIMEAS), Politecnico di Torino, Torino, Italy

Published
2021-07-01
How to Cite
Angelo Bonfitto, & Stefano Feraco. (2021). A Data-Driven Method for Vehicle Speed Estimation. Communications - Scientific Letters of the University of Zilina, 23(3), B165-B177. https://doi.org/10.26552/com.C.2021.3.B165-B177
Section
Mechanical Engineering in Transport