RT Journal Article
SR Electronic
A1 Bonfitto, Angelo
A1 Feraco, Stefano
T1 A Data-Driven Method for Vehicle Speed Estimation
JF Communications - Scientific Letters of the University of Zilina
YR 2021
VO 23
IS 3
SP B165
OP B177
DO 10.26552/com.C.2021.3.B165-B177
UL https://komunikacie.uniza.sk/artkey/csl-202103-0011.php
AB 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.