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Please use this identifier to cite or link to this item: http://dspace.vgtu.lt/handle/1/3978

Title: Identification of Road-Surface Type Using Deep Neural Networks for Friction Coefficient Estimation
Authors: Šabanovič, Eldar
Žuraulis, Vidas
Prentkovskis, Olegas
Skrickij, Viktor
Keywords: video image sensor
vehicle perception system
road type identification
artificial intelligence
vehicle dynamics
Issue Date: 2020
Publisher: MDPI
Citation: Šabanovič, E.; Žuraulis, V.; Prentkovskis, O.; Skrickij, V. Identification of Road-Surface Type Using Deep Neural Networks for Friction Coefficient Estimation. Sensors 2020, 20, 612.
Series/Report no.: 20;3
Abstract: Nowadays, vehicles have advanced driver-assistance systems which help to improve vehicle safety and save the lives of drivers, passengers and pedestrians. Identification of the road-surface type and condition in real time using a video image sensor, can increase the effectiveness of such systems significantly, especially when adapting it for braking and stability-related solutions. This paper contributes to the development of the new efficient engineering solution aimed at improving vehicle dynamics control via the anti-lock braking system (ABS) by estimating friction coefficient using video data. The experimental research on three different road surface types in dry and wet conditions has been carried out and braking performance was established with a car mathematical model (MM). Testing of a deep neural networks (DNN)-based road-surface and conditions classification algorithm revealed that this is the most promising approach for this task. The research has shown that the proposed solution increases the performance of ABS with a rule-based control strategy.
Description: This article belongs to the Special Issue Advance in Sensors and Sensing Systems for Driving and Transportation
URI: http://dspace.vgtu.lt/handle/1/3978
ISSN: 1424-8220
Appears in Collections:Moksliniai straipsniai / Research articles

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