Identification of Road-Surface Type Using Deep Neural Networks for Friction Coefficient Estimation

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 sig...

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Bibliographic Details
Published in:Sensors (Basel, Switzerland) Switzerland), 2020-01, Vol.20 (3), p.612
Main Authors: Šabanovič, Eldar, Žuraulis, Vidas, Prentkovskis, Olegas, Skrickij, Viktor
Format: Article
Language:English
Subjects:
Algorithms
Antilock braking systems
artificial intelligence
Automation
Automobiles
Camcorders
Cameras
Coefficient of friction
Datasets
Friction
Global positioning systems
GPS
Neural networks
road type identification
Roads & highways
Sensors
Strain gauges
System effectiveness
vehicle dynamics
vehicle perception system
Video data
video image sensor
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Summary: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.
ISSN:1424-8220
1424-8220
DOI:10.3390/s20030612