Automotive surface identification system based on modular neural network architecture

Aleksandr Bystrov, Edward Hoare, Thuy-Yung Tran, Nigel Clarke, Marina Gashinova, Mikhail Cherniakov

Research output: Chapter in Book/Report/Conference proceedingConference contribution

2 Citations (Scopus)
212 Downloads (Pure)

Abstract

The development of automotive remote surface identification system is an important step in ensuring road safety. In this paper we shall discuss a novel approach which addresses the road surface classification process. This method is based on polarimetric radar and sonar data fusion and surface identification using artificial neural network. A modular artificial neural network, which is considered in the paper, allows an overall increase in classification accuracy in the presence of a large number of surface types and a large number of signal features. We shall discuss the techniques involved and present classification results that have been achieved using modular neural network.
Original languageEnglish
Title of host publicationRadar Symposium (IRS), 2017 18th International
Place of PublicationPrague, Czech Republic
PublisherIEEE Xplore
Number of pages8
ISBN (Electronic)978-3736993433
ISBN (Print)978-1509043125
DOIs
Publication statusE-pub ahead of print - 15 Aug 2017

Publication series

NameRadar Symposium (IRS)
PublisherIEEE
ISSN (Electronic)2155-5753

Fingerprint

Dive into the research topics of 'Automotive surface identification system based on modular neural network architecture'. Together they form a unique fingerprint.

Cite this