Next Article in Journal
Energy-Efficient Deadline-Aware Data-Gathering Scheme Using Multiple Mobile Data Collectors
Previous Article in Journal
Conductive Photo-Activated Porphyrin-ZnO Nanostructured Gas Sensor Array
Article Menu
Issue 4 (April) cover image

Export Article

Open AccessArticle
Sensors 2017, 17(4), 745; doi:10.3390/s17040745

Automotive System for Remote Surface Classification

1
School of Engineering, University of Birmingham, Birmingham B15 2TT, UK
2
Jaguar Land Rover Research Department, Coventry CV3 4LF, UK
*
Author to whom correspondence should be addressed.
Academic Editor: Vittorio M. N. Passaro
Received: 27 January 2017 / Revised: 21 March 2017 / Accepted: 30 March 2017 / Published: 1 April 2017
(This article belongs to the Section Remote Sensors)
View Full-Text   |   Download PDF [2792 KB, uploaded 20 April 2017]   |  

Abstract

In this paper we shall discuss a novel approach to road surface recognition, based on the analysis of backscattered microwave and ultrasonic signals. The novelty of our method is sonar and polarimetric radar data fusion, extraction of features for separate swathes of illuminated surface (segmentation), and using of multi-stage artificial neural network for surface classification. The developed system consists of 24 GHz radar and 40 kHz ultrasonic sensor. The features are extracted from backscattered signals and then the procedures of principal component analysis and supervised classification are applied to feature data. The special attention is paid to multi-stage artificial neural network which allows an overall increase in classification accuracy. The proposed technique was tested for recognition of a large number of real surfaces in different weather conditions with the average accuracy of correct classification of 95%. The obtained results thereby demonstrate that the use of proposed system architecture and statistical methods allow for reliable discrimination of various road surfaces in real conditions. View Full-Text
Keywords: radar remote sensing; sonar applications; supervised learning; classification algorithms; artificial neural networks; multilayer perceptron; parameter extraction; sensor fusion radar remote sensing; sonar applications; supervised learning; classification algorithms; artificial neural networks; multilayer perceptron; parameter extraction; sensor fusion
Figures

Figure 1

This is an open access article distributed under the Creative Commons Attribution License which permits unrestricted use, distribution, and reproduction in any medium, provided the original work is properly cited. (CC BY 4.0).

Scifeed alert for new publications

Never miss any articles matching your research from any publisher
  • Get alerts for new papers matching your research
  • Find out the new papers from selected authors
  • Updated daily for 49'000+ journals and 6000+ publishers
  • Define your Scifeed now

SciFeed Share & Cite This Article

MDPI and ACS Style

Bystrov, A.; Hoare, E.; Tran, T.-Y.; Clarke, N.; Gashinova, M.; Cherniakov, M. Automotive System for Remote Surface Classification. Sensors 2017, 17, 745.

Show more citation formats Show less citations formats

Note that from the first issue of 2016, MDPI journals use article numbers instead of page numbers. See further details here.

Related Articles

Article Metrics

Article Access Statistics

1

Comments

[Return to top]
Sensors EISSN 1424-8220 Published by MDPI AG, Basel, Switzerland RSS E-Mail Table of Contents Alert
Back to Top