Automotive system for remote surface classification

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

Research output: Contribution to journalArticlepeer-review

13 Citations (Scopus)
194 Downloads (Pure)


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.
Original languageEnglish
Article number745
Number of pages18
Issue number4
Publication statusPublished - 1 Apr 2017


  • radar remote sensing
  • sonar applications
  • supervised learning
  • classification algorithms
  • artificial neural networks
  • multilayer perceptron
  • parameter extraction
  • sensor fusion


Dive into the research topics of 'Automotive system for remote surface classification'. Together they form a unique fingerprint.

Cite this