Comparison of Three Approaches for Scenario Classification for the Automotive Field

  • Nicola Bernini
  • Massimo Bertozzi
  • Luca Devincenzi
  • Luca Mazzei
Conference paper

DOI: 10.1007/978-3-642-41181-6_59

Volume 8156 of the book series Lecture Notes in Computer Science (LNCS)
Cite this paper as:
Bernini N., Bertozzi M., Devincenzi L., Mazzei L. (2013) Comparison of Three Approaches for Scenario Classification for the Automotive Field. In: Petrosino A. (eds) Image Analysis and Processing – ICIAP 2013. ICIAP 2013. Lecture Notes in Computer Science, vol 8156. Springer, Berlin, Heidelberg

Abstract

To extend the functionalities of Advanced Driver Assistance Systems (ADAS) and have a more accurate control on the parameters of sensors mounted on an intelligent vehicle, a tool that can classify the scenarios which the vehicle moves in, is needed.

This article presents a comparison of three classification techniques (PCA, ANN and SVM) to obtain a fast and robust scene classifier based only on images. The systems presented in this paper have been trained on three different categories of traffic scenarios: urban, highway, and rural, on a total of more than 23 hours of driving in different countries.

Keywords

scenario classification intelligent vehicles automotive 

Preview

Unable to display preview. Download preview PDF.

Unable to display preview. Download preview PDF.

Copyright information

© Springer-Verlag Berlin Heidelberg 2013

Authors and Affiliations

  • Nicola Bernini
    • 1
  • Massimo Bertozzi
    • 1
  • Luca Devincenzi
    • 1
  • Luca Mazzei
    • 1
  1. 1.Dip. Ing InformazioneParmaItaly