Computational intelligence in automotive applications

Yifei Wang*, Naim Dahnoun, Alin Achim

*Corresponding author for this work

Research output: Chapter in Book/Report/Conference proceedingChapter in a book

Abstract

In this chapter, we discuss one of the most popular machine vision applications in the automotive industry: lane detection and tracking. Model-based lane detection algorithms can be separated into lane modeling, feature extraction and model parameter estimation. Each of these steps is discussed in detail with examples and results. A recently proposed lane feature extraction approach, which is called the Global Lane Feature Refinement Algorithm (GLFRA), is also introduced. It provides a generalized framework to significantly improve various types of gradient-based lane feature maps by utilizing the global shape information and subsequently improves the parameter estimation and the tracking performance. Another important aspect of this application lies in the tracking stage.We compare the performances of three different types of particle filters (the sampling importance resampling particle filter, the Gaussian particle filter and the Gaussian sum particles filter) quantitatively and provide insightful result analysis and suggestions. Furthermore, the influence of featuremaps on the tracking performance is also investigated.

Original languageEnglish
Title of host publicationStudies in Computational Intelligence
Pages139-174
Number of pages36
Volume410
DOIs
Publication statusPublished - 1 Jan 2013

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