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Perception advances in outdoor vehicle detection for automatic cruise control

Published online by Cambridge University Press:  04 September 2009

S. Álvarez ,
M. Á. Sotelo ,
M. Ocaña ,
D. F. Llorca ,
I. Parra  and
L. M. Bergasa
S. Álvarez
Affiliation:
Department of Electronics, University of Alcalá, Ctra. N-II Km. 33, Alcalá de Henares, Madrid, Spain
M. Á. Sotelo*
Affiliation:
Department of Electronics, University of Alcalá, Ctra. N-II Km. 33, Alcalá de Henares, Madrid, Spain
M. Ocaña
Affiliation:
Department of Electronics, University of Alcalá, Ctra. N-II Km. 33, Alcalá de Henares, Madrid, Spain
D. F. Llorca
Affiliation:
Department of Electronics, University of Alcalá, Ctra. N-II Km. 33, Alcalá de Henares, Madrid, Spain
I. Parra
Affiliation:
Department of Electronics, University of Alcalá, Ctra. N-II Km. 33, Alcalá de Henares, Madrid, Spain
L. M. Bergasa
Affiliation:
Department of Electronics, University of Alcalá, Ctra. N-II Km. 33, Alcalá de Henares, Madrid, Spain
*
*Corresponding author. E-mail: sotelo@depeca.uah.es
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Summary

This paper describes a vehicle detection system based on support vector machine (SVM) and monocular vision. The final goal is to provide vehicle-to-vehicle time gap for automatic cruise control (ACC) applications in the framework of intelligent transportation systems (ITS). The challenge is to use a single camera as input, in order to achieve a low cost final system that meets the requirements needed to undertake serial production in automotive industry. The basic feature of the detected objects are first located in the image using vision and then combined with a SVM-based classifier. An intelligent learning approach is proposed in order to better deal with objects variability, illumination conditions, partial occlusions and rotations. A large database containing thousands of object examples extracted from real road scenes has been created for learning purposes. The classifier is trained using SVM in order to be able to classify vehicles, including trucks. In addition, the vehicle detection system described in this paper provides early detection of passing cars and assigns lane to target vehicles. In the paper, we present and discuss the results achieved up to date in real traffic conditions.

Keywords

VisionVehicle detectionAutomatic cruise controlSVM (support vector machine)Intelligent transportation systems
Type
Article
Information
Robotica , Volume 28 , Issue 5 , September 2010 , pp. 765 - 779
Copyright
Copyright © Cambridge University Press 2009

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