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Open AccessArticle

Improved Genetic Algorithm Optimization for Forward Vehicle Detection Problems

1
Navigation College, Dalian Maritime University, Dalian 116026, China
2
School of Automotive Engineering, Dalian University of Technology, Dalian 116024, China
*
Author to whom correspondence should be addressed.
Academic Editors: Baozhen Yao and Yudong Zhang
Information 2015, 6(3), 339-360; https://doi.org/10.3390/info6030339
Received: 30 May 2015 / Revised: 26 June 2015 / Accepted: 6 July 2015 / Published: 10 July 2015
(This article belongs to the Special Issue Swarm Information Acquisition and Swarm Intelligence in Engineering)
Automated forward vehicle detection is an integral component of many advanced driver-assistance systems. The method based on multi-visual information fusion, with its exclusive advantages, has become one of the important topics in this research field. During the whole detection process, there are two key points that should to be resolved. One is to find the robust features for identification and the other is to apply an efficient algorithm for training the model designed with multi-information. This paper presents an adaptive SVM (Support Vector Machine) model to detect vehicle with range estimation using an on-board camera. Due to the extrinsic factors such as shadows and illumination, we pay more attention to enhancing the system with several robust features extracted from a real driving environment. Then, with the introduction of an improved genetic algorithm, the features are fused efficiently by the proposed SVM model. In order to apply the model in the forward collision warning system, longitudinal distance information is provided simultaneously. The proposed method is successfully implemented on a test car and evaluation experimental results show reliability in terms of both the detection rate and potential effectiveness in a real-driving environment. View Full-Text
Keywords: vehicle detection; genetic algorithm (GA); advanced driver-assistance systems (ADAS); forward collision warning system (FCWS) vehicle detection; genetic algorithm (GA); advanced driver-assistance systems (ADAS); forward collision warning system (FCWS)
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Gang, L.; Zhang, M.; Zhao, X.; Wang, S. Improved Genetic Algorithm Optimization for Forward Vehicle Detection Problems. Information 2015, 6, 339-360.

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