Authors: Fawzi Nashashibi
Addresses: Robotics Centre – Mines ParisTech – JRU LARA, 60 Boulevard Saint-Michel, 75272 Paris Cedex 06, France
Abstract: This paper tackles the problem of improving the robustness of vehicle detection for advanced ACC and obstacle detection applications. Our approach is based on a multi-sensor data fusion for vehicle detection and tracking. Our architecture combines two sensors: a frontal camera and a 2D laser scanner. Improving robustness stems from two aspects. First, the vision-based detection by developing a multi-algorithm approach enhanced with a genetic AdaBoost-based algorithm for vehicle recognition is addressed. Then, the transferable belief model and evidence theory as a fusion framework to combine confidence levels delivered by the algorithms in order to improve the classification are used. The architecture of the system is very modular, generic and flexible: it could be used for other detection applications or using other sensors or algorithms providing the same outputs. The system was successfully implemented on a prototype vehicle and was evaluated under real conditions and over various multi-sensor databases and various test scenarios.
Keywords: ITS; ACC; vehicle tracking; monocular vision; laser scanning; object recognition; AdaBoost; multi-sensor fusion; theory of evidence; TBM; vehicle information; communication systems; vehicle detection; data fusion; multiple sensors; genetic algorithms; vehicle recognition; intelligent transport systems; adaptive cruise control; vehicle localisation.
International Journal of Vehicle Information and Communication Systems, 2009 Vol.2 No.1/2, pp.99 - 121
Published online: 09 Aug 2009 *Full-text access for editors Access for subscribers Purchase this article Comment on this article