PROPOSITION OF GENERIC VALIDATION CRITERIA USING STEREO-VISION FOR ON-ROAD OBSTACLE DETECTION

Mathias Perrollaz, Raphael Labayrade, Dominique Gruyer, Alain Lambert, and Didier Aubert

Keywords

Stereovision, obstacle detection, sensor fusion, intelligent vehicles

Abstract

Real-time obstacle detection is an essential function for the future of Advanced Driver Assistance Systems (ADAS), but its applications to the driving safety require a very high reliability: the detection rate must be high, while the false detection rate must remain extremely low. Such features seem antinomic for obstacle detection systems, especially when using a single sensor. Multi-sensor fusion is often considered as a mean to reduce this limitation. In this paper, we propose to use stereo-vision as a post-process to improve the reliability of any obstacle detection system, by reducing the number of false positives. Our algorithm, which is both generic and real-time confirms detections by locally using the stereoscopic data. We evaluated and validated our approach with an initial detection based on a vision system and a laser scanner. The evaluation dataset is real on-road data and contains more than 20,000 images.

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