Sensor Data Fusion for Road Obstacle Detection: A Validation Framework

Abstract : Real-time obstacle detection is an essential function for the future of Advanced Driving 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. Therefore, 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 con firms detections by locally using the stereoscopic data.
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Raphaël Labayrade, Mathias Perrollaz, Gruyer Dominique, Didier Aubert. Sensor Data Fusion for Road Obstacle Detection: A Validation Framework. Ciza Thomas. Sensor Fusion and Its Applications, in-tech / Sciyo, 2010, 978-953-307-101-5. ⟨hal-00683751⟩

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