A stereo vision system for real-time automotive obstacle detection

Abstract

This work presents a system for obstacle detection in a pair of images acquired by a stereo vision device installed on a moving vehicle. The whole system is structured in a pipeline of two different computational engines: a massively parallel architecture, PAPRICA, devoted to low-level image processing and a traditional serial architecture running medium-level tasks. A geometrical transformation, based on the assumption of a flat road in front of the vehicle, is performed to remove the perspective effect from both images. The difference between the results is used for the detection of free-space in front of the vehicle, thus allowing to avoid the high computational tasks involved in traditional stereo vision approaches; the geometrical transformation is performed by a specific hardware device integrated in PAPRICA architecture. The system was tested on the MOB-LAB experimental land vehicle, which was driven for more than 3000 km along extra-urban roads and freeways at speeds up to 80 km/h, and demonstrated its robustness with respect to shadows and changing illumination conditions, different road textures, and vehicle movement.