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— Many roads are not totaly planar and often present hills and valleys because of environment topography. Nevertheless, the majority of existing techniques for road obstacle detection using stereovision assumes that the road is planar. This can cause several issues : imprecision as regards the real position of obstacles as well as false obstacle detection(More)
In this article, we will present a technique for measuring visibility distances under foggy weather conditions using a camera mounted onboard a moving vehicle. Our research has focused in particular on the problem of detecting daytime fog and estimating visibility distances; thanks to these efforts, an original method has been developed, tested and(More)
The contrast of outdoor images acquired under adverse weather conditions, especially foggy weather, is altered by the scattering of daylight by atmospheric particles. As a consequence, different methods have been designed to restore the contrast of these images. However, there is a lack of methodology to assess the performances of the methods or to rate(More)
We propose a new cooperative fusion approach between stereovision and laser scanner in order to take advantage of the best features and cope with the drawbacks of these two sensors to perform robust, accurate and real time-detection of multi-obstacles in the automotive context. The proposed system is able to estimate the position and the height, width and(More)
In this paper, we propose a fast and robust stereo algorithm to perform in-vehicle obstacles detection and characterization. The stereo algorithm used is called the " v-disparity " 1 algorithm which provides a suitable representation of the geometric content of the road scene. The stereo algorithm principle is described, and then the in-vehicle embedded(More)
Stereo-vision is extensively used for intelligent vehicles, mainly for obstacle detection, as it provides a large amount of data. Many authors use it as a classical 3D sensor which provides a large tri-dimensional cloud of metric measurements, and apply methods usually designed for other sensors, such as clustering based on a distance. For stereo-vision,(More)
In adverse weather conditions, in particular, in daylight fog, the contrast of images grabbed by in-vehicle cameras in the visible light range is drastically degraded, which makes current driver assistance that relies on cameras very sensitive to weather conditions. An onboard vision system should take weather effects into account. The effects of daylight(More)
Vision-based autonomous vehicles must face numerous challenges in order to be effective in practical areas. Among these lies the detection and localization of independent-moving objects, so as to track or avoid them. In this paper a method that address this particular issue is presented. Information from stereo and motion is used to extract the ego-motion(More)
To monitor their networks, road operators equip them with cameras. Degraded meteorological conditions alter the transport system operation by modifying the behavior of drivers and by reducing the operation range of the sensors. A vision-based traffic monitoring system is proposed to take fog and rain into account and react accordingly. A background modeling(More)