An atmospheric visibility measurement system capable of quantifying the most common operating range of onboard exteroceptive sensors is a key parameter in the creation of driving assistance systems. This information is then utilized to adapt sensor operations and processing or to alert the driver that the onboard assistance system is momentarily inoperative. Moreover, a system capable of either detecting the presence of fog or estimating visibility distances constitutes in itself a driving aid. In this paper, we first present a review of different optical sensors likely to measure the visibility distance. We then present our stereovision based technique to estimate what we call the "mobilized visibility distance". This is the distance to the most distant object on the road surface having a contrast above 5%. In fact, this definition is very close to the definition of the meteorological visibility distance proposed by the International Commission on Illumination (CIE). The method combines the computation of both a depth map of the vehicle environment using the "v-disparity" approach and of local contrasts above 5%. Both methods are described separately. Then, their combination is detailed. A qualitative evaluation is done using different video sequences. Finally, a static quantitative evaluation is also performed thanks to reference targets installed on a dedicated test site.

This paper deals with a first evaluation of the efficiency and the robustness of the real-time "v-disparity" algorithm in stereovision for generic road obstacles detection towards various types of obstacles (vehicle, pedestrian, motorbike, cyclist, boxes) and under adverse conditions (day, night, rain, glowing effect, noise and false matches in the disparity map). The theoretical good properties of the "v-disparity" algorithm--accuracy, robustness, computational speed--are experimentally confirmed. The good results obtained allow us to use this stereo algorithm as the onboard perception process for Driving Safety Assistance: conductor warning and longitudinal control of a low speed automated vehicle (using a second order sliding mode control) in difficult and original situations, at frame rate using no special hardware. Results of experiments--Vehicle following at low speed, Stop'n'Go, Stop on Obstacle (pedestrian, fallen motorbike, load dropping obstacle)--are presented.

Road traffic incidents analysis has shown that a third of them occurs without any conflict which indicates problems with road following. In this paper a driving safety assistance system is introduced, whose aim is to prevent the driver drifting off or running off the road. The road following system is based on a frontal on-board monocular camera. In order to get a high degree of reliability and robustness, an original combination of three different algorithms is performed. Low level results from the first two algorithms are used to compute a reliability indicator and to update a high level model through the third algorithm using Kalman filtering. Searching areas of the road sides for the next image are also updated. Experimental results show the reliability and the robustness of this original association of three different algorithms. Various road situations are addressed, including roads with high curvature. A multi-lanes extension is also presented.