Road detection and vehicles tracking by vision for ACC

This article deals with a process designed first to extract the lane of vehicle by on-board monocular vision. This detection process is based upon a recursive updating of a statistical model of the lane obtained by a training phase. Once the lane has been located, a reconstruction algorithm computes the vehicle location on its lane and the 3D shape of the road. Thereafter, we are focus at the detection, location and tracking of front vehicles equipped with specific visual markers in order to achieve an accurate determination of the location and speed of these vehicles. Merging these various informations allows to point out the most dangerous obstacle. Each of these three processes is detailed significant examples are provide.Cet article présente, dans un premier temps, un procédé permettant de détecter la voie de circulation d'un véhicule par vision monoculaire embarquée. Ce processus de détection est basé sur une mise à jour récursive d'un modèle statistique de la voie obtenu par une phase d'apprentissage. Après avoir localisé la voie, un algorithme de reconstruction détermine la position du véhicule dans sa voie de circulation et le profil 3D de la route. Par la suite, nous nous intéressons à la détection, la localisation et surtout le suivi des véhicules situés à l'avant et équipés de marques visuelles afin de déterminer avec précision la position et la vitesse relative de ces véhicules. La combinaison de ces différentes informations permet de déterminer le véhicule le plus dangereux. La description détaillée de chacune des étapes de notre algorithme est suivie d'exemples significatifs

Alcohol exposure during late gestation: Multiple developmental outcomes in sheep

Alcohol consumption during pregnancy remains common in many countries. Exposure to even low amounts of alcohol (i.e. ethanol) in pregnancy can lead to the heterogeneous fetal alcohol spectrum disorders (FASD), while heavy alcohol consumption can result in the fetal alcohol syndrome (FAS). FAS is characterized by cerebral dysfunction, growth restriction and craniofacial malformations. However, the effects of lower doses of alcohol during pregnancy, such as those that lead to FASD, are less well understood. In this article, we discuss the findings of recent studies performed in our laboratories on the effects of fetal alcohol exposure using sheep, in which we investigated the effects of late gestational alcohol exposure on the developing brain, arteries, kidneys, heart and lungs. Our studies indicate that alcohol exposure in late gestation can (1) affect cerebral white matter development and increase the risk of hemorrhage in the fetal brain, (2) cause left ventricular hypertrophy with evidence of altered cardiomyocyte maturation, (3) lead to a decrease in nephron number in the kidney, (4) cause altered arterial wall stiffness and endothelial and smooth muscle function and (5) result in altered surfactant protein mRNA expression, surfactant phospholipid composition and pro-inflammatory cytokine mRNA expression in the lung. These findings suggest that fetal alcohol exposure in late gestation can affect multiple organs, potentially increasing the risk of disease and organ dysfunction in later life

Accurate road following and reconstruction by computer vision

International audienc

Commande de véhicule utilisant la reconnaissance d'indices image

Since several years, researchers attempt to design algorithms able to control a vehicle by using computer vision. Several approaches are now available but more often the vehicle control is achieved in separate way from the perception task. Indeed the vehicle state is combined with perception parameters to feed the regulation task. This paper presents the reliability of an original approach which combines perception and control task. The system we have designed is able to recognize roadsides from images cues in different situations (highway, road, etc), and to compute the vehicle wheel angle and several location parameters too. In particular, we present the need for achieving a training stage (given either by a control law or by a experimented human driver) to provide a good control law. The algorithm behavior is then presented in simulated and real situations as well in order to prove the reliability of the approach on an agricultural vehicle.Depuis plusieurs années, les chercheurs essayent de concevoir des algorithmes capables de commander un véhicule en employant la vision par ordinateur. Plusieurs approches sont maintenant disponibles mais le plus souvent, la commande du véhicule est réalisée en dehors de la tâche de perception. En effet l'état du vehicule est associé avec des paramètres de perception pour alimenter la tâche de régulation. Cet article présente la robustesse d'une approche originale qui combine la tâche de perception et la tâche de commande. Le système que nous avons conçu peut reconnaître les bords de la route à partir d'indices d'images en différentes situations (autoroute, route, etc..), et calculer l'angle des roues du véhicule ainsi que plusieurs paramètres de localisation. En particulier, nous présentons le besoin de la réalisation d'une étape d'apprentissage (réalisée par une loi de commande ou par un conducteur humain expérimenté) pour permettre une bonne loi de commande. Le comportement de l'algorithme est présenté dans des situations simulées et réelles sur un véhicule agricole

Cooperative localization based on range-only measurements from robots and infrastructure

International audienc

Lambda-Field: a continuous counterpart of the bayesian occupancy grid for risk assessment

International audienceIn a context of autonomous robots, one of the most important task is to ensure the safety of the robot and its surrounding. Most of the time, the risk of navigation is simply said to be the probability of collision. This notion of risk is not well defined in the literature, especially when dealing with occupancy grids. The Bayesian occupancy grid is the most used method to deal with complex environments. However, this is not fitted to compute the risk along a path by its discrete nature, hence giving poor results. In this article, we present a new way to store the occupancy of the environment that allows the computation of risk for a given path. We then define the risk as the force of collision that would occur for a given obstacle. Using this framework, we are able to generate navigation paths ensuring the safety of the robot

Parsimonious vehicle localization architecture using a generic top-down fusion process

International audienceIn this article, we present a parsimonious high level multi-sensor fusion architecture for robot localization using several types of localization techniques. Operating in a Top-Down mode, the parsimonious localization system based on the use of an existing absolute environment map, selects the most adequate modality in an economical and efficient way. Using a bayesian network associated to an Extended Kalman Filter and referring to contextual information, our proposed method aims to ensure a good localization level by selecting the best technique which responds the best to a fixed objective. Presented results show the parsimonious aspect of our application deploying a robot embedding several sensors (laser range-finder, UWB beacons and a low-cost GPS)