Visual saliency on the road: model and database dependent detection

National audienceIn the road context, objects of interest (salient or not) must be efficiently detected under any condition to ensure safety, for both driver assistance systems and autonomous vehicles. Nine representative state-of-the-art saliency models are evaluated on driving databases (human perception vs. robotics). Although not sufficient for robust detection, bottom-up saliency provides important information, especially when controlling for the classical biases.Dans le contexte routier, les objets d'intérêt (saillants ou non) doivent être efficacement détectés quelles que soient les conditions afin d'assurer la sécurité, que ce soit pour des systèmes d'assistance à la conduite ou des véhicules autonomes. Neufs modèles de saillance représentatifs de l'état de l'art sont évalués sur deux bases de données issues du contexte routier (perception humaine et robotique). Bien qu'elle ne soit pas suffisante pour la détection, la saillance visuelle bottom-up fournit des informations pertinentes, d'autant plus en la contrôlant pour ses biais classiques

Visual saliency on the road: model and database dependent detection

National audienceIn the road context, objects of interest (salient or not) must be efficiently detected under any condition to ensure safety, for both driver assistance systems and autonomous vehicles. Nine representative state-of-the-art saliency models are evaluated on driving databases (human perception vs. robotics). Although not sufficient for robust detection, bottom-up saliency provides important information, especially when controlling for the classical biases.Dans le contexte routier, les objets d'intérêt (saillants ou non) doivent être efficacement détectés quelles que soient les conditions afin d'assurer la sécurité, que ce soit pour des systèmes d'assistance à la conduite ou des véhicules autonomes. Neufs modèles de saillance représentatifs de l'état de l'art sont évalués sur deux bases de données issues du contexte routier (perception humaine et robotique). Bien qu'elle ne soit pas suffisante pour la détection, la saillance visuelle bottom-up fournit des informations pertinentes, d'autant plus en la contrôlant pour ses biais classiques

La surestimation de la distance intervéhiculaire dans le brouillard

Les difficultés de la conduite dans le brouillard sont le plus souvent envisagées en termes d'une réduction des distances de visibilité et des possibilités d'anticipation. Dans une série d'expérimentations en salle de brouillard et sur simulateur de conduite nous avons notamment cherche à étudier l'influence du brouillard sur la perception de la distance intervéhiculaire. Les résultats mettent en évidence un important phénomène de surestimation des distances dans le brouillard qui est susceptible de contribuer à la réduction des intervalles entre véhicules dans ces conditions. Les résultats montrent l'importance de la taille familière du véhicule comme indice de distance dans les situations étudiées. Ils confirment l'hypothèse d'une altération des mécanismes élémentaires de la perception de l'espace en condition de brouillard

Light Transmission in Fog: The Influence of Wavelength on the Extinction Coefficient

Autonomous driving is based on innovative technologies that have to ensure that vehicles are driven safely. LiDARs are one of the reference sensors for obstacle detection. However, this technology is affected by adverse weather conditions, especially fog. Different wavelengths are investigated to meet this challenge (905 nm vs. 1550 nm). The influence of wavelength on light transmission in fog is then examined and results reported. A theoretical approach by calculating the extinction coefficient for different wavelengths is presented in comparison to measurements with a spectroradiometer in the range of 350 nm–2450 nm. The experiment took place in the French Cerema PAVIN BPplatform for intelligent vehicles, which makes it possible to reproduce controlled fogs of different density for two types of droplet size distribution. Direct spectroradiometer extinction measurements vary in the same way as the models. Finally, the wavelengths for LiDARs should not be chosen on the basis of fog conditions: there is a small difference (<10%) between the extinction coefficients at 905 nm and 1550 nm for the same emitted power in fog

Fog Classification by Their Droplet Size Distributions: Application to the Characterization of Cerema’s Platform

Fog is one of major challenges for transportation systems. The automation of the latter is based on perception sensors that can be disrupted by atmospheric conditions. As fog conditions are random and non-reproducible in nature, Cerema has designed a platform to generate fog and rain on demand. Two types of artificial fog with different droplet size distributions are generated: they correspond to radiation fogs with small and medium droplets. This study presents an original method for classifying these different types of fog in a descriptive and quantitative way. It uses a new fog classification coefficient based on a principal component analysis, which measures the ability of a pair of droplet size distribution descriptors to differentiate between the two different types of fog. This method is applied to a database containing more than 12,000 droplet size distributions collected within the platform. It makes it possible to show: (1) that the two types of fog proposed by Cerema have significantly different droplet size distributions, for meteorological visibility values from 10 m to 1000 m; (2) that the proposed droplet size distribution range is included in the natural droplet size distribution range; (3) that the proposed droplet size distribution range should be extended in particular with larger droplets. Finally, the proposed method makes it possible to compare the different fog droplet size distribution descriptors proposed in the literature

A Controlled Environment for Testing Sensors under Adverse Weather Conditions : The Cerema R&D Fog and Rain Platform

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