Abstract. This work aims at introducing modelling, theoretical and numerical studies related to a new downscaling technique applied to computational fluid dynamics. Our method consists in building a local model, forced by large scale information computed thanks to a classical numerical weather predictor. The local model, compatible with the Navier-Stokes equations, is used for the small scale computation (downscaling) of the considered fluid. It is inspired by Pope's works on turbulence, and consists in a so-called Langevin system of stochastic differential equations. We introduce this model and exhibit its links with classical RANS models. Well-posedness, as well as mean-field interacting particle approximations and boundary condition issues are addressed. We present the numerical discretization of the stochastic downscaling method and investigate the accuracy of the proposed algorithm on simplified situations.Mathematics Subject Classification. 65C20, 65C35, 68U20, 35Q30.

In this article, we propose a new stochastic downscaling method: provided a numerical prediction of wind at large scale, we aim to improve the approximation at small scales thanks to a local stochastic model. We first recall the framework of a Lagrangian stochastic model borrowed from S.B. Pope. Then, we adapt it to our meteorological framework, both from the theoretical and numerical viewpoints. Finally, we present some promising numerical results corresponding to the simulation of wind over the Mediterranean Sea.

Featured Application: This work gives some advice on choosing the best wavelengths for active sensors working in the near-infrared spectral band (such as LiDARs or time-of-flight cameras) taking into account fog conditions. Abstract: 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.

The AWARE (All Weather All Roads Enhanced vision) French public funded project is aiming at the development of a low cost sensor fitting to automotive and aviation requirements, and enabling a vision in all poor visibility conditions, such as night, fog, rain and snow.In order to identify the technologies providing the best all-weather vision, we evaluated the relevance of four different spectral bands: Visible RGB, Near-Infrared (NIR), Short-Wave Infrared (SWIR) and Long-Wave Infrared (LWIR). Two test campaigns have been realized in outdoor natural conditions and in artificial fog tunnel, with four cameras recording simultaneously.This paper presents the detailed results of this comparative study, focusing on pedestrians, vehicles, traffic signs and lanes detection.

This paper presents a novel calibration algorithm for plenoptic cameras, especially the multi-focus configuration, where several types of micro-lenses are used, using raw images only. Current calibration methods rely on simplified projection models, use features from reconstructed images, or require separated calibrations for each type of micro-lens. In the multi-focus configuration, the same part of a scene will demonstrate different amounts of blur according to the micro-lens focal length. Usually, only micro-images with the smallest amount of blur are used. In order to exploit all available data, we propose to explicitly model the defocus blur in a new camera model with the help of our newly introduced Blur Aware Plenoptic (BAP) feature. First, it is used in a pre-calibration step that retrieves initial camera parameters, and second, to express a new cost function to be minimized in our single optimization process. Third, it is exploited to calibrate the relative blur between micro-images. It links the geometric blur, i.e., the blur circle, to the physical blur, i.e., the point spread function. Finally, we use the resulting blur profile to characterize the camera’s depth of field. Quantitative evaluations in controlled environment on real-world data demonstrate the effectiveness of our calibrations.