Abstract

The increasing need for mobility has brought about significant changes in transportation infrastructures. Inefficiencies cause enormous losses of time, decrease in the level of safety for both vehicles and pedestrians, high pollution, degradation of quality of life, and huge waste of nonrenewable fossil energy.The scope of this article is to introduce novel functionality for providing knowledge to vehicles, thus jointly managing traffic and safety. This will be achieved through the design of the proposed functionality, which, at a high level, will comprise (1) sensor networks formed by vehicles of a certain vicinity that exchange traffic-related information, (2) cognitive management functionality placed inside the vehicles for inferring knowledge and experience, and (3) cognitive management functionality in the overall transportation infrastructure. The goal of the aforementioned three main components shall be to issue directives to the drivers and the overall transportation infrastructure valuable in context handling.

Abstract

International audience; The widespread increase in the use of light emitting diodes in vehicle's head and taillights and also the use of dashboard cameras provides great prospects for the optical camera based visible light communications (VLC) technology in intelligent transport systems. In this paper, we experimentally investigate the impact of fog on the optical camera based VLC technology for vehicle-to-vehicle (V2V) communications. A range of meteorological visibilities between 5-120 m is considered based on realistic inter-vehicle distances in practical vehicular environments and using a real car taillight as the transmitter. We show a reduction in the index of modulation of the signals from 1 to 0.75 and 0.5 to allow for tracking purposes of the light source when sending '0' symbols. The results show that, the link is error-free up to 20 m meteorological visibility for the three modulation index scenarios and degrades considerably below 10 m meteorological visibility.

Abstract

International audience; With the rapid size shrinking in electronic devices,radiation-induced soft-error has emerged as a major concern to the current circuit manufacturing. In this paper, we present a new error correction scheme based on the residue number arithmetic to cope with the single soft-error issue. The proposed technique called bidirectional redundant residue number system requires the redundant moduli to satisfy some constraints to achieve fast error correction. In this system, both the iterations for decoding the valid number and the error-correcting table that contains all combinations of erroneous digit, are not necessary. The detection and the diagnosis are simultaneously performed in plural parallel consistent-checking that has the capability of locating the corrupt digit. Finally, efficient pipeline architecture for the self-diagnosis decoder is detailed.

Abstract

Fog reduces contrast and thus the visibility of vehicles and obstacles for drivers. Each year, this causes traffic accidents. Fog is caused by a high concentration of very fine water droplets in the air. When light hits these droplets, it is scattered and this results in a dense white background, called the atmospheric veil. As pointed in [1], Advanced Driver Assistance Systems (ADAS) based on the display of defogged images from a camera may help the driver by improving objects visibility in the image and thus may leads to a decrease of fatality and injury rates. In the last few years, the problem of single image defogging has attracted attention in the image processing community. Being an ill-posed problem, several methods have been proposed. However, a few among of these methods are dedicated to the processing of road images. One of the first exception is the method in [2], [1] where a planar constraint is introduced to improve the restoration of the road area, assuming an approximately flat road. The single image defogging problem being ill-posed, the choice of the Bayesian approach seems adequate to set this problem as an inference problem. A first Markov Random Field (MRF) approach of the problem has been proposed recently in [3]. However, this method is not dedicated to road images. In this paper, we propose a novel MRF model of the single image defogging problem which applies to all kinds of images but can also easily be refined to obtain better results on road images using the planar constraint. A comparative study and quantitative evaluation with several state-of-the-art algorithms is presented. This evaluation demonstrates that the proposed MRF model allows to derive a new algorithm which produces better quality results, in particular in case of a noisy input image.

Abstract

International audience; Good, efficient and reliable public transportation systems are of crucial importance for all major cities today. In this paper, we propose a concrete solution to a particular problem: improve the prediction of the bus arrival time at each bus stop station on a given itinerary, by taking to account global and local traffic contexts. The main principle consists of modeling the traffic data as an image structure, adapted for applying CNN deep neural networks. The results obtained shows that the proposed approach outperforms traditional machine learning techniques, such as OLS (Ordinary Least Squares) or SVR (Support Vector Regression) with different kernels (RBF or Polynomial), with more than 18% better accuracy prediction, while being computationally faster.

Abstract

© 2015 IEEE. Despite the overwhelming availability of techniques for computation of cardiac deformation and strain with 2D echocardiography, their widespread dissemination in clinical practice is still held back by the reported low reproducibility between different solutions. This can in part be attributed to the lack of a solid and open quality assurance framework to assess and compare their performance. Building upon our previous work, we present here a new pipeline for the benchmarking of such algorithms making use of realistic 2D synthetic sequences. While the synthetic motion is fully controlled by a computational heart model, the visual appearance is extremely similar to a real ultrasound recording. The pipeline is used to generate an initial library of four sequences. ispartof: pages:756-759 ispartof: IEEE International Symposium on Biomedical Imaging vol:2015-July pages:756-759 ispartof: IEEE International Symposium on Biomedical Imaging location:New York City. US date:May - May 2015 status: published

Abstract

International audience; In today's modern cities, mobility is of crucial importance, and public transportation is particularly concerned. The main objective is to propose solutions to a given, practical problem, which specifically concerns the bus arrival time at various bus stop stations, by taking to account local traffic conditions. We show that a global prediction approach, under some global macro-parameters (e.g., total number of vehicles or pedestrians) is not feasible. This observation leads us to the introduction of a finer granularity approach, where the traffic conditions are represented in terms of a traffic density matrix. Under this new paradigm, the experimental results obtained with both linear and neural networks (NN) approaches show promising prediction performances. Thus, the NN approach yields 24% more accurate prediction performances than a basic, linear regression

Abstract

This article focuses on testing and investigating further development needs for LiDARs in self-driving cars in adverse weather. The article compares two different LiDARs (Ibeo Lux and Velodyne PUCK), which both use the 905 nm wavelengths, which are used in more than 95% of currently available LiDARs. The performance was tested and estimated in stabilized fog conditions at Cerema fog chamber facilities. This provides a good basis for repeating the same validation procedure multiple times and ensuring the right development decisions. However, performance of the LiDARs suffers when the weather conditions become adverse and visibility range decreases. A 50% reduction in target detection performance was observed over the exhaustive tests. Therefore, changing to higher wavelengths (1550 nm) was considered using redesigned 'pre-prototype LiDAR'. The preliminary results indicate that there is no reason to not use 1550 nm wavelength, which due to eye safety regulations gives an opportunity to use 20 times more power compared to the traditional 905 nm. In order to clarify the expected benefits, additional feasibility studies are still needed.

Abstract

International audience; This paper deals with a new method that exploits optical flow field properties to simplify and strengthen the original c-velocity approach (Bouchafa and Zavidovique, 2012). C-velocity is a cumulative method based on a Hough-like transform adapted to velocities that allows 3D structure identification. In case of moving cameras, the 3D scene is assumed to be composed by a set of 3D planes that could be categorized in 3 main models: horizontal, lateral and vertical planes. We prove in this paper that, by using directly pixel coordinates to create what we will call the uv-velocity space, it is possible to detect 3D planes efficiently. We conduct our experiments on the KITTI optical flow dataset (Menze and Geiger, 2015) to prove our new concept besides the effectiveness of uv-velocity in detecting planes. In addition, we show how our approach could be applied to detect free navigation area (road), urban structures like buildings and obstacles from a moving camera in the context of Advanced Driver Assistance Systems.

Abstract

dvanced Driver Assistance Systems (ADAS) based on video camera tends to be generalized in today's automotive. However, if most of these systems perform nicely in good weather conditions, they perform very poorly under adverse weather particularly under rain. We present a novel approach that aims at detecting raindrops on a car windshield using only images from an in-vehicle camera. Based on the photometric properties of raindrops, the algorithm relies on image processing technics to highlight raindrops. Its results can be further used for image restoration and vision enhancement and hence it is a valuable tool for ADAS.