Contribution a la connaissance des effets locaux dus au vent sur les constructions : etude en soufflerie atmospherique et confrontation internationale

SIGLECNRS T 57016 / INIST-CNRS - Institut de l'Information Scientifique et TechniqueFRFranc

Conception d’un système d’alerte coopératif basé sur les ADAS communicants

International audienceIn this paper we propose a new methodology of development dedicated to cooperative ADAS. This methodology led us to implement a new framework for prototyping a communicating ADAS system. Within this framework, we combine the data from multiple modules: a vision module, a V2V communication module and Geo‐localization GPS module, in order to accomplish a cooperative warning system. To achieve this goal, we have developed a prototyping system based on the principle of augmented reality, in which we replay real data and change the characteristics of the communication system. The GPS data and routing protocols were crucial elements for V2V communication simulation made with ns‐2 simulator. We conducted different scenarios on real experimental platform consists of LaRA vehicles. Multiple results are presented to show up the compatibility and the performance efficiency of real‐time multi sensors in an integrated framework for collision avoidance applications. The implementation of the warning system was used to estimate the number of pre‐collisions detected in both real and simulated situations. The difference between these two situations was analyzed for several scenarios corresponding to different road situations. The results showed that the simulation of V2V communication provide additional data that improve the implementation of these new ADAS and to assess their performance.Cet article propose une nouvelle méthodologie de développement dédiée aux systèmes ADAS coopératifs. Cette méthodologie nous a conduit à mettre en œuvre un nouveau cadre de prototypage des systèmes ADAS communicants. Dans ce cadre, nous combinons les données de plusieurs modules, un module de vision, un module de communication V2V et un module de géolocalisation GPS, pour réaliser un système d’alerte coopératif. Afin d’atteindre cet objectif, nous avons développé un système de prototypage basé sur le principe de la réalité augmentée, dans lequel nous pouvons rejouer des données réelles et modifier les caractéristiques du système de communication. Les données du système de géolocalisation GPS et les protocoles de routage ont été des éléments primordiaux pour la simulation de la communication V2V réalisée avec le simulateur ns‐2. Nous avons effectué différents scénarios réels sur la plate‐forme du prototype LaRA composée de véhicules instrumentés. Plusieurs résultats sont présentés pour illustrer la compatibilité et l’efficacité de l’intégration des données réelles issues de plusieurs capteurs dans ce nouveau système de prototypage pour les applications d’alerte. La mise en œuvre du système d’alerte a permis d’estimer le nombre de pré‐collisions détectées dans deux situations, une réelle et une simulée. L’écart entre ces deux configurations a été étudié et analysé pour plusieurs scénarios qui correspondent aux différentes situations routières. Les résultats ont montré que les simulations de communications V2V fournissent des données complémentaires qui améliorent la mise en œuvre de ces nouveaux ADAS et permettent d’évaluer leurs performances

Practical Testing Application of Travel Time Estimation Using Applied Monte Carlo Method and Adaptive Estimation from Probes

International audienceThis paper presents a practical testing of two different methods to estimate the travel time in urban areas. The purpose behind this testing is to validate the behavior of each method regarding the road aspect in urban areas. The first method is based on Monte Carlo Method and the second one is based on adaptive estimation from probes. Both methods were modified to be adapted to our case and also to the nature of our data. The paper also describes an experiment with real-world data that was used in the testing of the two methods. Moreover it contains the architecture of the system used in order to make the tests. This work yeilded interesting results based on real-world experiments which give clear feedback about the application of the two methods to compute the travel time estimation per road section that can be used for processing the historical database as well as real time data. In general this work is a suitable validation of the two methods and encouraging for our future perspectives

Travel time estimation using cooperative probes vehicles

International audienceThis document presents an application using coop-ertive probes data based on real-world field testing to estimate travel time by applying adaptive Monte Carlo method and adaptive estimation from probes. The purpose is conducting the two methods to check which one gives better results in the context of database enrichment. Moreover the process should be run on the historical database and also it has to do real time computations. The innovative part of this work can be summed up in three sections. The first one is related to digital maping aspect, the second section is regarding the map-matching and GPS errors, and finally the adaptive estimation of travel time

Will Capsule Networks overcome Convolutional Neural Networks on Pedestrian Walking Direction ?*

International audienceThousands of people are dying every year due to road accidents; in fact 23% of world fatal accidents are pedestrians related, where 40% of them occur in Africa as reported by the World Health Organisation (WHO). Predicting the walking direction of a pedestrian could help to avoid an eventual accident. Existing studies can not handle pose and orientation transformations of the input object contrary to our proposed method. This paper describes a novel approach to determine the pedestrian orientation using Capsule Networks (CapsNet) based scheme. CapsNet are a new deep learning architecture that overcome some limitations of the existing studies, they are group of neurons invariant to rotation and affine transformations, which represent a specific interest to this work. Capsule Networks predicts the walking directions of pedestrians to prevent such mortal accidents, using four main walking directions (front, back, left and right).For this purpose, a new pedestrians dataset gathered from the most popular cities in Morocco is collected to be studied and used as a proof of the proposed approach. To enhance this proposed approach, we evaluated it using Daimler dataset and compared it to Convolutional Neural Networks (CNN) architectures. Experimental results reveal that the performance of the proposed approach reaches an accuracy of 97.60% on daimler dataset and 73.64% on our Moroccan collected dataset

An Application of the Sequential Monte Carlo to Increase the Accuracy of Travel Time Estimation in Urban Areas

International audienceThis paper presents an application of the Sequential Monte Carlo that will help to increase the accuracy of travel time estimations in our historical data. Our estimation filter is based on the Monte Carlo Method and was modeled in such a way as to be applicable to our new kind of data in order to estimate travel time per section of road. We took into consideration the delay time while changing the sections to symbolize the delay due to traffic lights or crossroads. We worked on an urban zone of Rouen, a French city, to evaluate our application. In this application, information is collected from a specific GPS system that warns drivers of the location of both fixed and mobile speed radars. Unlike the classical GPS system, this system is characterized by the data flow frequency where the GPS data is received from the probe vehicles at one minute intervals. After receiving the data we apply the map matching method in order to correct the GPS errors. Also, our geo-referencing system has special features; each road or section of road is formed by nodes and segments, and the intersection between each section is called a PUMAS points. The PUMAS Points are GPS coordinate points on a digital map which can be propagated or moved without cost, providing total flexibility to mesh a city or rural area. Over all the performance of the filter estimator is around 85% if we set our threshold at 50%

Cooperation of Passive Vision Systems in Detection and Tracking of Pedestrians

This work presents a cooperative approach for detecting and tracking pedestrians in an urban environment. Its originality lies in the cooperation of two vision systems. A monocular vision system retrieves feature elements and these elements are visualized. However, false detection can occur due to objects whose outline is similar to that of a pedestrian. This problem is solved by the introduction of an auto-adaptive stereovision algorithm that recovers all the vertical 3D segments of the scene. This cooperation supplies a fast and robust method for detecting pedestrian presence. Then, it allows for pedestrian tracking through multiple images

Accurate scale estimation based on unsynchronized camera network

International audienc

Visual odometry with unsynchronized multi-cameras setup for intelligent vehicle application

International audienc