Abstract

Vehicular communication networks are a type of network in which vehicles and roadside units are the communicating nodes, providing each other with information such as safety warnings and traffic information. As a cooperative approach, vehicular communications can be more effective in avoiding accidents and traffic congestion, rather than each vehicle trying to solve these problems individually. Therefore, intelligent transportation systems (ITS) have been developed to address the challenges of the safety, security, and efficacy of the current transportation systems. The field of inter vehicular communications (IVC) includes both vehicle-to-vehicle (V2V) and vehicle-to infrastructures (V2I) communication and is also known as the vehicular ad hoc network (VANET). The VANET is recognized as an important component of ITS in various national plans [1]. Direct communication between vehicles may be set up by means of mobile ad hoc networks (MANETs), which do not depend on the altered foundation. Research on MANETs covers the application prerequisites and correspondence conventions for everything from sensor systems to handheld PCs and vehicular frameworks [2].

Abstract

Most major operators use MPLS technology to manage their network via signalling and label distribution protocols. However, these protocols are complex to deploy, maintain and troubleshooting is often very difficult. The IETF has initiated the standardization of a segment routing architecture based on a simple control plane, lightweight, easyto-manage and instantiated on MPLS or IPv6. This architecture is based on the concept of source routing, in which the packet header carries the indications of the path to follow to reach its destination. Suitable for simple use cases and natively resistant to failure, more complex use cases require the resolution of technological issues for which we offer several solutions.In this thesis carried out within Orange Labs, we were interested in the instantiation of the Segment Routing architecture on the MPLS transfer plan and more particularly in traffic engineering, particularly with resource reservation. We have proposed solutions to the problems related to the hardware limitation of current routers that do not allow the expression of all constrained paths. This work is divided into two parts : (i) the proposal of algorithms for computing and encoding segment routing paths in order to bypass hardware limitations. (ii) the definition of architectural requirements and the construction of a functional proof of concept. Finally, this thesis proposes new research issues to consolidate traffic engineering tools for segment routing.; La majorité des grands opérateurs utilise la technologie MPLS pour gérer leur réseau via des protocoles de signalisation et de distributions de labels. Or, ces protocoles sont complexes à déployer, à maintenir et la résolution des pannes est souvent très difficile. L'IETF a initié la standardisation d'une architecture de routage par segments (Segment Routing) s'appuyant sur un plan de contrôle simple, léger, facile à gérer et instanciée sur MPLS ou IPv6. Cette architecture repose sur le concept de routage à la source, dans lequel l'en-tête des paquets transporte les indications du chemin à suivre pour atteindre sa destination. Adapté aux cas d'usages simples et offrant nativement une résistance aux pannes, les cas d'usages plus complexes exigent de résoudre des verrous technologiques pour lesquels nous proposons plusieurs solutions. Dans cette thèse effectuée au sein d'Orange Labs, nous nous sommes intéressés à l'instanciation de l'architecture Segment Routing sur le plan de transfert MPLS et plus particulièrement à l'ingénierie de trafic, notamment avec réservation de ressources. Nous avons proposé des solutions aux problèmes liés à la limitation matérielle des routeurs actuels ne permettant pas l'expression de tous les chemins contraints. Ce travail est divisé en deux parties : (i) la proposition d'algorithmes de calcul et d'encodage de chemins de routage par segment afin de contourner les limitations matérielles. (ii) la définition des exigences architecturales et la construction d'une preuve de concept fonctionnelle. Enfin, cette thèse propose de nouvelles pistes d'études afin de consolider les outils d'ingénierie de trafic pour le routage par segment.

Abstract

International audience; A prerequisite to the design of future Advanced Driver Assistance Systems for cars is a sensing system providing all the information required for high-level driving assistance tasks. Carsense is a European project whose purpose is to develop such a new sensing system. It will combine different sensors (laser, radar and video) and will rely on the fusion of the information coming from these sensors in order to achieve better accuracy, robustness and an increase of the information content. This paper demonstrates the interest of using probabilistic reasoning techniques to address this challenging multi-sensor data fusion problem. The approach used is called Bayesian Programming. It is a general approach based on an implementation of the Bayesian theory. It was introduced first to design robot control programs but its scope of application is much broader and it can be used whenever one has to deal with problems involving uncertain or incomplete knowledge.

Abstract

International audience; We consider the problem of coordinating a set of automated vehicles at an intersection with no traffic light. The priority-based coordination framework is adopted to separate the problem into a priority assignment problem and a vehicle control problem under fixed priorities. This framework ensures good properties like safety (collision-free trajectories, brake-safe control) and liveness (no gridlock). We propose a decentralized Model Predictive Control (MPC) approach where vehicles solve local optimization problems in parallel, ensuring them to cross the intersection smoothly. The proposed decentralized MPC scheme considers the requirements of efficiency, comfort and fuel economy and ensures the smooth behaviors of vehicles. Moreover, it maintains the system-wide safety property of the priority-based framework. Simulations are performed to illustrate the benefits of our approach. Document type: Conference object

Abstract

International audience; Machine-learning approaches for mental workload (MW) estimation by using the user brain activity went through a rapid expansion in the last decades. In fact, these techniques allow now to measure the MW with a high time resolution (e.g. few seconds). Despite such advancements, one of the outstanding problems of these techniques regards their ability to maintain a high reliability over time (e.g. high accuracy of classification even across consecutive days) without performing any recalibration procedure. Such characteristic will be highly desirable in real world applications, in which human operators could use such approach without undergo a daily training of the device. In this work, we reported that if a simple classifier is calibrated by using a low number of brain spectral features, between those ones strictly related to the MW (i.e. Frontal and Occipital Theta and Parietal Alpha rhythms), those features will make the classifier performance stable over time. In other words, the discrimination accuracy achieved by the classifier will not degrade significantly across different days (i.e. until one week). The methodology has been tested on twelve Air Traffic Controls (ATCOs) trainees while performing different Air Traffic Management (ATM) scenarios under three different difficulty levels.

Abstract

International audience; We investigate the mechanisms of attentional orienting in a 360-degree virtual environments. Through the use of Posner's paradigm, we study the effects of different attentional guidance techniques designed to improve information processing. The most efficient technique will be applied to a procedure learning tool in virtual reality and a remote air traffic control tower. The eye-tracker allows us to explore the differential effects of overt and covert orienting, to estimate the effectiveness of visual research and to use it as a technique for interaction in virtual reality.

Abstract

International audience; The measurement of the operators' workload is an important aspect of usage-oriented design of professional systems. In domains such as avionics, air traffic management or mission systems, being able to quantify the operators' workload under stress, and in potentially demanding physical and mental conditions, is mandatory to anticipate overload and prevent human errors. Current approaches to workload estimation rely mainly on experimentation in simulation as an approach that has proven its efficiency for the identification of bad system and/or user interface design. Even if one cannot expect to totally avoid experimenting, given the complexity of the issue of workload computation, a priori estimation of workload might be an interesting tool to pre-validate a design in order to save some time in the experimentation phase and facilitate the analysis of overload situations that appear during experimentation. Various approaches to the a priori measurement of workload have been proposed: performance-based, physiological and subjective measures. Although performance and physiological measures of workload may be more precise, subjective measures are more practical, easier and less costly to use. For these reasons, they have been applied to many complex domains. The experience, the skills and the level of training of the operator have been identified in the literature as being important human factors. Nevertheless, these parameters have not been deeply analyzed in the context of workload estimation. In this paper, we develop a predictive workload model based on the analysis of the tasks assigned to a human operator. We propose to use mental representations of tasks, human actors, human roles, knowledge and abilities. We then propose to estimate the operator's workload with reference to his experience and training, the load over time and the task complexity. Our approach is illustrated on an airborne maritime surveillance use-case, in the context of the French Medusa project.

Abstract

International audience; A prerequisite to the design of future Advanced Driver Assistance Systems for cars is a sensing system providing all the information required for high-level driving assistance tasks. Carsense is a European project whose purpose is to develop such a new sensing system. It will combine different sensors (laser, radar and video) and will rely on the fusion of the information coming from these sensors in order to achieve better accuracy, robustness and an increase of the information content. This paper demonstrates the interest of using probabilistic reasoning techniques to address this challenging multi-sensor data fusion problem. The approach used is called Bayesian Programming. It is a general approach based on an implementation of the Bayesian theory. It was introduced first to design robot control programs but its scope of application is much broader and it can be used whenever one has to deal with problems involving uncertain or incomplete knowledge.

Abstract

International audience; We investigated the contribution of specific HCI concepts to provide multimodal information to Air Traffic Controlers in the context of Remote Control Towers (i.e. when an airport is controlled from a distant location). We considered interactive spatial sound, tactile stimulation and body movements to design four different interaction and feedback modalities. Each of these modalities have been designed to provide specific solutions to typical Air Traffic Control identified use cases. Sixteen professional Air Traffic Controllers (ATCos) participated in the experiment, which was structured in four distinct scenarios. ATCos were immersed in an ecological setup, in which they were asked to control (i) one airport without augmentations modalities, (ii) two airports without augmentations, (iii) one airport with augmentations and (iv) two airports with augmentations. These experimental conditions constituted the four distinct experimental scenarios. Behavioral results shown a significant increase in overall participants’ performance when augmentation modalities were activated in remote control tower operations for one airport.

Abstract

International audience; This paper presents an approach for formation control of autonomous vehicles traversing along a multi-lane road with obstacles and traffic. A major challenge in this problem is a requirement for integrating individual vehicle behaviors such as lane-keeping and collision avoidance with a global formation maintenance behavior. We propose a hierarchical Model Predictive Control (MPC) approach. The desired formation is modeled as a virtual structure evolving curvilinearly along a centerline, and vehicle configurations are expressed as curvilinear relative longitudinal and lateral offsets from the virtual center. At high-level, the trajectory generation of the virtual center is achieved through an MPC framework, which allows various on-road driving constraints to be considered in the optimization. At low-level, a local MPC controller computes the vehicle inputs in order to track the desired trajectory, taking into account more personalized driving constraints. High-fidelity simulations show that the proposed approach drives vehicles to the desired formation while retains some freedom for individual vehicle behaviors.