Cette thèse est dédiée à la pré-calibration des nouveaux systèmes d’aides à la conduite (ADAS). Le développement de ces systèmes est devenu aujourd’hui un axe de recherche stratégique pour les constructeurs automobiles dans le but de proposer des véhicules plus sûrs et moins énergivores. Cette thèse contribue à une vision méthodologique multi-critère, multi-modèle et multi-scénario. Elle en propose une instanciation particulière pour la pré-calibration spécifique au Lane Centering Assistance (LCA). Elle s’appuie sur des modèles dynamiques de complexité juste nécessaire du véhicule et de son environnement pour, dans le cadre du formalisme H2/H∞, formaliser et arbitrer les compromis entre performance de suivi de voie, confort des passagers et robustesse. Les critères élaborés sont définis de manière à être d’interprétation aisée, car directement liés à la physique, et facilement calculables. Ils s’appuient sur des modèles de perturbations exogènes (e.g. courbure de la route ou rafale de vent) et de véhicules multiples mais représentatifs, de manière à réduire autant que possible le pessimisme tout en embrassant l’ensemble des situations réalistes. Des simulations et des essais sur véhicules démontrent l’intérêt de l’approche. / This thesis deals with the tuning of the new Advanced Driving Assistance Systems (ADAS). The development of these systems has become nowadays a strategic line of research for the automotive industry towards the conception of safer and fuel-efficient vehicles.This thesis contributes to a multi-criterion, multi-modeland multi-scenario methodological vision of the tuning process. It is presented through a specific application of the tuning of the Lane Centering Assistance (LCA). It relies on vehicle and environment’s dynamical models of adequate complexity in the aim of formalizing and managing, in a H2/H∞ framework, the trade-off between performance, comfort and robustness. The formulated criteria are easy to compute and defined in a way to be understandable, closely linked to practical specifications. The whole methodology is driven by the research of a pertinent trade-off between realism (being as closest as possible to reality) and complexity (quick evaluation of the criterion). The efficiency and the robustness of the approach is demonstrated through high-fidelity simulations and numerous tests on real vehicles.

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The demand for Advanced Driver Assistance System (ADAS) applications is increasing day by day and their development requires efficient prototyping and real time testing. ADTF (Automotive Data and Time Triggered Framework) is a software tool from Elektrobit which is used for Development, Validation and Visualization of Vision based applications, mainly for ADAS and Autonomous driving. With the help of ADTF tool, Image or Video data can be recorded and visualized and also the testing of data can be processed both on-line and off-line. The development of ADAS applications needs image and video processing and the algorithm has to be highly efficient and must satisfy Real-time requirements. The main objective of this research would be to integrate OpenCV library with ADTF cross platform. OpenCV libraries provide efficient image processing algorithms which can be used with ADTF for quick benchmarking and testing. An ADTF filter framework has been developed where the OpenCV algorithms can be directly used and the testing of the framework is carried out with .DAT and image files with a modular approach. CMake is also explained in this thesis to build the system with ease of use. The ADTF filters are developed in Microsoft Visual Studio 2010 in C++ and OpenMP API are used for Parallel programming approach.

Inspired by the idea of vision zero, there is a lot of work that needs to be done in the field of advance driver assistance systems to develop more safer systems. Driver intention detection with a prediction of upcoming behavior of the driver is one possible solution to reduce the fatalities in road traffic. Driver intention detection provides an early warning of the driver's behavior to an Advanced Driver Assistance Systems (ADAS) and at the same time reduces the risk of non-essential warnings. This will significantly reduce the problem of warning dilemma and the system will become more safer. A driving maneuver prediction can be regarded as an implementation of driver's behavior. So the aim of this thesis is to determine the driver's intention by early prediction of a driving maneuver using Controller Area Network (CAN) bus data. The focus of this thesis is to optimize and further develop an algorithm for driver intention detection with fuzzy logic and edit distance method. At first the basics concerning driver's intention detection are described as there exists different ways to determine it. This work basically uses CAN bus data to determine a driver's intention. The algorithm overview with the design parameters are described next to have an idea about the functioning of the algorithm. Then different implementation tasks are explained for optimization and further development of the algorithm. The main aim to execute these implementation tasks is to improve the overall performance of the algorithm concerning True Positive Rate (TPR), False Positive Rate (FPR) and earliness values. At the end, the results are validated to check the algorithm performance with different possibilities and a test drive is performed to evaluate the real time capability of the algorithm. Lastly the use of driver intention detection algorithm for an ADAS to make it more safer is described in details. The early warning information can be feed to an ADAS, for example, an automatic collision avoidance or a lane change assistance ADAS to further improve safety for these systems.

Made available in DSpace on 2015-04-14T14:41:39Z (GMT). No. of bitstreams: 1 442656.pdf: 1220602 bytes, checksum: 4de97658564c1d73a8c5fe8ab575f7ed (MD5) Previous issue date: 2012-10-08 / Today, China is a country well known and unknown, a big nation of Asia and the most populous of the Earth. However, although have a history dating back thousands of years, China is also an unknown country for most of the world. But because of the Olympic Games of 2008, China became famous. In this study, propose to discuss the origin of the games what the word game means? And what is the definition. For this purpose, we use the authors as Huizinga and speak in his book that the game is a function of life, but is not capable of exact definition in logical, biological or aesthetic terms. As we know in antiquity, in most of games, a competition was devoid of objective, but as the game and culture of competition were developed, the objective of game became stronger. Because of these characteristics and the appearance of the Olympic Games, we find relevant to study the culture that emerges from jokes. / Hoje em dia, a China ? conhecida por ser um pa?s muito desenvolvido, uma grande na??o da ?sia e a mais populosa da Terra. Contudo, apesar de possuir uma hist?ria que remonta a milhares de anos, a China tamb?m ? um pa?s desconhecido para grande parte do mundo. Por?m, com a realiza??o dos Jogos Ol?mpicos de 2008 a China passou a ser mais conhecida. Nesse trabalho, portanto, propomos discutir a origem dos jogos o que significa a palavra jogo? e qual ? a sua defini??o. Para tanto, lan?amos m?o de autores como Huizinga, que fala no seu livro Homo Ludens que o jogo ? uma fun??o da vida, mas n?o ? pass?vel de defini??o exata em termos l?gicos, biol?gicos ou est?ticos. Como sabemos na antiguidade, na maioria dos jogos, a competi??o era desprovida de objetivo, mas na medida em que ocorreu o desenvolvimento do jogo e de sua cultura de disputa, o seu objetivo ficou cada vez mais forte. Por conta dessas caracter?sticas e do surgimento dos Jogos Ol?mpicos que achamos pertinente estudar ent?o a cultura que emerge das brincadeiras.

Les systèmes avancés d'assistance à la conduite automobile ont comme objectif d'apporter de l'intelligence au véhicule en l'équipant d'un ensemble de capteurs extéroceptifs et proprioceptifs. L'approche classique consiste à doter le véhicule de capacités de perception pour lui permettre de connaître son environnement sans interaction intelligente avec cet environnement. Cette approche dite autonome a montré cependant des limites liées à la portée et à la précision des capteurs utilisés. Le grand développement et l'évolution rapide dans le domaine des télécommunications sans fil ont rendu possible de re-penser le modèle de conduite automobile en introduisant la notion de coopération entre les véhicules. Le travail de standardisation d'un protocole de communication est en cours d'élaboration mais le succès de l'introduction de cette technologie dans le domaine véhiculaire est fortement corrélé à la mise en oeuvre de services et d'applications réalistes et à forte valeur ajoutée pour l'utilisateur final. L'établissement d'une liste d'applications réalistes requiert un soin particulier surtout que les applications proposées dans la littérature ne sont pas actuellement réalisables techniquement et leur acceptabilité par le grand public n'est pas encore établie ni garantie. Pour combler ce manque, nous avons essayé à travers cette thèse de nous focaliser sur la conception de nouveaux systèmes coopératifs d'assistance à la conduite pour l'amélioration de la sécurité routière. Le but de la thèse est de prouver la faisabilité des applications coopératives avec le matériel de communication existant et d'étudier l'apport de cette technologie dans la prévention des collisions entre les véhicules. Le travail réalisé pendant la thèse couvre un domaine très large. D'abord, nous proposons une architecture de communication sans fil basée sur la norme 802.11 et nous étudions à travers des campagnes de test l'adéquation de cette architecture avec les exigences des applications coopératives en termes de portée, de débit et de latence. Sur la base de cette architecture et après une étude statistique des accidents sur les routes, nous avons expérimenté notre système dans deux types d'applications dans lesquelles la coopération peut avoir une valeur ajoutée par rapport au système autonome : - La prévention de risque à l'approche d'une intersection - L'assistance lors d'un changement de voie Pour chacun de ces deux scénarios, nous avons mis en application un système de prédiction et de prévention de risque de collision entre les véhicules à travers des tests réels sur la plateforme expérimentale de véhicules intelligents LaRA.

The World Health Organization predicts that by the year 2030, road traffic injuries will be one of the top five leading causes of death. Many of these deaths and injuries can be prevented by driving cars properly equipped with state-of-the-art safety and driver assistance systems. Some examples are auto-brake and auto-collision avoidance which are becoming more and more popular on the market today. A recent study by a Swedish insurance company has shown that on roadswith speeds up to 50 km/h an auto-brake system can reduce personal injuries by up to 64 percent. In fact in an estimated 40 percent of crashes, the auto-brake reduced the effects to the degree that no personal injury was sustained. It is imperative that these so called Advanced Driver Assistance Systems, to be really effective, have good situational awareness. It is important that they have adequate information of the vehicle’s immediate surroundings. Where are other cars, pedestrians or motorcycles relative to our own vehicle? How fast are they driving and in which lane? How is our own vehicle driving? Are there objects in the way of our own vehicle’s intended path? These and many more questions can be answered by a properly designed system for situational awareness. In this thesis we design and evaluate, both quantitatively and qualitatively, sensor fusion algorithms for multi-target tracking. We use a combination of camera and radar information to perform fusion and find relevant objects in a cluttered environment. The combination of these two sensors is very interesting because of their complementary attributes. The radar system has high range resolution but poor bearing resolution. The camera system on the other hand has a very high bearing resolution. This is very promising, with the potential to substantially increase the accuracy of the tracking system compared to just using one of the two. We have also designed algorithms for path prediction and a first threat awareness logic which are both qualitively evaluated.

L'utilisation d'algorithmes de vision permettrait d'élargir le domaine d'application des systèmes d'aide à la conduite à d'autres situations telles que : les scènes urbaines ou les conditions météorologiques dégradées. À cette fin, trois nouvelles applications sont étudiées dans cette thèse pour la pluie et les feux tricolores. La pluie est la condition météorologique dégradée la plus fréquente. Nous comparons les modèles physiques et photométriques existants pour la pluie et les gouttes de pluie. Lors d'une conduite en temps de pluie de jour, les gouttes sur le pare-brise diminuent considérablement la visibilité du conducteur. Lorsqu'elles sont vue par une camera embarquée standard celles-ci apparaissent défocalisées. Ainsi, nous proposons de détecter ces gouttes hors-focus en utilisant soit une approche par manque de gradients soit par l'évaluation locale du flou. Lors d'une conduite de nuit sous la pluie, ce sont les phares qui paradoxalement diminuent la visibilité car leur lumière est réfléchie par les gouttes vers le conducteur. Nous appuyant sur la conception d'un simulateur physique, nous proposons un éclairage adaptatif qui illuminerait la scène sans éclairer les gouttes qui tombent. Les résultats de notre simulateur et le premier prototype construit montre que l'idée avancée pourrait efficacement améliorer la visibilité générale d'une scène. D'autre part, nous étudions la détection et le suivi de gouttes de pluie à grande vitesse. Les feux tricolores ont un rôle crucial dans la compréhension des scènes urbaines. Bien qu'il existe déjà des systèmes de détection de feux tricolores, les algorithmes actuels ne fonctionnent que dans des conditions simples. Ainsi, nous avons développé un algorithme de détection de feux tricolores qui utilise une détection en niveau de gris des spots lumineux et une classification par reconnaissance de modèle. L'approche ainsi conçue est assez flexible pour détecter différents types de feux tricolores même avec une camera à faible dynamique. Notre proposition a été évaluée sur des séquences acquises en France, Chine et Suisse.

The study area, which will be open to tourism in Kusadasi (Aydin), has steep and high cliffs near the Aegean coast. In the area where some slidings and rockfall problems occurred in the past the geological hazards should be investigated and nature-friendly remedial measures should be taken. The aim of this study is to perform engineering geological studies to:(i) search geological hazards, (ii) reveal the slope stability problems, (iii) recommend nature-friendly solutions in order to prevent/minimize the hazards and (iv)compare the results obtained from 2-D and 3-D rockfall analyses. To accomplish these tasks, the geological survey was performed, the information about the discontinuities was collected by means of scanline surveys, the rock samples were collected, the in-situ and laboratory tests were carried out, the slope stability and rockfall analyses were performed for different slope conditions, remedial measures were offered for the problematical areas considering the data obtained and the results of 2-D and 3-D analysis were compared. Under the light of these studies, rock removal, drainage, greening (vegetation), filling the caverns, wall building and erosion prevention were offered as remedial measures. The comparison of the 2-D and 3-D rockfall analyses shows that the end points and bounce height values are different for each method. The differences between the 2-D and 3-D model originate from the slope geometry, the algorithm used in the software and the different input parameters. According to the field observations, the 2-D model is more realistic than 3-D model.

Driver's distraction is one of the main areas, which researchers are focusing on, in design of Advanced Drivers Assistance Systems (ADASs). Head pose and eye-gaze direction are two reliable indicators of a driver's gaze and the current focus of attention. Compared with other methods that make use of head pose only, methods that combine eye information can achieve higher accuracy. The naturalistic driving environment always presents unique challenges (e.g., unstable illumination, jolts, etc.) to video-based gaze estimation and tracking systems. Some methods can achieve relatively high proficiency in the stationary laboratory environment, but they may not be suitable for the unstable driving environment. In addition, performing in real time or near-real time is another consideration for gaze estimation in an ADAS. Therefore, these special challenges need to be overcome to design ADASs. In this thesis, we proposed a new driver's gaze zone estimation framework designed for the naturalistic driving environment. The framework combines head and eye information to estimate the gaze zone of the driver in both daytime and nighttime. The framework is composed of five main components: Facial Landmark Detection, Head Pose Estimation, Iris Center Detection, Upper Eyelid Information Extraction, and Gaze Zone Estimation. First, Constrained Local Neural Field (CLNF) is applied to obtain the facial landmarks in the image plane and the 3D model of the face in the object frame. In addition, extracting region of interest (ROI) is utilized as an optimization strategy for CLNF facial landmark detection. Second, head pose estimation can be regarded as a Perspective-n-Point (PnP) problem. Levenberg-Marquardt optimization method is used to solve the PnP problem based on the 2D landmark locations in the image plane and their corresponding 3D locations in the object frame. Third, a regression model-based method is employed to obtain the iris center from eye landmarks detected in the previous part. For upper eyelid information extraction, a quadratic function is utilized to model the upper eyelid, and the second-order coefficient is extracted. Finally, the head pose and the eye information are combined to form a feature vector, and Random Decision Forest classifier is utilized to estimate the current gaze zone of the driver from the feature vector extracted. The experiment is carried out in the realistic driving environment in both daytime and nighttime with three volunteers by Kinect sensor V2 for Windows that is put at the back of windshield. Weighted and unweighted accuracy are utilized as evaluation metrics in gaze zone estimation. Weighted accuracy evaluates gaze zones with different significance while unweighted accuracy treats each gaze zone equally. Experiment results show that the gaze zone estimation framework proposed in this work has better performance compared to the reference in the daytime. The weighted and unweighted accuracy of gaze zone estimation reach 96.6% and 95.0% for daytime, respectively. For nighttime, the weighted and unweighted accuracy can reach 96% and 91.4%.