Cette thèse en Cognitique se focalise sur la « Conception Centrée sur l'Humain » (Human Centred Design) de futures assistances à la conduite automobile, adaptées aux conducteurs âgés (ou Elderly Adapted Driver Assistance Systems).Pour ce faire, la démarche proposée repose sur une approche et une méthodologie pluridisciplinaire. Sur le plan ergonomique, il s'agit de mieux connaître les spécificitésde la population des conducteurs âgés, dans le but d'identifier des difficultés et des besoins en assistance. A cette fin, 76 conducteurs âgés (de 70 à 87 ans) ont conduitun véhicule instrumenté, immergé dans le trafic. Le corpus de données comporte2100 kilomètres de conduite et 1400 situations de conduite autoévaluées par lesconducteurs, complétés par 6 Focus Group (30 conducteurs âgés).Le second volet, relevant d'une démarche d'Ingénierie Cognitive, vise à concevoir et développer des fonctions de « monitorage » à partir du corpus de données. L'objectif est de disposer de modèles et de fonctions d'analyse temps-réel capables (1) de superviser l'activité de conduite des conducteurs âgés (2) en regard du contexte ou des risques situationnels, afin de (3) diagnostiquer des difficultés ou erreurs de conduite, à des fins d’adaptativité des assistances. Des fonctions de monitorage en lien avec les contrôles de base du véhicule (gestion de la vitesse, positionnement dans la voie et la gestion de l'espace inter-véhiculaire avant) sont développées. Sur cette base, des fonctions de monitorage plus intégrées pour l'aide aux franchissements d'intersections (Tourne-à-Gauche) et l'assistance à l'insertion sur voies rapides (et au changement de voie) sont également proposées. / This thesis in Cognitics presents a Human Centered Design approach for thedevelopment of future driving assistance systems dedicated to elderly drivers orElderly Adapted Driver Assistance Systems (E-ADAS).To do so, this work relies on a multi-disciplinary approach for data collection andanalysis. Regarding Ergonomics, the aim is to better understand the specificrequirements of this population in order to identify their actual difficulties and actualneeds of assistance. In this frame, 76 drivers (aged from 70 to 87 years old) took partto an on-the-road experiment, driving an instrumented car. The dataset includes2100 km of ecological driving data and 1400 auto-evaluated driving situations,completed by 6 Focus Groups (involving 30 elderly drivers).The second part of this research, relying on Cognitive Engineering, explores thedesign and implementation of monitoring functions based on the aforementioneddataset. The objective is to have real-time models and analytical functions, able to:(1) supervise the driving activity as realized by an elderly driver, (2) taking in toconsideration the driving context or situational risks (3) in order to detect difficulties ordriving errors. Beyond this thesis, these diagnostics will have to be integrated inassistive systems to better adapt their support to the specific needs of elderly drivers.Specific monitoring functions related to basic vehicle control (speed management,lane positioning and headway regulation) are presented. Based on these results,integrated monitoring functions for intersection crossings in Left-Turn manoeuver,highway merging assistance, and, more broadly, lane change assistance areintroduced.

Driving safety is the major issue not only for the drivers, but also for the government. The happening rate of traffic accidents is the critical benchmark of traffic improvement for the Ministry of Communications. Even the government officials constantly urge the drivers not to drunk driving, or over speed driving, the happening rate still cannot be decreased largely. Most of accidents are made by the careless or ignorant driving habits. With the evolution of driving safety technology, the driving assistance system helps drivers to avoid the collision and lower the happening rate of accidents. Among these driving safety technologies, collision detection system is well-known one. It can detect not only any object around the vehicle, but also notify the drivers to stop passively or stop the vehicle actively. One major function of the collision detection system is used to eliminate the blind spots for the drivers. Some blind spots are generated by the rigid structure of vehicle which is designed to protect the inside passengers and not allowed to change. Fortunately, with the evolution of Augmented Reality and the lower manufacturing cost of video equipment, Driving Lens is targeted to eliminate the blind spots for the drivers. Currently, there are still some limitations about the existing products such as the around view monitor or rear view monitor. In order to improve the driving safety and enhance the driving experience, Driving Lens will offer different customized solutions for the driver such as 180 degree front view without the blind spots behind the pillars, and these solutions won’t be limited by the specific auto brand or vehicle model.

Dans le cadre de systèmes d’aide à la conduite, nous avons contribué aux approches de stéréovision pour l’extraction de contour, la mise en correspondance des images stéréoscopiques et la détection de véhicules. L’extraction de contour réalisée est basée sur le concept declivity line que nous avons proposé. La declivity line est construite en liant des déclivités selon leur position relative et similarité d’intensité. L’extraction de contour est obtenue en filtrant les declivity lines construites basées sur leurs caractéristiques. Les résultats expérimentaux montrent que la declivity lines méthode extrait plus de l’informations utiles comparées à l’opérateur déclivité qui les a filtrées. Des points de contour sont ensuite mis en correspondance en utilisant la programmation dynamique et les caractéristiques de declivity lines pour réduire le nombre de faux appariements. Dans notre méthode de mise en correspondance, la declivity lines contribue à la reconstruction détaillée de la scène 3D. Finalement, la caractéristique symétrie des véhicules sont exploitées comme critère pour la détection de véhicule. Pour ce faire, nous étendons le concept de carte de symétrie monoculaire à la stéréovision. En conséquence, en effectuant la détection de véhicule sur la carte de disparité, une carte de symétrie (axe; largeur; disparity) est construite au lieu d’une carte de symétrie (axe; largeur). Dans notre concept, des obstacles sont examinés à différentes profondeurs pour éviter la perturbation de la scène complexe dont le concept monoculaire souffre. / In the framework of driving assistance systems, we contributed to stereo vision approaches for edge extraction, matching of stereoscopic pair of images and vehicles detection. Edge extraction is performed based on the concept of declivity line we introduced. Declivity line is constructed by connecting declivities according to their relative position and intensity similarity. Edge extraction is obtained by filtering constructed declivity lines based on their characteristics. Experimental results show that declivity line method extracts additional useful information compared to declivity operator which filtered them out. Edge points of declivity lines are then matched using dynamic programming, and characteristics of declivity line reduce the number of false matching. In our matching method, declivity line contributes to detailed reconstruction of 3D scene. Finally, symmetrical characteristic of vehicles are exploited as a criterion for their detection. To do so, we extend the monocular concept of symmetry map to stereo concept. Consequently, by performing vehicle detection on disparity map, a (axis; width; disparity) symmetry map is constructed instead of an (axis; width) symmetry map. In our stereo concept, obstacles are examined at different depths thus avoiding disturbance of complex scene from which monocular concept suffers.

Mobility is a topic of great interest in research and engineering since critical aspects such as safety, traffic efficiency, and environmental sustainability still represent wide open challenges for researchers and engineers. In this thesis, at first, we address the cooperative driving safety problem both from a centralized and decentralized perspective. Then we address the problem of optimal energy management of hybrid vehicles to improve environmental sustainability, and finally, we develop an intersection management systems for Connected Autonomous Vehicle to maximize the traffic efficiency at an intersection. To address the first two topics, we define a common framework. Both the cooperative safety and the energy management for Hybrid Electric Vehicle requires to model the driver behavior. In the first case, we are interested in evaluating the safety of the driver’s intentions, while in the second case, we are interested in predicting the future velocity profile to optimize energy management in a fixed time horizon. The framework is the Co-Driver, which is, in short, a bio-inspired agent able both to model and to imitate a human driver. It is based on a layered control structure based on the generation of atomic human-like longitudinal maneuvers that compete with each other like affordances. To address driving safety, the Co-Driver behaves like a safe driver, and its behavior is compared to the actual driver to understand if he/she is acting safely and providing warnings if not. In the energy management problem, the Co-Driver aims at imitating the driver to predict the future velocity. The Co-Driver generates a set of possible maneuvers and selects one of them, imitating the action selection process of the driver. At first, we address the problem of safety by developing and investigating a framework for Advanced Driving Assistance Systems (ADAS) built on the Co-Driver. We developed and investigated this framework in an innovative context of new intelligent road infrastructure, where vehicles and roads communicate. The infrastructure that allows the roads to interact with vehicles and the environment is the topic of a research project called SAFESTRIP. This project is about deploying innovative sensors and communication devices on the road that communicate with all vehicles. Including vehicles that are equipped with Vehicle-To-Everything (V2X) technology and vehicles that are not, using an interface (HMI) on smart-phones. Co-Driver-based ADAS systems exploit connections between vehicles and (smart) roads provided by SAFESTRIP to cover several safety-critical use cases: pedestrian protection, wrong-way vehicles on-ramps, work-zones on roads and intersections. The ADAS provide personalized warning messages that account for the adaptive driver behavior to maximize the acceptance of the system. The ability of the framework to predict human drivers’ intention is exploited in a second application to improve environmental sustainability. We employ it to feed with the estimated speed profile a novel online Model Predictive Control (MPC) approach for Hybrid Electric Vehicles, introducing a state-of-the-art electrochemical model of the battery. Such control aims at preserving battery life and fuel consumption through equivalent costs. We validated the approach with actual driving data used to simulate vehicles and the power-train dynamics. At last, we address the traffic efficiency problem in the context of autonomous vehicles crossing an intersection. We propose an intersection management system for Connected Autonomous Vehicles based on a bi-level optimization framework. The motion planning of the vehicle is provided by a simplified optimal control problem, while we formulate the intersection management problem (in terms of order and timing) as a Mixed Integer Non-Linear Programming. The latter approximates a linear problem with a powerful piecewise linearization technique. Therefore, thanks to this technique, we can bound the error and employ commercial solvers to solve the problem (fast enough). Finally, this framework is validated in simulation and compared with the "Fist-Arrived First-Served" approach to show the impact of the proposed algorithm.

La conduite d’un véhicule industriel est une activité professionnelle complexe qui s’exerce dans un environnement dynamique en constante évolution. Elle nécessite un apprentissage spécifique et se situe dans un cadre réglementaire strict, qui relève aussi bien du code du travail que de la réglementation routière. A ces caractéristiques s’ajoutent de fortes contraintes spatio-temporelles qui imposent aux conducteurs le recours à des stratégies opératoires pour répondre à l’objectif principal de leur activité : le respect des délais de livraison dans des conditions optimales de sécurité, de sûreté et de productivité.Cette thèse traite de l'apport de la psychologie cognitive à la conception de systèmes d'assistance à la conduite de véhicules industriels. Les travaux sont destinés à intégrer, dès la conception des nouveaux systèmes, les contraintes du fonctionnement cognitif humain en situation réelle, ainsi que les besoins et attentes des conducteurs, afin que leur soient proposées des solutions technologiques adaptées et utilisables.La partie appliquée illustre deux dimensions majeures de l'activité de conduite d'un camion : la productivité, au travers de la problématique de l'assistance à l'éco-conduite (projet Conduite Economique Assistée, ADEME- RENAULT TRUCKS) ; la sécurité, au travers de la problématique de l'assistance à la détection et à la protection des usagers vulnérables de la route (projet VIVRE2, ANR-PREDIT05-LUTB).D’un point de vue scientifique, la thèse aboutit à la proposition d’un modèle du fonctionnement humain dans les activités finalisées, complété par un modèle adapté à l’activité de conduite d’un véhicule industriel. Les analyses effectuées en situations réelles enrichissent les connaissances, d’une part, sur les stratégies de conduite appliquées à la conduite rationnelle d’un poids lourd en environnement extra-urbain, et, d’autre part, sur les composantes de l’activité des conducteurs qui effectuent des livraisons en milieu urbain. De plus, les travaux effectués dans le cadre du projet VIVRE2 ont permis de préciser les représentations et les comportements à risque des usagers vulnérables vis-à-vis des camions en ville.D’un point de vue applicatif et ergonomique, les travaux sur simulateur dynamique de conduite ont permis l’évaluation d’une interface homme-machine innovante qui pourrait être adaptée à l’éco-conduite, ainsi que la proposition et l’évaluation de systèmes d’assistance pour garantir la sécurité des usagers vulnérables lors des manœuvres à basse vitesse en milieu urbain. / Driving a truck is a complex professional activity that takes place in a dynamic and constant changing environment. It needs a specific learning and it is set in a strict regulated framework including French labour code (Code du travail) as road regulation. Strong spatio-temporal pressure should be added to those characteristics. These constraints entail to drivers the use of operative strategies to achieve the main objective of their activity: respect of delivery time in optimal conditions of safety, security and productivity.This thesis deals with the contribution of cognitive psychology to the design of driving assistance systems for trucks. Works are intended to integrate, from the design of new systems, the demands of human cognitive functioning in real situation and the needs and expectations of drivers so that adapted and usable technological solutions could be proposed to them.Applied part shows two major dimensions of truck driving activity: productivity through the issue of the eco-driving assistance (“Conduite Economique Assistée, ADEME- RENAULT TRUCKS” project) and safety through the issue of the assistance to detection and protection of vulnerable road users (“VIVRE2, ANR-PREDIT05-LUTB” project).From a scientific point of view, the thesis ends with a proposal of a model of human functioning in finalized activities, of which is added an adapted model of the truck driving activity. The analysis performed in real environment enhance knowledge, on the one hand, on the applied driving strategies to the eco-driving of a truck in extra-urban environment and, on the other hand, on the components of the activity of drivers doing deliveries in urban environment. Moreover, works performed in VIVRE2 project allowed to specify representations and risky behaviours of vulnerable users with relation to trucks in town.From an applicative and ergonomic point of view, works on driving dynamic simulator allowed the evaluation of an innovative man-machine interface which could be adapted to eco-driving and the proposal as well as the evaluation of assistance systems to guarantee safety of vulnerable users during low speed manoeuvres in urban environment.

<div>With more and more videos taken from dash cams on thousands of cars, retrieving these videos and searching for important information is a daunting task. The purpose of this work is to mine some key road and vehicle motion attributes in a large-scale driving video data set for traffic analysis, sensing algorithm development and autonomous driving test benchmarks. Current sensing and control of autonomous cars based on full-view identification makes it difficult to maintain a high-frequency with a fast-moving vehicle, since computation is increasingly used to cope with driving environment changes.</div><div><br></div><div>A big challenge in video data mining is how to deal with huge amounts of data. We use a compact representation called the road profile system to visualize the road environment in long 2D images. It reduces the data from each frame of image to one line, thereby compressing the video clip to the image. This data dimensionality reduction method has several advantages: First, the data size is greatly compressed. The data is compressed from a video to an image, and each frame in the video is compressed into a line. The data size is compressed hundreds of times. While the size and dimensionality of the data has been compressed greatly, the useful information in the driving video is still completely preserved, and motion information is even better represented more intuitively. Because of the data and dimensionality reduction, the identification algorithm computational efficiency is higher than the full-view identification method, and it makes the real-time identification on road is possible. Second, the data is easier to be visualized, because the data is reduced in dimensionality, and the three-dimensional video data is compressed into two-dimensional data, the reduction is more conducive to the visualization and mutual comparison of the data. Third, continuously changing attributes are easier to show and be captured. Due to the more convenient visualization of two-dimensional data, the position, color and size of the same object within a few frames will be easier to compare and capture. At the same time, in many cases, the trouble caused by tracking and matching can be eliminated. Based on the road profile system, there are three tasks in autonomous driving are achieved using the road profile images.</div><div><br></div><div>The first application is road edge detection under different weather and appearance for road following in autonomous driving to capture the road profile image and linearity profile image in the road profile system. This work uses naturalistic driving video data mining to study the appearance of roads, which covers large-scale road data and changes. This work excavated a large number of naturalistic driving video sets to sample the light-sensitive area for color feature distribution. The effective road contour image is extracted from the long-time driving video, thereby greatly reducing the amount of video data. Then, the weather and lighting type can be identified. For each weather and lighting condition obvious features are I identified at the edge of the road to distinguish the road edge. </div><div><br></div><div>The second application is detecting vehicle interactions in driving videos via motion profile images to capture the motion profile image in the road profile system. This work uses visual actions recorded in driving videos taken by a dashboard camera to identify this interaction. The motion profile images of the video are filtered at key locations, thereby reducing the complexity of object detection, depth sensing, target tracking and motion estimation. The purpose of this reduction is for decision making of vehicle actions such as lane changing, vehicle following, and cut-in handling.</div><div><br></div><div>The third application is motion planning based on vehicle interactions and driving video. Taking note of the fact that a car travels in a straight line, we simply identify a few sample lines in the view to constantly scan the road, vehicles, and environment, generating a portion of the entire video data. Without using redundant data processing, we performed semantic segmentation to streaming road profile images. We plan the vehicle's path/motion using the smallest data set possible that contains all necessary information for driving.</div><div><br></div><div>The results are obtained efficiently, and the accuracy is acceptable. The results can be used for driving video mining, traffic analysis, driver behavior understanding, etc.</div>

Les récentes percées technologiques dans les domaines de l’actionnement, de la perception et de l’intelligence artificielle annoncent une nouvelle ère pour l’assistance à la conduite et les véhicules hautement automatisés. Toutefois, dans un contexte où l’automatisation demeure imparfaite, il est primordial de s’assurer que le système d’automatisation de la conduite puisse maintenir la conscience de la situation du conducteur afin que ce dernier puisse accomplir avec succès son rôle de supervision des actions du système. En même temps, le système doit pouvoir assurer la sécurité du véhicule et prévenir les actions du conducteur qui risqueraient de compromettre sa sécurité et celle des usagers de la route. Il est donc nécessaire d’intégrer dès la conception du système automatisé de conduite, la problématique des interactions avec le conducteur en réglant les problèmes de partage de tâche et de degré de liberté, d’autorité et de niveau d’automatisation du système. S’inscrivant dans le cadre du projet ANR-CoCoVeA (Coopération Conducteur-Véhicule Automatisé), cette thèse se penche de plus près sur la question de la coopération entre l’automate de conduite et le conducteur. Notre objectif est de fournir au conducteur un niveau d’assistance conforme à ses attentes, capable de prendre en compte ses intentions tout en assurant un niveau de sécurité important. Pour cela nous proposons un cadre général qui intègre l’ensemble des fonctionnalités nécessaires sous la forme d’une architecture permettant une coopération à plusieurs niveaux de la tâche de conduite. Les notions d’attribution des tâches et de gestion d’autorité avec leurs différentes nuances sont abordées et l’ensemble des fonctions du système identifiées dans l’architecture ont été étudiées et adaptées pour ce besoin de coopération. Ainsi, nous avons développé des algorithmes de décision de la manœuvre à effectuer, de planification de trajectoire et de contrôle qui intègrent des mécanismes leur permettant de s’adapter aux actions et aux intentions du conducteur lors d’un éventuel conflit. En complément de l’aspect technique, cette thèse étudie les notions de coopération sous l’angle des facteurs humains en intégrant des tests utilisateur réalisés sur le simulateur de conduite dynamique SHERPA-LAMIH. Ces tests ont permis à la fois de valider les développements réalisés et d’approfondir l’étude grâce à l’éclairage qu’ils ont apporté sur l’intérêt de chaque forme de coopération. / The recent technological breakthroughs in the actuation, perception and artificial intelligence domains herald a new dawn for driving assistance and highly automated driving. However, in a context where the automation remains imperfect and prone to error, it is crucial to ensure that the automated driving system maintains the driver’s situation awareness in order to be able to successfully and continuously supervise the system’s actions. At the same time, the system must be able to ensure the safety of the vehicle and prevent the driver’s actions that would compromise his safety and that of other road users. Therefore, it is essential that the issue of interaction and cooperation with the driver is addressed throughout the whole system design process. This entails the issues of task allocation, authority management and levels of automation. Conducted in the scope of the projet ANR-CoCoVeA (French acronym for: "Cooperation between Driver and Automated Vehicle"), this thesis takes a closer look at the question of cooperation between the driver and automated driving systems. Our main objective is to provide the driver with a suitable assistance level that accounts for his intentions while ensuring global safety. For this matter, we propose a general framework that incorporates the necessary features for a successful cooperation at the different levels of the driving task in the form of a system architecture. The questions of task allocation and authority management are addressed under their different nuances and the identified system functionalities are studied and adapted to match the cooperation requirements. Therefore, we have developed algorithms to perform maneuver decision making, trajectory planning, and control that include the necessary mechanisms to adapt to the driver’s actions and intentions in the case of potential conflicts. In addition to the technical aspects, this thesis studies the cooperation notions from the human factor perspective. User test studies conducted on the SHERPA-LAMIH dynamic simulator allowed for the validation of the different developments while shedding light on the benefits of different cooperation forms.