Search

Advanced driver assistance systems (ADAS) help drivers to perform complex driving tasks and to avoid or mitigate dangerous situations. The vehicle senses the external world using sensors and then builds and updates an internal model of the environment configuration. Vehicle perception consists of establishing the spatial and temporal relationships between the vehicle and the static and moving obstacles in the environment. Vehicle perception is composed of two main tasks: simultaneous localization and mapping (SLAM) deals with modelling static parts; and detection and tracking moving objects (DATMO) is responsible for modelling moving parts of the environment. The perception output is used to reason and decide which driving actions are the best for specific driving situations. In order to perform a good reasoning and control, the system has to correctly model the surrounding environment. The accurate detection and classification of moving objects is a critical aspect of a moving object tracking system. Therefore, many sensors are part of a common intelligent vehicle system.Multiple sensor fusion has been a topic of research since long; the reason is the need to combine information from different views of the environment to obtain a more accurate model. This is achieved by combining redundant and complementary measurements of the environment. Fusion can be performed at different levels inside the perception task.Classification of moving objects is needed to determine the possible behaviour of the objects surrounding the vehicle, and it is usually performed at tracking level. Knowledge about the class of moving objects at detection level can help to improve their tracking, reason about their behaviour, and decide what to do according to their nature. Most of the current perception solutions consider classification information only as aggregate information for the final perception output.Also, the management of incomplete information is an important issue in these perception systems. Incomplete information can be originated from sensor-related reasons, such as calibration issues and hardware malfunctions; or from scene perturbations, like occlusions, weather issues and object shifting. It is important to manage these situations by taking into account the degree of imprecision and uncertainty into the perception process.The main contributions in this dissertation focus on the DATMO stage of the perception problem. Precisely, we believe that including the object's class as a key element of the object's representation and managing the uncertainty from multiple sensors detections, we can improve the results of the perception task, i.e., a more reliable list of moving objects of interest represented by their dynamic state and appearance information. Therefore, we address the problems of sensor data association, and sensor fusion for object detection, classification, and tracking at different levels within the DATMO stage. We believe that a richer list of tracked objects can improve future stages of an ADAS and enhance its final results. Although we focus on a set of three main sensors: radar, lidar, and camera, we propose a modifiable architecture to include other type or number of sensors. First, we define a composite object representation to include class information as a part of the object state from early stages to the final output of the perception task. Second, we propose, implement, and compare two different perception architectures to solve the DATMO problem according to the level where object association, fusion, and classification information is included and performed. Our data fusion approaches are based on the evidential framework, which is used to manage and include the uncertainty from sensor detections and object classifications. Third, we propose an evidential data association approach to establish a relationship between two sources of evidence from object detections. We apply this approach at tracking level to fuse information from two track representations, and at detection level to find the relations between observations and to fuse their representations. We observe how the class information improves the final result of the DATMO component. Fourth, we integrate the proposed fusion approaches as a part of a real-time vehicle application. This integration has been performed in a real vehicle demonstrator from the interactIVe European project. Finally, we analysed and experimentally evaluated the performance of the proposed methods. We compared our evidential fusion approaches against each other and against a state-of-the-art method using real data from different driving scenarios and focusing on the detection, classification and tracking of different moving objects: pedestrian, bike, car and truck. We obtained promising results from our proposed approaches and empirically showed how our composite representation can improve the final result when included at different stages of the perception task.

Advanced driver assistance systems (ADAS) help drivers to perform complex driving tasks and to avoid or mitigate dangerous situations. The vehicle senses the external world using sensors and then builds and updates an internal model of the environment configuration. Vehicle perception consists of establishing the spatial and temporal relationships between the vehicle and the static and moving obstacles in the environment. Vehicle perception is composed of two main tasks: simultaneous localization and mapping (SLAM) deals with modelling static parts; and detection and tracking moving objects (DATMO) is responsible for modelling moving parts in the environment. In order to perform a good reasoning and control, the system has to correctly model the surrounding environment. The accurate detection and classification of moving objects is a critical aspect of a moving object tracking system. Therefore, many sensors are part of a common intelligent vehicle system. Classification of moving objects is needed to determine the possible behaviour of the objects surrounding the vehicle, and it is usually performed at tracking level. Knowledge about the class of moving objects at detection level can help improve their tracking. Most of the current perception solutions consider classification information only as aggregate information for the final perception output. Also, management of incomplete information is an important requirement for perception systems. Incomplete information can be originated from sensor-related reasons, such as calibration issues and hardware malfunctions; or from scene perturbations, like occlusions, weather issues and object shifting. It is important to manage these situations by taking them into account in the perception process. The main contributions in this dissertation focus on the DATMO stage of the perception problem. Precisely, we believe that including the object's class as a key element of the object's representation and managing the uncertainty from multiple sensors detections, we can improve the results of the perception task, i.e., a more reliable list of moving objects of interest represented by their dynamic state and appearance information. Therefore, we address the problems of sensor data association, and sensor fusion for object detection, classification, and tracking at different levels within the DATMO stage. Although we focus on a set of three main sensors: radar, lidar, and camera, we propose a modifiable architecture to include other type or number of sensors. First, we define a composite object representation to include class information as a part of the object state from early stages to the final output of the perception task. Second, we propose, implement, and compare two different perception architectures to solve the DATMO problem according to the level where object association, fusion, and classification information is included and performed. Our data fusion approaches are based on the evidential framework, which is used to manage and include the uncertainty from sensor detections and object classifications. Third, we propose an evidential data association approach to establish a relationship between two sources of evidence from object detections. We observe how the class information improves the final result of the DATMO component. Fourth, we integrate the proposed fusion approaches as a part of a real-time vehicle application. This integration has been performed in a real vehicle demonstrator from the interactIVe European project. Finally, we analysed and experimentally evaluated the performance of the proposed methods. We compared our evidential fusion approaches against each other and against a state-of-the-art method using real data from different driving scenarios. These comparisons focused on the detection, classification and tracking of different moving objects: pedestrian, bike, car and truck.; Les systèmes avancés d'assistance au conducteur (ADAS) aident les conducteurs à effectuer des tâches de conduite complexes et à éviter ou atténuer les situations dangereuses. Le véhicule détecte le monde extérieur au moyen de capteurs, et ensuite construit et met à jour un modèle interne de la configuration de l'environnement. La perception de véhicule consiste à établir des relations spatiales et temporelles entre le véhicule et les obstacles statiques et mobiles dans l'environnement. Cette perception se compose de deux tâches principales : la localisation et cartographie simultanées (SLAM) traite de la modélisation de pièces statiques; et la détection et le suivi d'objets en mouvement (DATMO) est responsable de la modélisation des pièces mobiles dans l'environnement. Afin de réaliser un bon raisonnement et contrôle, le système doit modéliser correctement l'environnement. La détection précise et la classification des objets en mouvement est un aspect essentiel d'un système de suivi d'objets. Classification des objets en mouvement est nécessaire pour déterminer le comportement possible des objets entourant le véhicule, et il est généralement réalisée au niveau de suivi des objets. La connaissance de la classe d'objets en mouvement au niveau de la détection peut aider à améliorer leur suivi. La plupart des solutions de perception actuels considèrent informations de classification seulement comme information additional pour la sortie final de la perception. Aussi, la gestion de l'information incomplète est une exigence importante pour les systèmes de perception. Une information incomplète peut être originaire de raisons liées à la détection, tels que les problèmes d calibrage et les dysfonctionnements des capteurs; ou des perturbations de la scène, comme des occlusions, des problèmes de météo et objet déplacement. Les principales contributions de cette thèse se concentrent sur ​​la scène DATMO. Précisément, nous pensons que l'inclusion de la classe de l'objet comme un élément clé de la représentation de l'objet et la gestion de l'incertitude de plusieurs capteurs de détections, peut améliorer les résultats de la tâche de perception. Par conséquent, nous abordons les problèmes de l'association de données, la fusion de capteurs, la classification et le suivi à différents niveaux au sein de la phase de DATMO. Même si nous nous concentrons sur un ensemble de trois capteurs principaux: radar, lidar, et la caméra, nous proposons une architecture modifiables pour inclure un autre type ou nombre de capteurs. Premièrement, nous définissons une représentation composite de l'objet pour inclure des informations de classe et de l'état d'objet deouis le début de la tâche de perception. Deuxièmement, nous proposons, mettre en œuvre, et comparons deux architectures de perception afin de résoudre le problème de DATMO selon le niveau où l'association des objets, la fusion et la classification des informations sont inclus et appliquées. Nos méthodes de fusion de données sont basées sur la théorie de l'evidence, qui est utilisé pour gérer et inclure l'incertitude de la détection du capteur et de la classification des objets. Troisièmement, nous proposons une approche d'association de données bassée en la théorie de l'evidence pour établir une relation entre deux liste des détections d'objets. Quatrièmement, nous intégrons nos approches de fusion dans le cadre d'une application véhicule en temps réel. Cette intégration a été réalisée dans un réelle démonstrateur de véhicule du projet European InteractIVe. Finalement, nous avons analysé et évalué expérimentalement les performances des méthodes proposées. Nous avons comparé notre fusion rapproche les uns contre les autres et contre une méthode state-of-the-art en utilisant des données réelles de scénarios de conduite différents. Ces comparaisons sont concentrés sur la détection, la classification et le suivi des différents objets en mouvement: piétons, vélos, voitures et camions.

Advanced driver assistance systems (ADAS) help drivers to perform complex driving tasks and to avoid or mitigate dangerous situations. The vehicle senses the external world using sensors and then builds and updates an internal model of the environment configuration. Vehicle perception consists of establishing the spatial and temporal relationships between the vehicle and the static and moving obstacles in the environment. Vehicle perception is composed of two main tasks: simultaneous localization and mapping (SLAM) deals with modelling static parts; and detection and tracking moving objects (DATMO) is responsible for modelling moving parts of the environment. The perception output is used to reason and decide which driving actions are the best for specific driving situations. In order to perform a good reasoning and control, the system has to correctly model the surrounding environment. The accurate detection and classification of moving objects is a critical aspect of a moving object tracking system. Therefore, many sensors are part of a common intelligent vehicle system.Multiple sensor fusion has been a topic of research since long; the reason is the need to combine information from different views of the environment to obtain a more accurate model. This is achieved by combining redundant and complementary measurements of the environment. Fusion can be performed at different levels inside the perception task.Classification of moving objects is needed to determine the possible behaviour of the objects surrounding the vehicle, and it is usually performed at tracking level. Knowledge about the class of moving objects at detection level can help to improve their tracking, reason about their behaviour, and decide what to do according to their nature. Most of the current perception solutions consider classification information only as aggregate information for the final perception output.Also, the management of incomplete information is an important issue in these perception systems. Incomplete information can be originated from sensor-related reasons, such as calibration issues and hardware malfunctions; or from scene perturbations, like occlusions, weather issues and object shifting. It is important to manage these situations by taking into account the degree of imprecision and uncertainty into the perception process.The main contributions in this dissertation focus on the DATMO stage of the perception problem. Precisely, we believe that including the object's class as a key element of the object's representation and managing the uncertainty from multiple sensors detections, we can improve the results of the perception task, i.e., a more reliable list of moving objects of interest represented by their dynamic state and appearance information. Therefore, we address the problems of sensor data association, and sensor fusion for object detection, classification, and tracking at different levels within the DATMO stage. We believe that a richer list of tracked objects can improve future stages of an ADAS and enhance its final results. Although we focus on a set of three main sensors: radar, lidar, and camera, we propose a modifiable architecture to include other type or number of sensors. First, we define a composite object representation to include class information as a part of the object state from early stages to the final output of the perception task. Second, we propose, implement, and compare two different perception architectures to solve the DATMO problem according to the level where object association, fusion, and classification information is included and performed. Our data fusion approaches are based on the evidential framework, which is used to manage and include the uncertainty from sensor detections and object classifications. Third, we propose an evidential data association approach to establish a relationship between two sources of evidence from object detections. We apply this approach at tracking level to fuse information from two track representations, and at detection level to find the relations between observations and to fuse their representations. We observe how the class information improves the final result of the DATMO component. Fourth, we integrate the proposed fusion approaches as a part of a real-time vehicle application. This integration has been performed in a real vehicle demonstrator from the interactIVe European project. Finally, we analysed and experimentally evaluated the performance of the proposed methods. We compared our evidential fusion approaches against each other and against a state-of-the-art method using real data from different driving scenarios and focusing on the detection, classification and tracking of different moving objects: pedestrian, bike, car and truck. We obtained promising results from our proposed approaches and empirically showed how our composite representation can improve the final result when included at different stages of the perception task.

Advanced driver assistance systems (ADAS) help drivers to perform complex driving tasks and to avoid or mitigate dangerous situations. The vehicle senses the external world using sensors and then builds and updates an internal model of the environment configuration. Vehicle perception consists of establishing the spatial and temporal relationships between the vehicle and the static and moving obstacles in the environment. Vehicle perception is composed of two main tasks: simultaneous localization and mapping (SLAM) deals with modelling static parts; and detection and tracking moving objects (DATMO) is responsible for modelling moving parts in the environment. In order to perform a good reasoning and control, the system has to correctly model the surrounding environment. The accurate detection and classification of moving objects is a critical aspect of a moving object tracking system. Therefore, many sensors are part of a common intelligent vehicle system. Classification of moving objects is needed to determine the possible behaviour of the objects surrounding the vehicle, and it is usually performed at tracking level. Knowledge about the class of moving objects at detection level can help improve their tracking. Most of the current perception solutions consider classification information only as aggregate information for the final perception output. Also, management of incomplete information is an important requirement for perception systems. Incomplete information can be originated from sensor-related reasons, such as calibration issues and hardware malfunctions; or from scene perturbations, like occlusions, weather issues and object shifting. It is important to manage these situations by taking them into account in the perception process. The main contributions in this dissertation focus on the DATMO stage of the perception problem. Precisely, we believe that including the object's class as a key element of the object's representation and managing the uncertainty from multiple sensors detections, we can improve the results of the perception task, i.e., a more reliable list of moving objects of interest represented by their dynamic state and appearance information. Therefore, we address the problems of sensor data association, and sensor fusion for object detection, classification, and tracking at different levels within the DATMO stage. Although we focus on a set of three main sensors: radar, lidar, and camera, we propose a modifiable architecture to include other type or number of sensors. First, we define a composite object representation to include class information as a part of the object state from early stages to the final output of the perception task. Second, we propose, implement, and compare two different perception architectures to solve the DATMO problem according to the level where object association, fusion, and classification information is included and performed. Our data fusion approaches are based on the evidential framework, which is used to manage and include the uncertainty from sensor detections and object classifications. Third, we propose an evidential data association approach to establish a relationship between two sources of evidence from object detections. We observe how the class information improves the final result of the DATMO component. Fourth, we integrate the proposed fusion approaches as a part of a real-time vehicle application. This integration has been performed in a real vehicle demonstrator from the interactIVe European project. Finally, we analysed and experimentally evaluated the performance of the proposed methods. We compared our evidential fusion approaches against each other and against a state-of-the-art method using real data from different driving scenarios. These comparisons focused on the detection, classification and tracking of different moving objects: pedestrian, bike, car and truck.; Les systèmes avancés d'assistance au conducteur (ADAS) aident les conducteurs à effectuer des tâches de conduite complexes et à éviter ou atténuer les situations dangereuses. Le véhicule détecte le monde extérieur au moyen de capteurs, et ensuite construit et met à jour un modèle interne de la configuration de l'environnement. La perception de véhicule consiste à établir des relations spatiales et temporelles entre le véhicule et les obstacles statiques et mobiles dans l'environnement. Cette perception se compose de deux tâches principales : la localisation et cartographie simultanées (SLAM) traite de la modélisation de pièces statiques; et la détection et le suivi d'objets en mouvement (DATMO) est responsable de la modélisation des pièces mobiles dans l'environnement. Afin de réaliser un bon raisonnement et contrôle, le système doit modéliser correctement l'environnement. La détection précise et la classification des objets en mouvement est un aspect essentiel d'un système de suivi d'objets. Classification des objets en mouvement est nécessaire pour déterminer le comportement possible des objets entourant le véhicule, et il est généralement réalisée au niveau de suivi des objets. La connaissance de la classe d'objets en mouvement au niveau de la détection peut aider à améliorer leur suivi. La plupart des solutions de perception actuels considèrent informations de classification seulement comme information additional pour la sortie final de la perception. Aussi, la gestion de l'information incomplète est une exigence importante pour les systèmes de perception. Une information incomplète peut être originaire de raisons liées à la détection, tels que les problèmes d calibrage et les dysfonctionnements des capteurs; ou des perturbations de la scène, comme des occlusions, des problèmes de météo et objet déplacement. Les principales contributions de cette thèse se concentrent sur ​​la scène DATMO. Précisément, nous pensons que l'inclusion de la classe de l'objet comme un élément clé de la représentation de l'objet et la gestion de l'incertitude de plusieurs capteurs de détections, peut améliorer les résultats de la tâche de perception. Par conséquent, nous abordons les problèmes de l'association de données, la fusion de capteurs, la classification et le suivi à différents niveaux au sein de la phase de DATMO. Même si nous nous concentrons sur un ensemble de trois capteurs principaux: radar, lidar, et la caméra, nous proposons une architecture modifiables pour inclure un autre type ou nombre de capteurs. Premièrement, nous définissons une représentation composite de l'objet pour inclure des informations de classe et de l'état d'objet deouis le début de la tâche de perception. Deuxièmement, nous proposons, mettre en œuvre, et comparons deux architectures de perception afin de résoudre le problème de DATMO selon le niveau où l'association des objets, la fusion et la classification des informations sont inclus et appliquées. Nos méthodes de fusion de données sont basées sur la théorie de l'evidence, qui est utilisé pour gérer et inclure l'incertitude de la détection du capteur et de la classification des objets. Troisièmement, nous proposons une approche d'association de données bassée en la théorie de l'evidence pour établir une relation entre deux liste des détections d'objets. Quatrièmement, nous intégrons nos approches de fusion dans le cadre d'une application véhicule en temps réel. Cette intégration a été réalisée dans un réelle démonstrateur de véhicule du projet European InteractIVe. Finalement, nous avons analysé et évalué expérimentalement les performances des méthodes proposées. Nous avons comparé notre fusion rapproche les uns contre les autres et contre une méthode state-of-the-art en utilisant des données réelles de scénarios de conduite différents. Ces comparaisons sont concentrés sur la détection, la classification et le suivi des différents objets en mouvement: piétons, vélos, voitures et camions.

With an anticipation to make driving experience safer and more convenient, over the decades, researchers have tried to develop intelligent systems for modern vehicles. The intended systems can either drive automatically or monitor a human driver and assist him in navigation by warning in case of a developing dangerous situation. Contrary to the human drivers, these systems are not constrained by many physical and psychological limitations and therefore prove more robust in extreme conditions. A key component of an intelligent vehicle system is the reliable perception of the environment. Laser range finders have been popular sensors which are widely used in this context. The classical 2D laser scanners have some limitations which are often compensated by the addition of other complementary sensors including cameras and radars. The recent advent of new sensors, such as 3D laser scanners which perceive the environment at a high spatial resolution, has proven to be an interesting addition to the arena. Although there are well-known methods for perception using 2D laser scanners, approaches using a 3D range scanner are relatively rare in literature. Most of those which exist either address the problem partially or augment the system with many other sensors. Surprisingly, many of those rely on reducing the dimensionality of the problem by projecting 3D data to 2D and using the well-established methods for 2D perception. In contrast to these approaches, this work addresses the problem of vehicle perception using a single 3D laser scanner. First contribution of this research is made by the extension of a generic 3D mapping framework based on an optimized occupancy grid representation to solve the problem of simultaneous localization and mapping (SLAM). Using the 3D occupancy grid, we introduce a variance-based elevation map for the segmentation of range measurements corresponding to the ground. To correct the vehicle location from odometry, we use a grid-based incremental scan matching method. The resulting SLAM framework forms a basis for rest of the contributions which constitute the major achievement of this work. After obtaining a good vehicle localization and a reliable map with ground segmentation, we focus on the detection and tracking of moving objects (DATMO). The second contribution of this thesis is the method for discriminating between the dynamic objects and the static environment. The presented approach uses motion-based detection and density-based clustering for segmenting the moving objects from 3D occupancy grid. It does not use object specific models but enables detecting arbitrary traffic participants. Third contribution is an innovative method for layered classification of the detected objects based on supervised learning technique which makes it easier to estimate their position with time. Final contribution is a method for tracking the detected objects by using Viterbi algorithm to associate the new observations with the existing objects in the environment. The proposed framework is verified with the datasets acquired from a laser scanner mounted on top of a vehicle moving in different environments including urban, highway and pedestrian-zone scenarios. The promising results thus obtained show the applicability of the proposed system for simultaneous localization and mapping with detection, classification and tracking of moving objects in dynamic outdoor environments using a single 3D laser scanner.; Depuis des décennies, les chercheurs essaient de développer des systèmes intelligents pour les véhicules modernes, afin de rendre la conduite plus sûre et plus confortable. Ces systèmes peuvent conduire automatiquement le véhicule ou assister un conducteur en le prévenant et en l'assistant en cas de situations dangereuses. Contrairement aux conducteurs, ces systèmes n'ont pas de contraintes physiques ou psychologiques et font preuve d'une grande robustesse dans des conditions extrêmes. Un composant clé de ces systèmes est la fiabilité de la perception de l'environnement. Pour cela, les capteurs lasers sont très populaires et largement utilisés. Les capteurs laser 2D classiques ont des limites qui sont souvent compensées par l'ajout d'autres capteurs complémentaires comme des caméras ou des radars. Les avancées récentes dans le domaine des capteurs, telles que les capteurs laser 3D qui perçoivent l'environnement avec une grande résolution spatiale, ont montré qu'ils étaient une solution intéressante afin d'éviter l'utilisation de plusieurs capteurs. Bien qu'il y ait des méthodes bien connues pour la perception avec des capteurs laser 2D, les approches qui utilisent des capteurs lasers 3D sont relativement rares dans la littérature. De plus, la plupart d'entre elles utilisent plusieurs capteurs et réduisent le problème de la 3ème dimension en projetant les données 3D sur un plan et utilisent les méthodes classiques de perception 2D. Au contraire de ces approches, ce travail résout le problème en utilisant uniquement un capteur laser 3D et en utilisant les informations spatiales fournies par ce capteur. Notre première contribution est une extension des méthodes génériques de cartographie 3D fondée sur des grilles d'occupations optimisées pour résoudre le problème de cartographie et de localisation simultanée (SLAM en anglais). En utilisant des grilles d'occupations 3D, nous définissons une carte d'élévation pour la segmentation des données laser correspondant au sol. Pour corriger les erreurs de positionnement, nous utilisons une méthode incrémentale d'alignement des données laser. Le résultat forme la base pour le reste de notre travail qui constitue nos contributions les plus significatives. Dans la deuxième partie, nous nous focalisons sur la détection et le suivi des objets mobiles (DATMO en anglais). La deuxième contribution de ce travail est une méthode pour distinguer les objets dynamiques des objets statiques. L'approche proposée utilise une détection fondée sur le mouvement et sur des techniques de regroupement pour identifier les objets mobiles à partir de la grille d'occupations 3D. La méthode n'utilise pas de modèles spécifiques d'objets et permet donc la détection de tout type d'objets mobiles. Enfin, la troisième contribution est une méthode nouvelle pour classer les objets mobiles fondée sur une technique d'apprentissage supervisée. La contribution finale est une méthode pour suivre les objets mobiles en utilisant l'algorithme de Viterbi pour associer les nouvelles observations avec les objets présents dans l'environnement, Dans la troisième partie, l'approche propose est testée sur des jeux de données acquis à partir d'un capteur laser 3D monté sur le toit d'un véhicule qui se déplace dans différents types d'environnement incluant des environnements urbains, des autoroutes et des zones piétonnes. Les résultats obtenus montrent l'intérêt du système intelligent proposé pour la cartographie et la localisation simultanée ainsi que la détection et le suivi d'objets mobiles en environnement extérieur et dynamique en utilisant un capteur laser 3D.

With an anticipation to make driving experience safer and more convenient, over the decades, researchers have tried to develop intelligent systems for modern vehicles. The intended systems can either drive automatically or monitor a human driver and assist him in navigation by warning in case of a developing dangerous situation. Contrary to the human drivers, these systems are not constrained by many physical and psychological limitations and therefore prove more robust in extreme conditions. A key component of an intelligent vehicle system is the reliable perception of the environment. Laser range finders have been popular sensors which are widely used in this context. The classical 2D laser scanners have some limitations which are often compensated by the addition of other complementary sensors including cameras and radars. The recent advent of new sensors, such as 3D laser scanners which perceive the environment at a high spatial resolution, has proven to be an interesting addition to the arena. Although there are well-known methods for perception using 2D laser scanners, approaches using a 3D range scanner are relatively rare in literature. Most of those which exist either address the problem partially or augment the system with many other sensors. Surprisingly, many of those rely on reducing the dimensionality of the problem by projecting 3D data to 2D and using the well-established methods for 2D perception. In contrast to these approaches, this work addresses the problem of vehicle perception using a single 3D laser scanner. First contribution of this research is made by the extension of a generic 3D mapping framework based on an optimized occupancy grid representation to solve the problem of simultaneous localization and mapping (SLAM). Using the 3D occupancy grid, we introduce a variance-based elevation map for the segmentation of range measurements corresponding to the ground. To correct the vehicle location from odometry, we use a grid-based incremental scan matching method. The resulting SLAM framework forms a basis for rest of the contributions which constitute the major achievement of this work. After obtaining a good vehicle localization and a reliable map with ground segmentation, we focus on the detection and tracking of moving objects (DATMO). The second contribution of this thesis is the method for discriminating between the dynamic objects and the static environment. The presented approach uses motion-based detection and density-based clustering for segmenting the moving objects from 3D occupancy grid. It does not use object specific models but enables detecting arbitrary traffic participants. Third contribution is an innovative method for layered classification of the detected objects based on supervised learning technique which makes it easier to estimate their position with time. Final contribution is a method for tracking the detected objects by using Viterbi algorithm to associate the new observations with the existing objects in the environment. The proposed framework is verified with the datasets acquired from a laser scanner mounted on top of a vehicle moving in different environments including urban, highway and pedestrian-zone scenarios. The promising results thus obtained show the applicability of the proposed system for simultaneous localization and mapping with detection, classification and tracking of moving objects in dynamic outdoor environments using a single 3D laser scanner.; Depuis des décennies, les chercheurs essaient de développer des systèmes intelligents pour les véhicules modernes, afin de rendre la conduite plus sûre et plus confortable. Ces systèmes peuvent conduire automatiquement le véhicule ou assister un conducteur en le prévenant et en l'assistant en cas de situations dangereuses. Contrairement aux conducteurs, ces systèmes n'ont pas de contraintes physiques ou psychologiques et font preuve d'une grande robustesse dans des conditions extrêmes. Un composant clé de ces systèmes est la fiabilité de la perception de l'environnement. Pour cela, les capteurs lasers sont très populaires et largement utilisés. Les capteurs laser 2D classiques ont des limites qui sont souvent compensées par l'ajout d'autres capteurs complémentaires comme des caméras ou des radars. Les avancées récentes dans le domaine des capteurs, telles que les capteurs laser 3D qui perçoivent l'environnement avec une grande résolution spatiale, ont montré qu'ils étaient une solution intéressante afin d'éviter l'utilisation de plusieurs capteurs. Bien qu'il y ait des méthodes bien connues pour la perception avec des capteurs laser 2D, les approches qui utilisent des capteurs lasers 3D sont relativement rares dans la littérature. De plus, la plupart d'entre elles utilisent plusieurs capteurs et réduisent le problème de la 3ème dimension en projetant les données 3D sur un plan et utilisent les méthodes classiques de perception 2D. Au contraire de ces approches, ce travail résout le problème en utilisant uniquement un capteur laser 3D et en utilisant les informations spatiales fournies par ce capteur. Notre première contribution est une extension des méthodes génériques de cartographie 3D fondée sur des grilles d'occupations optimisées pour résoudre le problème de cartographie et de localisation simultanée (SLAM en anglais). En utilisant des grilles d'occupations 3D, nous définissons une carte d'élévation pour la segmentation des données laser correspondant au sol. Pour corriger les erreurs de positionnement, nous utilisons une méthode incrémentale d'alignement des données laser. Le résultat forme la base pour le reste de notre travail qui constitue nos contributions les plus significatives. Dans la deuxième partie, nous nous focalisons sur la détection et le suivi des objets mobiles (DATMO en anglais). La deuxième contribution de ce travail est une méthode pour distinguer les objets dynamiques des objets statiques. L'approche proposée utilise une détection fondée sur le mouvement et sur des techniques de regroupement pour identifier les objets mobiles à partir de la grille d'occupations 3D. La méthode n'utilise pas de modèles spécifiques d'objets et permet donc la détection de tout type d'objets mobiles. Enfin, la troisième contribution est une méthode nouvelle pour classer les objets mobiles fondée sur une technique d'apprentissage supervisée. La contribution finale est une méthode pour suivre les objets mobiles en utilisant l'algorithme de Viterbi pour associer les nouvelles observations avec les objets présents dans l'environnement, Dans la troisième partie, l'approche propose est testée sur des jeux de données acquis à partir d'un capteur laser 3D monté sur le toit d'un véhicule qui se déplace dans différents types d'environnement incluant des environnements urbains, des autoroutes et des zones piétonnes. Les résultats obtenus montrent l'intérêt du système intelligent proposé pour la cartographie et la localisation simultanée ainsi que la détection et le suivi d'objets mobiles en environnement extérieur et dynamique en utilisant un capteur laser 3D.

Perceiving or understanding the environment surrounding of a vehicle is a very important step in building driving assistant systems or autonomous vehicles. In this thesis, we focus on using laser scanner as a main perception sensor in context of dynamic outdoor environments. To solve this problem, we have to deal with 3 main tasks: (1) identify static part and dynamic entities moving in the environment, (2) use static part of the environment to build a map of the environment and localize the vehicle inside this map: this task is know as "Simultaneous Localization And Mapping" (SLAM) and finally (3) Detect And Track Moving Objects (DATMO). Regarding SLAM, the first contribution of this research is made by a grid-based approach\footnote{An occupancy grid is a decomposition of the environment in rectangular cells where each cell contains the probability that it is occupied by an obstacle.} to solve both problems of SLAM and detection of moving objects. To correct vehicle location from odometry we introduce a new fast incremental scan matching method that works reliably in dynamic outdoor environments. After good vehicle location is estimated, the surrounding map is updated incrementally and moving objects are detected without a priori knowledge of the targets. Our second contribution is an efficient, precise and multiscale representation of 2D/3D environment. This representation is actually an extension of occupancy grid where (1) only cells corresponding to occupied part of the environment are stored and updated (2) where cells are represented by a cluster of gaussian to have a fine representation of the environment and (3) where several occupancy grids are used to store and update a multiscale representation of the environment. Regarding DATMO, we firstly present a method of simultaneous detection, classification and tracking moving objects. A model-based approach is introduced to interpret the laser measurement sequence over a sliding window of time by hypotheses of moving object trajectories. The data-driven Markov chain Monte Carlo (DDMCMC) technique is used to explore the hypothesis space and effectively find the most likely solution. An other important problem to solve regarding DATMO is the definition of an appropriate dynamic model. In practice, objects can change their dynamic behaviors over time (e.g. : stopped, moving, accelerating, etc...). To adapt to these changing behaviors, a multiple dynamic model is generally required. But, this set of dynamic models and interactions between these models are always given a priori. Our second contribution on DATMO is a method to guide in the choice of motion models and in the estimation of interactions between these motion models. The last part of this thesis reports integration of these contributions on different experimental platforms in the framework of some national and european projects. Evaluations are presented which confirm the robustness and reliability of our contributions.