Les véhicules autonomes, qualifiés aussi de véhicules sans conducteur, deviennent dans certains contextes une réalité tangible et partageront très bientôt nos routes avec d’autres véhicules classiques. Pour qu’un véhicule autonome se déplace de manière sécurisée, il doit savoir où il se trouve et ce qui l’entoure dans l’environnement. Pour la première tâche, pour déterminer sa position dans l’environnement, il doit se localiser selon six degrés de liberté (position et angles de rotation). Alors que pour la deuxième tâche, une bonne connaissance de cet environnement « proche » est nécessaire, ce qui donne lieu à une solution sous forme de cartographie. Par conséquent, pour atteindre le niveau de sécurité souhaité des véhicules autonomes, une localisation précise est primordiale. Cette localisation précise permet au véhicule non seulement de se positionner avec précision, mais également de trouver sa trajectoire optimale et d’éviter efficacement les collisions avec des objets statiques et dynamiques sur son trajet. Actuellement, la solution la plus répandue est le système de positionnement (GPS). Ce système ne permet qu’une précision limitée (de l’ordre de plusieurs mètres) et bien que les systèmes RTK (RealTime Kinematic) et DGPS (Differential GPS) aient atteint une précision bien plus satisfaisante, ces systèmes restent sensibles au masquage des signaux, et aux réflexions multiples, en particulier dans les zones urbaines denses. Toutes ces déficiences rendent ces systèmes inadaptés pour traiter des tâches critiques telles que l’évitement des collisions. Une alternative qui a récemment attiré l’attention des experts (chercheurs et industriels), consiste à utiliser une carte à priori pour localiser la voiture de l’intérieur de celui-ci. En effet, les cartes facilitent le processus de navigation et ajoutent une couche supplémentaire de sécurité et de compréhension. Le véhicule utilise ses capteurs embarqués pour comparer ce qu’il perçoit à un moment donné avec ce qui est stocké dans sa mémoire. Les cartes à priori permettent donc au véhicule de mieux se localiser dans son environnement en lui permettant de focaliser ses capteurs et la puissance de calcul uniquement sur les objets en mouvement. De cette façon, le véhicule peut prédire ce qui devrait arriver et voir ensuite ce qui se passe réellement en temps réel, et donc peut prendre une décision sur ce qu’il faut faire.Cette thèse vise donc à développer des outils permettant une localisation précise d’un véhicule autonome dans un environnement connu à priori. Cette localisation est déterminée par appariement (Map-matching) entre une carte de l’environnement disponible a priori et les données collectées au fur et à mesure que le véhicule se déplace. Pour ce faire, deux phases distinctes sont déployées. La première permet la construction de la carte, avec une précision centimétrique en utilisant des techniques de construction de cartes statiques ou dynamiques. La seconde correspond à la capacité de localiser le véhicule dans cette carte 3D en l’absence d’infrastructures dédiées comprenant le système GPS, les mesures inertielles (IMU) ou des balises.Au cours de ce travail, différentes techniques sont développées pour permettre la réalisation des deux phases mentionnées ci-dessus. Ainsi, la phase de construction de cartes, qui consiste à recaler des nuages de points capturés pour construire une représentation unique et unifiée de l’environnement, correspond au problème de la localisation et de la cartographie simultanée (SLAM). Afin de faire face à ce problème, nous avons testé et comparé différentes méthodes de recalage. Cependant, l’obtention de cartes précises nécessite des nuages de points très denses, ce qui les rend inefficaces pour une utilisation en temps réel. Dans ce contexte, une nouvelle méthode de réduction des points est proposée. (...) / The concept of self-driving vehicles is becoming a happening reality and will soon share our roads with other vehicles –autonomous or not-. For a self-driving car to move around in its environment in a securely, it needs to sense to its immediate environment and most importantly localize itself to be able to plan a safe trajectory to follow. Therefore, to perform tasks suchas trajectory planning and navigation, a precise localization is of upmost importance. This would further allow the vehicle toconstantly plan and predict an optimal path in order to weave through cluttered spaces by avoiding collisions with other agentssharing the same space as the latter. For years, the Global Positioning System (GPS) has been a widespread complementary solution for navigation. The latter allows only a limited precision (range of several meters). Although the Differential GPSand the Real Time Kinematic (RTK) systems have reached considerable accuracy, these systems remain sensitive to signal masking and multiple reflections, offering poor reliability in dense urban areas. All these deficiencies make these systems simply unsuitable to handle hard real time constraints such as collision avoidance. A prevailing alternative that has attracted interest recently, is to use upload a prior map in the system so that the agent can have a reliable support to lean on. Indeed,maps facilitate the navigation process and add an extra layer of security and other dimensions of semantic understanding. The vehicle uses its onboard sensors to compare what it perceives at a given instant to what is stored in the backend memory ofthe system. In this way, the autonomous vehicle can actually anticipate and predict its actions accordingly.The purpose of this thesis is to develop tools allowing an accurate localization task in order to deal with some complex navigation tasks outlined above. Localization is mainly performed by matching a 3D prior map with incoming point cloudstructures as the vehicle moves. Three main objectives are set out leading with two distinct phases deployed (the map building and the localization). The first allows the construction of the map, with centimeter accuracy using static or dynamic laser surveying technique. Explicit details about the experimental setup and data acquisition campaigns thoroughly carried outduring the course of this work are given. The idea is to construct efficient maps liable to be updated in the long run so thatthe environment representation contained in the 3D models are compact and robust. Moreover, map-building invariant on any dedicated infrastructure is of the paramount importance of this work in order to rhyme with the concept of flexible mapping and localization. In order to build maps incrementally, we rely on a self-implementation of state of the art iterative closest point (ICP) algorithm, which is then upgraded with new variants and compared to other implemented versions available inthe literature. However, obtaining accurate maps requires very dense point clouds, which make them inefficient for real-time use. Inthis context, the second objective deals with points cloud reduction. The proposed approach is based on the use of both colorinformation and the geometry of the scene. It aims to find sets of 3D points with the same color in a very small region and replacing each set with one point. As a result, the volume of the map will be significantly reduced, while the proprieties of this map such as the shape and color of scanned objects remain preserved.The third objective resort to efficient, precise and reliable localization once the maps are built and treated. For this purpose, the online data should be accurate, fast with low computational effort whilst maintaining a coherent model of the explored space. To this end, the Velodyne HDL-32 comes into play. (...)

Many modern mobile communication devices are equipped with a GPS receiver and anavigation tool. These devices are useful when a user seeks to reach a specified destinationas soon as possible, but may not be so when he/she only needs to arrive at thedestination in time and wants to focus on some activities on the way. To deal with thislatter situation, a method and device called “Space Time Alarm Clock” is presented forhelping the user reach the destination by a specified deadline and inform the user aboutthe consequences of his/her decisions. It does so by continuously and efficiently computinghow much more time the user may stay at his/her current location without failing toreach the destination by the deadline. Furthermore, it determines the possible movementchoices that a user can make with regards to an underlying road network, it computesthe shortest travel time associated with each choice and informs the user about the consequencesof his/her decisions. Advantage of this approach is that it works completelyin the background so that the user‘s en-route activities will never be interfered with. The“Space Time Alarm Clock” was implemented for Stockholm, where it was tested.

A large proportion of movement in urban area is constrained to a road network such as pedestrian, bicycle and vehicle. That movement information is commonly collected by Global Positioning System (GPS) sensor, which has generated large collections of trajectories. A contiguous sequential pattern (CSP) in these trajectories represents a certain number of objects traversing a sequence of spatially contiguous edges in the network, which is an intuitive way to study regularities in network-constrained movement. CSPs are closely related to route choices and traffic flows and can be useful in travel demand modeling and transportation planning. However, the efficient and scalable extraction of CSPs and effective visualization of the heavily overlapping CSPs are remaining challenges. To address these challenges, the thesis develops two algorithms and a visual analytics system. Firstly, a fast map matching (FMM) algorithm is designed for matching a noisy trajectory to a sequence of edges traversed by the object with a high performance. Secondly, an algorithm called bidirectional pruning based closed contiguous sequential pattern mining (BP-CCSM) is developed to extract sequential patterns with closeness and contiguity constraint from the map matched trajectories. Finally, a visual analytics system called sequential pattern explorer for trajectories (SPET) is designed for interactive mining and visualization of CSPs in a large collection of trajectories. Extensive experiments are performed on a real-world taxi trip GPS dataset to evaluate the algorithms and visual analytics system. The results demonstrate that FMM achieves a superior performance by replacing repeated routing queries with hash table lookups. BP-CCSM considerably outperforms three state-of-the-art algorithms in terms of running time and memory consumption. SPET enables the user to efficiently and conveniently explore spatial and temporal variations of CSPs in network-constrained movement. / <p>QC 20171122</p>

The use of GPS probes in traffic management is growing rapidly as the required data collection infrastructure is increasingly in place, with significant number of mobile sensors moving around covering expansive areas of the road network. Many travelers carry with them at least one device with a built-in GPS receiver. Furthermore, vehicles are becoming more and more location aware. Vehicles in commercial fleets are now routinely equipped with GPS. Travel time is important information for various actors of a transport system, ranging from city planning, to day to day traffic management, to individual travelers. They all make decisions based on average travel time or variability of travel time among other factors. AVI (Automatic Vehicle Identification) systems have been commonly used for collecting point-to-point travel time data. Floating car data (FCD) -timestamped locations of moving vehicles- have shown potential for travel time estimation. Some advantages of FCD compared to stationary AVI systems are that they have no single point of failure and they have better network coverage. Furthermore, the availability of opportunistic sensors, such as GPS, makes the data collection infrastructure relatively convenient to deploy. Currently, systems that collect FCD are designed to transmit data in a limited form and relatively infrequently due to the cost of data transmission. Thus, reported locations are far apart in time and space, for example with 2 minutes gaps. For sparse FCD to be useful for transport applications, it is required that the corresponding probes be matched to the underlying digital road network. Matching such data to the network is challenging. This thesis makes the following contributions: (i) a map-matching and path inference algorithm, (ii) a method for route travel time estimation, (iii) a fixed point approach for joint path inference and travel time estimation, and (iv) a method for fusion of FCD with data from automatic number plate recognition. In all methods, scalability and overall computational efficiency are considered among design requirements. Throughout the thesis, the methods are used to process FCD from 1500 taxis in Stockholm City. Prior to this work, the data had been ignored because of its low frequency and minimal information. The proposed methods proved that the data can be processed and transformed into useful traffic information. Finally, the thesis implements the main components of an experimental ITS laboratory, called iMobility Lab. It is designed to explore GPS and other emerging data sources for traffic monitoring and control. Processes are developed to be computationally efficient, scalable, and to support real time applications with large data sets through a proposed distributed implementation. / <p>QC 20150525</p>

With the simple means of a digitized map and the wheel speed signals, it is possible to position a vehicle with an accuracy comparable to GPS. The positioning problem is a non-linear filtering problem and a particle filter has been applied to solve it. Two new approaches studied are the Auxiliary Particle Filter (APF), that aims at lowerering the variance of the error, and Rao-Blackwellization that exploits the linearities in the model. The results show that these methods require problems of higher complexity to fully utilize their advantages. Another aspect in this thesis has been to handle off-road driving scenarios, using dead reckoning. An off road detection mechanism has been developed and the results show that off-road driving can be detected accurately. The algorithm has been successfully implemented on a hand-held computer by quantizing the particle filter while keeping good filter performance.

<p>With the simple means of a digitized map and the wheel speed signals, it is possible to position a vehicle with an accuracy comparable to GPS. The positioning problem is a non-linear filtering problem and a particle filter has been applied to solve it. Two new approaches studied are the Auxiliary Particle Filter (APF), that aims at lowerering the variance of the error, and Rao-Blackwellization that exploits the linearities in the model. The results show that these methods require problems of higher complexity to fully utilize their advantages.</p><p>Another aspect in this thesis has been to handle off-road driving scenarios, using dead reckoning. An off road detection mechanism has been developed and the results show that off-road driving can be detected accurately. The algorithm has been successfully implemented on a hand-held computer by quantizing the particle filter while keeping good filter performance.</p>

This dissertation is concerned with the problem of estimating travel time per links in urban context using sparsely sampled GPS data. One of the challenges in this thesis is use the sparsely sampled data. A part of this research work, i developed a digital map with its new geographic information system (GIS), dealing with map-matching problem, where we come out with an enhancement tecnique, and also the shortest path problem.The thesis research work was conduct within the project PUMAS, which is an avantage for our research regarding the collection process of our data from the real world field and also in making our tests. The project PUMAS (Plate-forme Urbaine de Mobilité Avancée et Soutenable / Urban Platform for Sustainable and Advanced Mobility) is a preindustrial project that has the objective to inform about the traffic situation and also to develop an implement a platform for sustainable mobility in order to evaluate it in the region, specifically Rouen, France. The result is a framework for any traffic controller or manager and also estimation researcher to access vast stores of data about the traffic estimation, forecasting and status.

GPS technology has been embedded into portable, low-cost electronic devices nowadays to track the movements of mobile objects. This implication has greatly impacted the transportation field by creating a novel and rich source of traffic data on the road network. Although the promise offered by GPS devices to overcome problems like underreporting, respondent fatigue, inaccuracies and other human errors in data collection is significant; the technology is still relatively new that it raises many issues for potential users. These issues tend to revolve around the following areas: reliability, data processing and the related application. This thesis aims to study the GPS tracking form the methodological, technical and practical aspects. It first evaluates the reliability of GPS based traffic data based on data from an experiment containing three different traffic modes (car, bike and bus) traveling along the road network. It then outline the general procedure for processing GPS tracking data and discuss related issues that are uncovered by using real-world GPS tracking data of 316 cars. Thirdly, it investigates the influence of road network density in finding optimal location for enhancing travel efficiency and decreasing travel cost. The results show that the geographical positioning is reliable. Velocity is slightly underestimated, whereas altitude measurements are unreliable.Post processing techniques with auxiliary information is found necessary and important when solving the inaccuracy of GPS data. The densities of the road network influence the finding of optimal locations. The influence will stabilize at a certain level and do not deteriorate when the node density is higher.

The application of GPS probes in traffic management is growing rapidly as the required data collection infrastructure is increasingly in place in urban areas with significant number of mobile sensors moving around covering expansive areas of the road network. Most travelers carry with them at least one device with a built-in GPS receiver. Furthermore, vehicles are becoming more and more location aware. Currently, systems that collect floating car data are designed to transmit the data in a limited form and relatively infrequently due to the cost of data transmission. That means the reported locations of vehicles are far apart in time and space. In order to extract traffic information from the data, it first needs to be matched to the underlying digital road network. Matching such sparse data to the network, especially in dense urban, area is challenging. This thesis introduces a map-matching and path inference algorithm for sparse GPS probes in urban networks. The method is utilized in a case study in Stockholm and showed robustness and high accuracy compared to a number of other methods in the literature. The method is used to process floating car data from 1500 taxis in Stockholm City. The taxi data had been ignored because of its low frequency and minimal information. The proposed method showed that the data can be processed and transformed into information that is suitable for traffic studies. The thesis implemented the main components of an experimental ITS laboratory, called iMobility Lab. It is designed to explore GPS and other emerging traffic and traffic-related data for traffic monitoring and control. / <p>QC 20121107</p>

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