<p dir="ltr">This thesis aims to assess the efficacy of adaptive Gaussian mixture filtering for an inertial navigation-based cislunar application. The thesis focuses on a fully autonomous system, where the navigation system is solely reliant on onboard sensors and receives no navigation information from external tracking systems. The proposed adaptive filter is tested under non-ideal conditions. Specifically, this thesis considers the challenging case where range information is unavailable, and instead, only bearings angles with respect to illuminated celestial bodies are measured. The performance of the adaptive filter is compared to the unscented Kalman filter (UKF), and the filter consistency and errors are compared. The proposed filter addresses challenges in linearization errors that accrue in the UKF measurement update equations. The adaptive filter is shown to be a consistent estimator, significantly outperforming the UKF. Considering design requirements for similar navigation missions, recommendations and practical considerations are suggested for future cislunar autonomous navigation applications</p>

<p dir="ltr">In the realm of autonomous vehicle technology, the Multimodal Vehicle Detection Network (MVDNet) represents a significant leap forward, particularly in the challenging context of weather conditions. This paper focuses on the enhancement of MVDNet through the integration of a multi-head attention layer, aimed at refining its performance. The integrated multi-head attention layer in the MVDNet model is a pivotal modification, advancing the network's ability to process and fuse multimodal sensor information more efficiently. The paper validates the improved performance of MVDNet with multi-head attention through comprehensive testing, which includes a training dataset derived from the Oxford Radar Robotcar. The results clearly demonstrate that the Multi-Head MVDNet outperforms the other related conventional models, particularly in the Average Precision (AP) estimation, under challenging environmental conditions. The proposed Multi-Head MVDNet not only contributes significantly to the field of autonomous vehicle detection but also underscores the potential of sophisticated sensor fusion techniques in overcoming environmental limitations.</p>

<p dir="ltr">In this thesis, a MATLAB model was used to simulate a 2-vehicle platoon where the lead truck is a conventional class 8 vehicle while the key parameters of the following truck was tested in various road conditions to minimize Inter vehicular Distance (IVD) and maximize fuel savings while ensuring safety</p>

Le travail présenté dans la thèse s’inscrit dans le projet de recherche partenarial ANR-ABV 2009 dont l’objet est la conception d’un système de conduite automatisée à basse vitesse. Il décrit et analyse les principes d’un contrôle partagé d’un véhicule automobile entre un conducteur humain et un copilote électronique (E-copilote). L’objectif est de mettre en place une coopération Homme-Machine efficace entre le conducteur et l’E-copilote. Un des enjeux est notamment de permettre au conducteur d’interagir avec l’E-copilote de façon continue pour pouvoir exécuter les manœuvres qu’il souhaite sans nécessiter la désactivation ni être gêné par l’E-copilote. Cet enjeu répond au besoin de prise en compte des actions du conducteur entreprises pour pallier celles du E-copilote dans certaines situations par exemple éviter un obstacle non perçu par le système. L’objectif dans ce cas est de garantir le confort au conducteur ainsi que sa conscience du mode engagé (système actif ou pas). Le conducteur et l’E-copilote agissant simultanément sur le système de direction, chacun doit être conscient des actions de l’autre : une communication bidirectionnelle est essentielle. Pour atteindre cet objectif, nous avons retenu les interactions haptiques à travers le système de direction du véhicule. Le couple appliqué par le conducteur sur volant est utilisé par l’E-copilote pour prendre en compte ces actions de la même façon que le couple produit par l’E-copilote est ressenti par le conducteur et utilisé pour comprendre le comportement du système. D’autres aspects essentiels pour la coopération H-M ont également été abordés : l’´étude des changements de modes de fonctionnement du système ainsi que l’IHM via laquelle le conducteur interagit avec le système. / The work presented in the thesis is part of the research partnership project ANRABV 2009 which aims is to design an automated low-speed driving. It describes and analyzes the principles of shared control of a motor vehicle between a human driver and an electronic copilot (E-copilot). The objective is to establish effective human-machine cooperation between the driver and E-copilot. One issue is particular to allow the driver to interact with the E-copilot continuously in order to perform maneuvers he wants without requiring deactivation neither constrained by E-copilot. This issue addresses the need for consideration of driver actions taken to remedy those of E-copilot for example avoiding undetected obstacle by the system while ensuring operator comfort and the driver situation awareness. The driver and E-co-pilot acting simultaneously on the steering system, everyone must be aware of the actions of the other: twoway communication is essential. To achieve this goal, we used the haptic interactions through the steering system of the vehicle. The torque applied by the driver on the steering wheel is used by the E-copilot to take into account these actions as the torque produced by the E-copilot is felt by the driver and used to understand the system’s behavior. Other key issues for the Human-Machine Cooperation were also discussed: the study of changes in modes of operation of the system and HMI via which the driver interact with the system.

<p>This thesis presents solutions to some of the problems facing Autonomous Surface Vehicle (ASV) deployments in inland waterways through the development of navigational and control systems. Fluvial systems are one of the hardest inland waterways to navigate and are thus used as a use-case for system development. The systems are built to reduce the reliance on a-prioris during ASV operation. This is crucial for exceptionally dynamic environments such as fluvial bodies of water that have poorly defined routes and edges, can change course in short time spans, carry away and deposit obstacles, and expose or cover shoals and man-made structures as their water level changes. While navigation of fluvial systems is exceptionally difficult potential autonomous data collection can aid in important scientific missions in under studied environments.</p> <p><br></p> <p>The work has four contributions targeting solutions to four fundamental problems present in fluvial system navigation and control. To sense the course of fluvial systems for navigable path determination a fluvial segmentation study is done and a novel dataset detailed. To enable rapid path computations and augmentations in a fast moving environment a Dubins path generator and augmentation algorithm is presented ans is used in conjunction with an Integral Line-Of-Sight (ILOS) path following method. To rapidly avoid unseen/undetected obstacles present in fluvial environments a Deep Reinforcement Learning (DRL) agent is built and tested across domains to create dynamic local paths that can be rapidly affixed to for collision avoidance. Finally, a custom low-cost and deployable ASV, BREAM (Boat for Robotic Engineering and Applied Machine-Learning), capable of operating in fluvial environments is presented along with an autonomy package used in providing base level sensing and autonomy processing capability to varying platforms.</p> <p><br></p> <p>Each of these contributions form a part of a larger documented Fluvial Navigation Control Architecture (FNCA) that is proposed as a way to aid in a-priori free navigation of fluvial waterways. The architecture relates the navigational structures into high, mid, and low-level controller Guidance and Navigational Control (GNC) layers that are designed to increase cross vehicle and domain deployments. Each component of the architecture is documented, tested, and its application to the control architecture as a whole is reported.</p>

Several manufacturers of road vehicles today are working on developing autonomous vehicles. One subject that is often up for discussion when it comes to integrating autonomous road vehicles into the infrastructure is the safety aspect. There is in the context no common view of how safety should be quantified. As a contribution to this discussion we propose describing each potential hazardous event of a vehicle as a Semi-Markov Process (SMP). A reliability-based method for using the semi-Markov representation to calculate the probability of a hazardous event to occur is presented. The method simplifies the expression for the reliability using the Laplace-Stieltjes transform and calculates the transform of the reliability exactly. Numerical inversion algorithms are then applied to approximate the reliability up to a desired error tolerance. The method is validated using alternative techniques and is thereafter applied to a system for automated steering based on a real example from the industry. A desired evolution of the method is to involve a framework for how to represent each hazardous event as a SMP. / Flertalet tillverkare av vägfordon jobbar idag på att utveckla autonoma fordon. Ett ämne ofta på agendan i diskussionen om att integrera autonoma fordon på vägarna är säkerhet. Det finns i sammanhanget ingen klar bild över hur säkerhet ska kvantifieras. Som ett bidrag till denna diskussion föreslås här att beskriva varje potentiellt farlig situation av ett fordon som en Semi-Markov process (SMP). En metod presenteras för att via beräkning av funktionssäkerheten nyttja semi-Markov representationen för att beräkna sannolikheten för att en farlig situation ska uppstå. Metoden nyttjar Laplace-Stieltjes transformen för att förenkla uttrycket för funktionssäkerheten och beräknar transformen av funktionssäkerheten exakt. Numeriska algoritmer för den inversa transformen appliceras sedan för att beräkna funktionssäkerheten upp till en viss feltolerans. Metoden valideras genom alternativa tekniker och appliceras sedan på ett system för autonom styrning baserat på ett riktigt exempel från industrin. En fördelaktig utveckling av metoden som presenteras här skulle vara att involvera ett ramverk för hur varje potentiellt farlig situation ska representeras som en SMP.

No description available.

<p dir="ltr">The growing movement toward sustainable use of ocean resources is driven by the pressing need to alleviate environmental and human stressors on the planet and its oceans. From monitoring the food web to supporting sustainable fisheries and observing environmental shifts to protect against the effects of climate change, ocean observations significantly impact the Blue Economy. Acknowledging the critical role of Autonomous Underwater Vehicles (AUVs) in achieving persistent ocean exploration, this research addresses challenges focusing on the limited energy and storage capacity of AUVs, introducing a comprehensive underwater docking solution with a specific emphasis on enhancing the terminal homing phase through innovative vision algorithms leveraging neural networks.</p><p dir="ltr">The primary goal of this work is to establish a docking procedure that is failure-tolerant, scalable, and systematically validated across diverse environmental conditions. To fulfill this objective, a robust dock detection mechanism has been developed that ensures the resilience of the docking procedure through \comment{an} improved detection in different challenging environmental conditions. Additionally, the study addresses the prevalent issue of data sparsity in the marine domain by artificially generating data using CycleGAN and Artistic Style Transfer. These approaches effectively provide sufficient data for the docking detection algorithm, improving the localization of the docking station.</p><p dir="ltr">Furthermore, this work introduces methods to compress the learned docking detection model without compromising performance, enhancing the efficiency of the overall system. Alongside these advancements, a station-keeping algorithm is presented, enabling the mobile docking station to maintain position and heading while awaiting the arrival of the AUV. To leverage the sensors onboard and to take advantage of the computational resources to their fullest extent, this research has demonstrated the feasibility of simultaneously learning docking detection and marine wildlife classification through multi-task and transfer learning. This multifaceted approach not only tackles the limitations of AUVs' energy and storage capacity but also contributes to the robustness, scalability, and systematic validation of underwater docking procedures, aligning with the broader goals of sustainable ocean exploration and the blue economy.</p>

[ES] En la actualidad, uno de los principales desafíos a los que se enfrentan las grandes áreas metropolitanas es la congestión provocada por el tráfico, la cual se ha convertido en un problema importante al que se enfrentan las autoridades de cada ciudad. Para abordar este problema es necesario implementar una solución eficiente para controlar el tráfico que genere beneficios para los ciudadanos, como reducir los tiempos de viaje de los vehículos y, en consecuencia, el consumo de combustible, el ruido, y la contaminación ambiental. De hecho, al analizar adecuadamente la demanda de tráfico, es posible predecir las condiciones futuras del tráfico, y utilizar esa información para la optimización de las rutas tomadas por los vehículos. Este enfoque puede ser especialmente efectivo si se aplica en el contexto de los vehículos autónomos, que tienen un comportamiento más predecible, lo cual permite a los administradores de la ciudad mitigar los efectos de la congestión, como es la contaminación, al mejorar el flujo de tráfico de manera totalmente centralizada. La validación de este enfoque generalmente requiere el uso de simulaciones que deberían ser lo más realistas posible. Sin embargo, lograr altos grados de realismo puede ser complejo cuando los patrones de tráfico reales, definidos a través de una matriz de Origen/Destino (O-D) para los vehículos en una ciudad, son desconocidos, como ocurre la mayoría de las veces. Por lo tanto, la primera contribución de esta tesis es desarrollar una heurística iterativa para mejorar el modelado de la congestión de tráfico; a partir de las mediciones de bucle de inducción reales hechas por el Ayuntamiento de Valencia (España), pudimos generar una matriz O-D para la simulación de tráfico que se asemeja a la distribución de tráfico real. Si fuera posible caracterizar el estado del tráfico prediciendo las condiciones futuras del tráfico para optimizar la ruta de los vehículos automatizados, y si se pudieran tomar estas medidas para mitigar de manera preventiva los efectos de la congestión con sus problemas relacionados, se podría mejorar el flujo de tráfico en general. Por lo tanto, la segunda contribución de esta tesis es desarrollar una Ecuación de Predicción de Tráfico para caracterizar el comportamiento en las diferentes calles de la ciudad en términos de tiempo de viaje con respecto al volumen de tráfico, y aplicar una regresión logística a esos datos para predecir las condiciones futuras del tráfico. La tercera y última contribución de esta tesis apunta directamente al nuevo paradigma de gestión de tráfico previsto, tratándose de un servidor de rutas capaz de manejar todo el tráfico en una ciudad, y equilibrar los flujos de tráfico teniendo en cuenta las condiciones de congestión del tráfico presentes y futuras. Por lo tanto, realizamos un estudio de simulación con datos reales de congestión de tráfico en la ciudad de Valencia (España), para demostrar cómo se puede mejorar el flujo de tráfico en un día típico mediante la solución propuesta. Los resultados experimentales muestran que nuestra solución, combinada con una actualización frecuente de las condiciones del tráfico en el servidor de rutas, es capaz de lograr mejoras sustanciales en términos de velocidad promedio y tiempo de trayecto, ambos indicadores de un menor grado de congestión y de una mejor fluidez del tráfico. / [CA] En l'actualitat, un dels principals desafiaments als quals s'enfronten les grans àrees metropolitanes és la congestió provocada pel trànsit, que s'ha convertit en un problema important al qual s'enfronten les autoritats de cada ciutat. Per a abordar aquest problema és necessari implementar una solució eficient per a controlar el trànsit que genere beneficis per als ciutadans, com reduir els temps de viatge dels vehicles i, en conseqüència, el consum de combustible, el soroll, i la contaminació ambiental. De fet, en analitzar adequadament la demanda de trànsit, és possible predir les condicions futures del trànsit, i utilitzar aqueixa informació per a l'optimització de les rutes preses pels vehicles. Aquest enfocament pot ser especialment efectiu si s'aplica en el context dels vehicles autònoms, que tenen un comportament més predictible, i això permet als administradors de la ciutat mitigar els efectes de la congestió, com és la contaminació, en millorar el flux de trànsit de manera totalment centralitzada. La validació d'aquest enfocament generalment requereix l'ús de simulacions que haurien de ser el més realistes possible. No obstant això, aconseguir alts graus de realisme pot ser complex quan els patrons de trànsit reals, definits a través d'una matriu d'Origen/Destinació (O-D) per als vehicles en una ciutat, són desconeguts, com ocorre la majoria de les vegades. Per tant, la primera contribució d'aquesta tesi és desenvolupar una heurística iterativa per a millorar el modelatge de la congestió de trànsit; a partir dels mesuraments de bucle d'inducció reals fetes per l'Ajuntament de València (Espanya), vam poder generar una matriu O-D per a la simulació de trànsit que s'assembla a la distribució de trànsit real. Si fóra possible caracteritzar l'estat del trànsit predient les condicions futures del trànsit per a optimitzar la ruta dels vehicles automatitzats, i si es pogueren prendre aquestes mesures per a mitigar de manera preventiva els efectes de la congestió amb els seus problemes relacionats, es podria millorar el flux de trànsit en general. Per tant, la segona contribució d'aquesta tesi és desenvolupar una Equació de Predicció de Trànsit per a caracteritzar el comportament en els diferents carrers de la ciutat en termes de temps de viatge respecte al volum de trànsit, i aplicar una regressió logística a aqueixes dades per a predir les condicions futures del trànsit. La tercera i última contribució d'aquesta tesi apunta directament al nou paradigma de gestió de trànsit previst. Es tracta d'un servidor de rutes capaç de manejar tot el trànsit en una ciutat, i equilibrar els fluxos de trànsit tenint en compte les condicions de congestió del trànsit presents i futures. Per tant, realitzem un estudi de simulació amb dades reals de congestió de trànsit a la ciutat de València (Espanya), per a demostrar com es pot millorar el flux de trànsit en un dia típic mitjançant la solució proposada. Els resultats experimentals mostren que la nostra solució, combinada amb una actualització freqüent de les condicions del trànsit en el servidor de rutes, és capaç d'aconseguir millores substancials en termes de velocitat faig una mitjana i de temps de trajecte, tots dos indicadors d'un grau menor de congestió i d'una fluïdesa millor del trànsit. / [EN] Currently, one of the main challenges that large metropolitan areas have to face is traffic congestion, which has become an important problem faced by city authorities. To address this problem, it becomes necessary to implement an efficient solution to control traffic that generates benefits for citizens, such as reducing vehicle journey times and, consequently, use of fuel, noise and environmental pollution. In fact, by properly analyzing traffic demand, it becomes possible to predict future traffic conditions, and to use that information for the optimization of the routes taken by vehicles. Such an approach becomes especially effective if applied in the context of autonomous vehicles, which have a more predictable behavior, thus enabling city management entities to mitigate the effects of traffic congestion and pollution by improving the traffic flow in a city in a fully centralized manner. Validating this approach typically requires the use of simulations, which should be as realistic as possible. However, achieving high degrees of realism can be complex when the actual traffic patterns, defined through an Origin/Destination (O-D) matrix for the vehicles in a city, are unknown, as occurs most of the times. Thus, the first contribution of this thesis is to develop an iterative heuristic for improving traffic congestion modeling; starting from real induction loop measurements made available by the City Hall of Valencia, Spain, we were able to generate an O-D matrix for traffic simulation that resembles the real traffic distribution. If it were possible to characterize the state of traffic by predicting future traffic conditions for optimizing the route of automated vehicles, and if these measures could be taken to preventively mitigate the effects of congestion with its related problems, the overall traffic flow could be improved. Thereby, the second contribution of this thesis was to develop a Traffic Prediction Equation to characterize the different streets of a city in terms of travel time with respect to the vehicle load, and applying logistic regression to those data to predict future traffic conditions. The third and last contribution of this thesis towards our envisioned traffic management paradigm was a route server capable of handling all the traffic in a city, and balancing traffic flows by accounting for present and future traffic congestion conditions. Thus, we perform a simulation study using real data of traffic congestion in the city of Valencia, Spain, to demonstrate how the traffic flow in a typical day can be improved using our proposed solution. Experimental results show that our proposed solution, combined with frequent updating of traffic conditions on the route server, is able to achieve substantial improvements in terms of average travel speeds and travel times, both indicators of lower degrees of congestion and improved traffic fluidity. / Finally, I want to thank the Ecuatorian Republic through the "Secretaría de Educación Superior, Ciencia, Tecnología e Innovación" (SENESCYT), for granting me the scholarship to finance my studies. / Zambrano Martínez, JL. (2019). Efficient Traffic Management in Urban Environments [Tesis doctoral]. Universitat Politècnica de València. https://doi.org/10.4995/Thesis/10251/129865

Face à l’évolution rapide des technologies nécessaires à l’automatisation de la conduite au cours de ces dernières années, les grands constructeurs automobiles promettent la commercialisation de véhicules autonomes à l’horizon 2020. Cependant, la définition des interactions entre les systèmes de conduite automatisée et le conducteur au cours de la tâche de conduite reste une question ouverte. L'objectif de cette thèse est de concevoir, développer et évaluer des principes de coopération entre le conducteur et les systèmes de conduite automatisée. Compte tenu de la complexité d'un tel Système Homme-Machine, la thèse propose, en premier lieu une architecture de contrôle coopératif hiérarchique et deux principes de coopération généraux sur deux niveaux dans l’architecture qui serviront ensuite de base commune pour la conception des systèmes coopératifs développés pour les cas d’usages définis. Afin d’assurer une coopération efficace avec le conducteur dans un environnement de conduite dynamique, le véhicule autonome a besoin de comprendre la situation et de partager sa compréhension de la situation avec le conducteur. Pour cela, cette thèse propose un formalisme de représentation de la scène de conduite basé sur le repère de Frenet. Ensuite, une méthode de prédiction de trajectoire est également proposée. Sur la base de la détection de manœuvre et de l'estimation du jerk, cette méthode permet d’améliorer la précision de la trajectoire prédite comparée à celle déterminée par la méthode basée sur une hypothèse d'accélération constante. Dans la partie d’études de cas, deux principes de coopération sont mis en œuvre dans deux cas d’usage. Dans le premier cas de la gestion d’insertion sur autoroute, un système de contrôle longitudinal coopératif est conçu. Il comporte une fonction de planification de manœuvre et de génération de trajectoire basée sur la commande prédictive. En fonction du principe de coopération, ce système peut à la fois gérer automatiquement l’insertion d’un véhicule et donner la possibilité au conducteur de changer la décision du système. Dans le second cas d'usage qui concerne le contrôle de trajectoire et le changement de voie sur autoroute, le problème de partage du contrôle est formulé comme un problème d’optimisation sous contraintes qui est résolu en ligne en utilisant l’approche de la commande prédictive (MPC). Cette approche assure le transfert continu de l’autorité du contrôle entre le système et le conducteur en adaptant les pondérations dans la fonction de coût et en mettant en œuvre des contraintes dynamiques en ligne dans le modèle prédictif, tout en informant le conducteur des dangers potentiels grâce au retour haptique sur le volant. Les deux systèmes sont évalués à l’aide de tests utilisateur sur simulateur de conduite. En fonction des résultats des tests, cette thèse discute la question des facteurs humains et la perception de l'utilisateur sur les principes de coopération. / Given rapid advancement of automated driving (AD) technologies in recent years, major car makers promise the commercialization of AD vehicles within one decade from now. However, how the automation should interact with human drivers remains an open question. The objective of this thesis is to design, develop and evaluate interaction principles for AD systems that can cooperate with a human driver. Considering the complexity of such a human-machine system, this thesis begins with proposing two general cooperation principles and a hierarchical cooperative control architecture to lay a common basis for interaction and system design in the defined use cases. Since the proposed principles address a dynamic driving environment involving manually driven vehicles, the AD vehicle needs to understand it and to share its situational awareness with the driver for efficient cooperation. This thesis first proposes a representation formalism of the driving scene in the Frenet frame to facilitate the creation of the spatial awareness of the AD system. An adaptive vehicle longitudinal trajectory prediction method is also presented. Based on maneuver detection and jerk estimation, this method yields better prediction accuracy than the method based on constant acceleration assumption. As case studies, this thesis implements two cooperation principles for two use cases respectively. In the first use case of highway merging management, this thesis proposes a cooperative longitudinal control framework featuring an ad-hoc maneuver planning function and a model predictive control (MPC) based trajectory generation for transient maneuvers. This framework can automatically handle a merging vehicle, and at the mean time it offers the driver a possibility to change the intention of the system. In another use case concerning highway lane positioning and lane changing, a shared steering control problem is formulated in MPC framework. By adapting the weight on the stage cost and implementing dynamic constraints online, the MPC ensures seamless control transfer between the system and the driver while conveying potential hazards through haptic feedback. Both of the designed systems are evaluated through user tests on driving simulator. Finally, human factors issue and user’s perception on these new interaction paradigms are discussed.