Dissertations / Theses: 'Autonomous Driving Systems'

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Al-Khoury, Fadi. "Safety of Machine Learning Systems in Autonomous Driving." Thesis, KTH, Skolan för industriell teknik och management (ITM), 2017. http://urn.kb.se/resolve?urn=urn:nbn:se:kth:diva-218020.

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Machine Learning, and in particular Deep Learning, are extremely capable tools for solving problems which are difficult, or intractable to tackle analytically. Application areas include pattern recognition, computer vision, speech and natural language processing. With the automotive industry aiming for increasing amount of automation in driving, the problems to solve become increasingly complex, which appeals to the use of supervised learning methods from Machine Learning and Deep Learning. With this approach, solutions to the problems are learned implicitly from training data, and inspecting their correctness is not possible directly. This presents concerns when the resulting systems are used to support safety-critical functions, as is the case with autonomous driving of automotive vehicles. This thesis studies the safety concerns related to learning systems within autonomous driving and applies a safety monitoring approach to a collision avoidance scenario. Experiments are performed using a simulated environment, with a deep learning system supporting perception for vehicle control, and a safety monitor for collision avoidance. The related operational situations and safety constraints are studied for an autonomous driving function, with potential faults in the learning system introduced and examined. Also, an example is considered for a measure that indicates trustworthiness of the learning system during operation.
Maskininlärning, och i synnerhet deep learning, är extremt kapabla verktyg för att lösa problem som är svåra, eller omöjliga att hantera analytiskt. Applikationsområden inkluderar mönsterigenkänning, datorseende, tal‐ och språkförståelse. När utvecklingen inom bilindustrin går mot en ökad grad av automatisering, blir problemen som måste lösas alltmer komplexa, vilket har lett till ett ökat användande av metoder från maskininlärning och deep learning. Med detta tillvägagångssätt lär sig systemet lösningen till ett problem implicit från träningsdata och man kan inte direkt utvärdera lösningens korrekthet. Detta innebär problem när systemet i fråga är del av en säkerhetskritisk funktion, vilket är fallet för självkörande fordon. Detta examensarbete behandlar säkerhetsaspekter relaterade till maskininlärningssystem i autonoma fordon och applicerar en safety monitoring‐metodik på en kollisionsundvikningsfunktion. Simuleringar utförs, med ett deep learning‐system som del av systemet för perception, som ger underlag för styrningen av fordonet, samt en safety monitor för kollisionsundvikning. De relaterade operationella situationerna och säkerhetsvillkoren studeras för en autonom körnings‐funktion, där potentiella fel i det lärande systemet introduceras och utvärderas. Vidare introduceras ett förslag på ett mått på trovärdighet hos det lärande systemet under drift.

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Agha, Jafari Wolde Bahareh. "A systematic Mapping study of ADAS and Autonomous Driving." Thesis, Mälardalens högskola, Akademin för innovation, design och teknik, 2019. http://urn.kb.se/resolve?urn=urn:nbn:se:mdh:diva-42754.

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Nowadays, autonomous driving revolution is getting closer to reality. To achieve the Autonomous driving the first step is to develop the Advanced Driver Assistance System (ADAS). Driver-assistance systems are one of the fastest-growing segments in automotive electronics since already there are many forms of ADAS available. To investigate state of art of development of ADAS towards Autonomous Driving, we develop Systematic Mapping Study (SMS). SMS methodology is used to collect, classify, and analyze the relevant publications. A classification is introduced based on the developments carried out in ADAS towards Autonomous driving. According to SMS methodology, we identified 894 relevant publications about ADAS and its developmental journey toward Autonomous Driving completed from 2012 to 2016. We classify the area of our research under three classifications: technical classifications, research types and research contributions. The related publications are classified under thirty-three technical classifications. This thesis sheds light on a better understanding of the achievements and shortcomings in this area. By evaluating collected results, we answer our seven research questions. The result specifies that most of the publications belong to the Models and Solution Proposal from the research type and contribution. The least number of the publications belong to the Automated…Autonomous driving from the technical classification which indicated the lack of publications in this area.

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Villalonga, Pineda Gabriel. "Leveraging Synthetic Data to Create Autonomous Driving Perception Systems." Doctoral thesis, Universitat Autònoma de Barcelona, 2021. http://hdl.handle.net/10803/671739.

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L’anotació manual d’imatges per desenvolupar sistemes basats en visió per computador ha estat un dels punts més problemàtics des que s’utilitza aprenentatge automàtic per a això. Aquesta tesi es centra en aprofitar les dades sintètiques per alleujar el cost de les anotacions manuals en tres tasques de percepció relacionades amb l’assistència a la conducció i la conducció autònoma. En tot moment assumim l’ús de xarxes neuronals convolucionals per al desenvolupament dels nostres models profunds de percepció. La primera tasca planteja el reconeixement de senyals de trànsit, un problema de classificació d’imatges. Assumim que el nombre de classes de senyals de trànsit a reconèixer s’ha d’incrementar sense haver pogut anotar noves imatges amb què realitzar el corresponent reentrenament. Demostrem que aprofitant les dades sintètiques de les noves classes i transformant-les amb una xarxa adversària-generativa (GAN, de les seves sigles en anglès) entrenada amb les classes conegudes (sense usar mostres de les noves classes), és possible reentrenar la xarxa neuronal per classificar tots els senyals en una proporció ~1/4 entre classes noves i conegudes. La segona tasca consisteix en la detecció de vehicles i vianants (objectes) en imatges. En aquest cas, assumim la recepció d’un conjunt d’imatges sense anotar. L’objectiu és anotar automàticament aquestes imatges perquè així es puguin utilitzar posteriorment en l’entrenament del detector d’objectes que desitgem. Per assolir aquest objectiu, vam partir de dades sintètiques anotades i proposem un mètode d’aprenentatge semi-supervisat basat en la idea del co-aprenentatge. A més, utilitzem una GAN per reduir la distància entre els dominis sintètic i real abans d’aplicar el co-aprenentatge. Els nostres resultats quantitatius mostren que el procediment desenvolupat permet anotar el conjunt d’imatges d’entrada amb la precisió suficient per entrenar detectors d’objectes de forma efectiva; és a dir, tan precisos com si les imatges s’haguessin anotat manualment. A la tercera tasca deixem enrere l’espai 2D de les imatges, i ens centrem en processar núvols de punts 3D provinents de sensors LiDAR. El nostre objectiu inicial era desenvolupar un detector d’objectes 3D (vehicles, vianants, ciclistes) entrenat en núvols de punts sintètics estil LiDAR. En el cas de les imatges es podia esperar el problema de canvi de domini degut a les diferències visuals entre les imatges sintètiques i reals. Però, a priori, no esperàvem el mateix en treballar amb núvols de punts LiDAR, ja que es tracta d’informació geomètrica provinent del mostreig actiu del món, sense que l’aparença visual influeixi. No obstant això, a la pràctica, hem vist que també apareixen els problemes d’adaptació de domini. Factors com els paràmetres de mostreig del LiDAR, la configuració dels sensors a bord del vehicle autònom, i l’anotació manual dels objectes 3D, indueixen diferències de domini. A la tesi demostrem aquesta observació mitjançant un exhaustiu conjunt d’experiments amb diferents bases de dades públiques i detectors 3D disponibles. Per tant, en relació amb la tercera tasca, el treball s’ha centrat finalment en el disseny d’una GAN capaç de transformar núvols de punts 3D per portar-los d’un domini a un altre, un tema relativament inexplorat.Finalment, cal esmentar que tots els conjunts de dades sintètiques usats en aquestes tres tasques han estat dissenyats i generats en el context d’aquesta tesi doctoral i es faran públics. En general, considerem que aquesta tesi presenta un avanç en el foment de la utilització de dades sintètiques per al desenvolupament de models profunds de percepció, essencials en el camp de la conducció autònoma.
La anotación manual de imágenes para desarrollar sistemas basados en visión por computador ha sido uno de los puntos más problemáticos desde que se utiliza aprendizaje automático para ello. Esta tesis se centra en aprovechar los datos sintéticos para aliviar el coste de las anotaciones manuales en tres tareas de percepción relacionadas con la asistencia a la conducción y la conducción autónoma. En todo momento asumimos el uso de redes neuronales convolucionales para el desarrollo de nuestros modelos profundos de percepción. La primera tarea plantea el reconocimiento de señales de tráfico, un problema de clasificación de imágenes. Asumimos que el número de clases de señales de tráfico a reconocer se debe incrementar sin haber podido anotar nuevas imágenes con las que realizar el correspondiente reentrenamiento. Demostramos que aprovechando los datos sintéticos de las nuevas clases y transformándolas con una red adversaria-generativa (GAN, de sus siglas en inglés) entrenada con las clases conocidas (sin usar muestras de las nuevas clases), es posible reentrenar la red neuronal para clasificar todas las señales en una proporción de ~1/4 entre clases nuevas y conocidas. La segunda tarea consiste en la detección de vehículos y peatones (objetos) en imágenes. En este caso, asumimos la recepción de un conjunto de imágenes sin anotar. El objetivo es anotar automáticamente esas imágenes para que así se puedan utilizar posteriormente en el entrenamiento del detector de objetos que deseemos. Para alcanzar este objetivo, partimos de datos sintéticos anotados y proponemos un método de aprendizaje semi-supervisado basado en la idea del co-aprendizaje. Además, utilizamos una GAN para reducir la distancia entre los dominios sintético y real antes de aplicar el co-aprendizaje. Nuestros resultados cuantitativos muestran que el procedimiento desarrollado permite anotar el conjunto de imágenes de entrada con la precisión suficiente para entrenar detectores de objetos de forma efectiva; es decir, tan precisos como si las imágenes se hubiesen anotado manualmente. En la tercera tarea dejamos atrás el espacio 2D de las imágenes, y nos centramos en procesar nubes de puntos 3D provenientes de sensores LiDAR. Nuestro objetivo inicial era desarrollar un detector de objetos 3D (vehículos, peatones, ciclistas) entrenado en nubes de puntos sintéticos estilo LiDAR. En el caso de las imágenes cabía esperar el problema de cambio de dominio debido a las diferencias visuales entre las imágenes sintéticas y reales. Pero, a priori, no esperábamos lo mismo al trabajar con nubes de puntos LiDAR, ya que se trata de información geométrica proveniente del muestreo activo del mundo, sin que la apariencia visual influya. Sin embargo, en la práctica, hemos visto que también aparecen los problemas de adaptación de dominio. Factores como los parámetros de muestreo del LiDAR, la configuración de los sensores a bordo del vehículo autónomo, y la anotación manual de los objetos 3D, inducen diferencias de dominio. En la tesis demostramos esta observación mediante un exhaustivo conjunto de experimentos con diferentes bases de datos públicas y detectores 3D disponibles. Por tanto, en relación a la tercera tarea, el trabajo se ha centrado finalmente en el diseño de una GAN capaz de transformar nubes de puntos 3D para llevarlas de un dominio a otro, un tema relativamente inexplorado. Finalmente, cabe mencionar que todos los conjuntos de datos sintéticos usados en estas tres tareas han sido diseñados y generados en el contexto de esta tesis doctoral y se harán públicos. En general, consideramos que esta tesis presenta un avance en el fomento de la utilización de datos sintéticos para el desarrollo de modelos profundos de percepción, esenciales en el campo de la conducción autónoma.
Manually annotating images to develop vision models has been a major bottleneck since computer vision and machine learning started to walk together. This thesis focuses on leveraging synthetic data to alleviate manual annotation for three perception tasks related to driving assistance and autonomous driving. In all cases, we assume the use of deep convolutional neural networks (CNNs) to develop our perception models. The first task addresses traffic sign recognition (TSR), a kind of multi-class classification problem. We assume that the number of sign classes to be recognized must be suddenly increased without having annotated samples to perform the corresponding TSR CNN re-training. We show that leveraging synthetic samples of such new classes and transforming them by a generative adversarial network (GAN) trained on the known classes (i.e., without using samples from the new classes), it is possible to re-train the TSR CNN to properly classify all the signs for a ~1/4 ratio of new/known sign classes. The second task addresses on-board 2D object detection, focusing on vehicles and pedestrians. In this case, we assume that we receive a set of images without the annotations required to train an object detector, i.e., without object bounding boxes. Therefore, our goal is to self-annotate these images so that they can later be used to train the desired object detector. In order to reach this goal, we leverage from synthetic data and propose a semi-supervised learning approach based on the co-training idea. In fact, we use a GAN to reduce the synth-to-real domain shift before applying co-training. Our quantitative results show that co-training and GAN-based image-to-image translation complement each other up to allow the training of object detectors without manual annotation, and still almost reaching the upper-bound performances of the detectors trained from human annotations. While in previous tasks we focus on vision-based perception, the third task we address focuses on LiDAR pointclouds. Our initial goal was to develop a 3D object detector trained on synthetic LiDAR-style pointclouds. While for images we may expect synth/real-to-real domain shift due to differences in their appearance (e.g. when source and target images come from different camera sensors), we did not expect so for LiDAR pointclouds since these active sensors factor out appearance and provide sampled shapes. However, in practice, we have seen that it can be domain shift even among real-world LiDAR pointclouds. Factors such as the sampling parameters of the LiDARs, the sensor suite configuration on-board the ego-vehicle, and the human annotation of 3D bounding boxes, do induce a domain shift. We show it through comprehensive experiments with different publicly available datasets and 3D detectors. This redirected our goal towards the design of a GAN for pointcloud-to-pointcloud translation, a relatively unexplored topic. Finally, it is worth to mention that all the synthetic datasets used for these three tasks, have been designed and generated in the context of this PhD work and will be publicly released. Overall, we think this PhD presents several steps forward to encourage leveraging synthetic data for developing deep perception models in the field of driving assistance and autonomous driving.
Universitat Autònoma de Barcelona. Programa de Doctorat en Informàtica

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Sharma, Devendra. "Evaluation and Analysis of Perception Systems for Autonomous Driving." Thesis, KTH, Skolan för elektroteknik och datavetenskap (EECS), 2020. http://urn.kb.se/resolve?urn=urn:nbn:se:kth:diva-291423.

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For safe mobility, an autonomous vehicle must perceive the surroundings accurately. There are many perception tasks associated with understanding the local environment such as object detection, localization, and lane analysis. Object detection, in particular, plays a vital role in determining an object’s location and classifying it correctly and is one of the challenging tasks in the self-driving research area. Before employing an object detection module in autonomous vehicle testing, an organization needs to have a precise analysis of the module. Hence, it becomes crucial for a company to have an evaluation framework to evaluate an object detection algorithm’s performance. This thesis develops a comprehensive framework for evaluating and analyzing object detection algorithms, both 2D (camera images based) and 3D (LiDAR point cloud-based). The pipeline developed in this thesis provides the ability to evaluate multiple models with ease, signified by the key performance metrics, Average Precision, F-score, and Mean Average Precision. 40-point interpolation method is used to calculate the Average Precision.
För säker rörlighet måste ett autonomt fordon uppfatta omgivningen exakt. Det finns många uppfattningsuppgifter associerade med att förstå den lokala miljön, såsom objektdetektering, lokalisering och filanalys. I synnerhet objektdetektering spelar en viktig roll för att bestämma ett objekts plats och klassificera det korrekt och är en av de utmanande uppgifterna inom det självdrivande forskningsområdet. Innan en anställd detekteringsmodul används i autonoma fordonsprovningar måste en organisation ha en exakt analys av modulen. Därför blir det avgörande för ett företag att ha en utvärderingsram för att utvärdera en objektdetekteringsalgoritms prestanda. Denna avhandling utvecklar ett omfattande ramverk för utvärdering och analys av objektdetekteringsalgoritmer, både 2 D (kamerabilder baserade) och 3 D (LiDAR-punktmolnbaserade). Rörledningen som utvecklats i denna avhandling ger möjlighet att enkelt utvärdera flera modeller, betecknad med nyckelprestandamätvärdena, Genomsnittlig precision, F-poäng och genomsnittlig genomsnittlig precision. 40-punkts interpoleringsmetod används för att beräkna medelprecisionen.

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Behere, Sagar. "Architecting Autonomous Automotive Systems : With an emphasis on Cooperative Driving." Licentiate thesis, KTH, Inbyggda styrsystem, 2013. http://urn.kb.se/resolve?urn=urn:nbn:se:kth:diva-120595.

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The increasing usage of electronics and software in a modern automobile enables realization of many advanced features. One such feature is autonomous driving. Autonomous driving means that a human driver’s intervention is not required to drive the automobile; rather, theautomobile is capable of driving itself. Achieving automobile autonomyrequires research in several areas, one of which is the area of automotive electrical/electronics (E/E) architectures. These architectures deal with the design of the computer hardware and software present inside various subsystems of the vehicle, with particular attention to their interaction and modularization. The aim of this thesis is to investigate how automotive E/E architectures should be designed so that 1) it ispossible to realize autonomous features and 2) a smooth transition canbe made from existing E/E architectures, which have no explicit support for autonomy, to future E/E architectures that are explicitly designed for autonomy.The thesis begins its investigation by considering the specific problem of creating autonomous behavior under cooperative driving condi-tions. Cooperative driving conditions are those where continuous wireless communication exists between a vehicle and its surroundings, which consist of the local road infrastructure as well as the other vehicles in the vicinity. In this work, we define an original reference architecture for cooperative driving. The reference architecture demonstrates how a subsystem with specific autonomy features can be plugged into an existing E/E architecture, in order to realize autonomous driving capabilities. Two salient features of the reference architecture are that it isminimally invasive and that it does not dictate specific implementation technologies. The reference architecture has been instantiated on two separate occasions and is the main contribution of this thesis. Another contribution of this thesis is a novel approach to the design of general, autonomous, embedded systems architectures. The approach introduces an artificial consciousness within the architecture, that understands the overall purpose of the system and also how the different existing subsystems should work together in order to meet that purpose.This approach can enable progressive autonomy in existing embedded systems architectures, over successive design iterations.

QC 20130412

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Behere, Sagar. "Reference Architectures for Highly Automated Driving." Doctoral thesis, KTH, Inbyggda styrsystem, 2016. http://urn.kb.se/resolve?urn=urn:nbn:se:kth:diva-179306.

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Highly automated driving systems promise increased road traffic safety, as well as positive impacts on sustainable transportation by means of increased traffic efficiency and environmental friendliness. The design and development of such systems require scientific advances in a number of areas. One area is the vehicle's electrical/electronic (E/E) architecture. The E/E architecture can be presented using a number of views, of which an important one is the functional view. The functional view describes the decomposition of the system into its main logical components, along with the hierarchical structure, the component inter-connections, and requirements. When this view captures the principal ideas and patterns that constitute the foundation of a variety of specific architectures, it may be termed as a reference architecture. Two reference architectures for highly automated driving form the principal contribution of this thesis. The first reference architecture is for cooperative driving. In a cooperative driving situation, vehicles and road infrastructure in the vicinity of a vehicle continuously exchange wireless information and this information is then used to control the motion of the vehicle. The second reference architecture is for autonomous driving, wherein the vehicle is capable of driver-less operation even without direct communication with external entities. The description of both reference architectures includes their main components and the rationale for how these components should be distributed across the architecture and its layers. These architectures have been validated via multiple real-world instantiations, and the guidelines for instantiation also form part of the architecture description. A comparison with similar architectures is also provided, in order to highlight the similarities and differences. The comparisons show that in the context of automated driving, the explicit recognition of components for semantic understanding, world modeling, and vehicle platform abstraction are unique to the proposed architecture. These components are not unusual in architectures within the Artificial Intelligence/robotics domains; the proposed architecture shows how they can be applied within the automotive domain. A secondary contribution of this thesis is a description of a lightweight, four step approach for model based systems engineering of highly automated driving systems, along with supporting model classes. The model classes cover the concept of operations, logical architecture, application software components, and the implementation platforms. The thesis also provides an overview of current implementation technologies for cognitive driving intelligence and vehicle platform control, and recommends a specific setup for development and accelerated testing of highly automated driving systems, that includes model- and hardware-in-the-loop techniques in conjunction with a publish/subscribe bus. Beyond the more "traditional" engineering concepts, the thesis also investigates the domain of machine consciousness and computational self-awareness. The exploration indicates that current engineering methods are likely to hit a complexity ceiling, breaking through which may require advances in how safety-critical systems can self-organize, construct, and evaluate internal models to reflect their perception of the world. Finally, the thesis also presents a functional architecture for the brake system of an autonomous truck. This architecture proposes a reconfiguration of the existing brake systems of the truck in a way that provides dynamic, diversified redundancy, and an increase in the system reliability and availability, while meeting safety requirements.

QC 20151216

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Veeramani, Lekamani Sarangi. "Model Based Systems Engineering Approach to Autonomous Driving : Application of SysML for trajectory planning of autonomous vehicle." Thesis, KTH, Skolan för elektroteknik och datavetenskap (EECS), 2018. http://urn.kb.se/resolve?urn=urn:nbn:se:kth:diva-254891.

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Model Based Systems Engineering (MBSE) approach aims at implementing various processes of Systems Engineering (SE) through diagrams that provide different perspectives of the same underlying system. This approach provides a basis that helps develop a complex system in a systematic manner. Thus, this thesis aims at deriving a system model through this approach for the purpose of autonomous driving, specifically focusing on developing the subsystem responsible for generating a feasible trajectory for a miniature vehicle, called AutoCar, to enable it to move towards a goal. The report provides a background on MBSE and System Modeling Language (SysML) which is used for modelling the system. With this background, an MBSE framework for AutoCar is derived and the overall system design is explained. This report further explains the concepts involved in autonomous trajectory planning followed by an introduction to Robot Operating System (ROS) and its application for trajectory planning of the system. The report concludes with a detailed analysis on the benefits of using this approach for developing a system. It also identifies the shortcomings of applying MBSE to system development. The report closes with a mention on how the given project can be further carried forward to be able to realize it on a physical system.
Modellbaserade systemteknikens (MBSE) inriktning syftar till att implementera de olika processerna i systemteknik (SE) genom diagram som ger olika perspektiv på samma underliggande system. Detta tillvägagångssätt ger en grund som hjälper till att utveckla ett komplext system på ett systematiskt sätt. Sålunda syftar denna avhandling att härleda en systemmodell genom detta tillvägagångssätt för autonom körning, med särskild inriktning på att utveckla delsystemet som är ansvarigt för att generera en genomförbar ban för en miniatyrbil, som kallas AutoCar, för att göra det möjligt att nå målet. Rapporten ger en bakgrund till MBSE and Systemmodelleringsspråk (SysML) som används för modellering av systemet. Med denna bakgrund, MBSE ramverket för AutoCar är härledt och den övergripande systemdesignen förklaras. I denna rapport förklaras vidare begreppen autonom banplanering följd av en introduktion till Robot Operating System (ROS) och dess tillämpning för systemplanering av systemet. Rapporten avslutas med en detaljerad analys av fördelarna med att använda detta tillvägagångssätt för att utveckla ett system. Det identifierar också bristerna för att tillämpa MBSE på systemutveckling. Rapporten stänger med en omtale om hur det givna projektet kan vidarebefordras för att kunna realisera det på ett fysiskt system.

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Perez, Cervantes Marcus Sebastian. "Issues of Control with Older Drivers and Future Automated Driving Systems." Research Showcase @ CMU, 2011. http://repository.cmu.edu/theses/21.

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It is inevitable that as a person ages they will encounter different physical and cognitive impairments as well as dynamic social issues. We started this project under the assumption that autonomous driving would greatly benefit the fastest growing population in developed countries, the elderly. However, the larger question at hand was how are older drivers going to interact with future automated driving systems? It was through the qualitative research we conducted that we were able to uncover the answer to this question; older drivers are not willing to give up “control” to autonomous cars. As interaction designers, we need to define what type of interactions need to occur in these future automated driving systems, so older drivers still feel independent and in control when driving. Lawrence D. Burns, former Vice president of Research and Development at General Motors and author of Reinventing the Automobile Personal Urban Mobility for the 21st Century talks about two driving factors that will shape the future of the automobile. These factors are energy and connectivity (Burns et al., 2010). We would add a third one, which is control. If we address these three factors we might be able to bridge the gap between how we drive today and how we will drive in the future and thus create more cohesive future automated driving systems.

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Jugade, Shriram. "Shared control authority between human and autonomous driving system for intelligent vehicles." Thesis, Compiègne, 2019. http://www.theses.fr/2019COMP2507.

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Ce travail aborde le problème de l’autorité de contrôle partagée entre les conducteurs et système de conduite autonome sans retour haptique utilisant la fusion des entrées de conduite. Le développement d’une autorité de contrôle partagée est divisé en différentes étapes : cadre de contrôle partagé, évaluation des facteurs de conduite, prévision du comportement de conduite, processus de fusion, etc. La résolution des conflits est la stratégie de haut niveau introduite dans le cadre permettant de réaliser la fusion. Les entrées de conduite sont évaluées en fonction de différents facteurs tels que le risque de collision, la limitation de vitesse, la prévention de voie / départ, etc., sous la forme d’un degré de confiance dans l’admissibilité d’une entrée de conduite à l’aide de données de capteur. La résolution de conflit est ciblée pour un horizon temporel particulier dans le futur en utilisant une prédiction d’entrée de conduite basée sur un capteur utilisant des réseaux de neurones. Un jeu non coopératif à deux joueurs (comprenant l’admissibilité et l’intention de conduite) est défini comme représentant la résolution du conflit comme un problème de négociation. L’entrée motrice finale est calculée en utilisant l’équilibre de Nash. La stratégie de contrôle partagé est validée à l’aide d’un banc d’essai intégré aux logiciels Simulink et IPG CarMaker. Divers aspects de la stratégie de contrôle partagé, tels que l’accent mis sur l’homme, la prévention des collisions, l’absence de toute information sur la conduite, l’affinement de la conduite manuelle, etc., ont été inclus dans le processus de validation
Road traffic accidents have always been a concern to the driving community which has led to various research developments for improving the way we drive the vehicles. Since human error causes most of the road accidents, introducing automation in the vehicle is an efficient way to address this issue thus making the vehicles intelligent. This approach has led to the development of ADAS (Advanced Driver Assistance Systems) functionalities. The process of introducing automation in the vehicle is continuously evolving. Currently the research in this field has targeted full autonomy of the vehicle with the aim to tackle the road safety to its fullest potential. The gap between ADAS and full autonomy is not narrow. One of the approach to bridge this gap is to introduce collaboration between human driver and autonomous system. There have been different methodologies such as haptic feedback, cooperative driving where the autonomous system adapts according to the human driving inputs/intention for the corrective action each having their own limitations. This work addresses the problem of shared control authority between human driver and autonomous driving system without haptic feedback using the fusion of driving inputs. The development of shared control authority is broadly divided into different stages i.e. shared control framework, driving input assessment, driving behavior prediction, fusion process etc. Conflict resolution is the high level strategy introduced in the framework for achieving the fusion. The driving inputs are assessed with respect to different factors such as collision risk, speed limitation,lane/road departure prevention etc in the form of degree of belief in the driving input admissibility using sensor data. The conflict resolution is targeted for a particular time horizon in the future using a sensor based driving input prediction using neural networks. A two player non-cooperative game (incorporating admissibility and driving intention) is defined to represent the conflict resolution as a bargaining problem. The final driving input is computed using the Nash equilibrium. The shared control strategy is validated using a test rig integrated with the software Simulink and IPG CarMaker. Various aspects of shared control strategy such as human-centered, collision avoidance, absence of any driving input, manual driving refinement etc were included in the validation process

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Kang, Yong Suk. "Development of Predictive Vehicle Control System using Driving Environment Data for Autonomous Vehicles and Advanced Driver Assistance Systems." Diss., Virginia Tech, 2018. http://hdl.handle.net/10919/85106.

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In the field of modern automotive engineering, many researchers are focusing on the development of advanced vehicle control systems such as autonomous vehicle systems and Advanced Driver Assistance Systems (ADAS). Furthermore, Driver Assistance Systems (DAS) such as cruise control, Anti-Lock Braking Systems (ABS), and Electronic Stability Control (ESC) have become widely popular in the automotive industry. Therefore, vehicle control research attracts attention from both academia and industry, and has been an active area of vehicle research for over 30 years, resulting in impressive DAS contributions. Although current vehicle control systems have improved vehicle safety and performance, there is room for improvement for dealing with various situations. The objective of the research is to develop a predictive vehicle control system for improving vehicle safety and performance for autonomous vehicles and ADAS. In order to improve the vehicle control system, the proposed system utilizes information about the upcoming local driving environment such as terrain roughness, elevation grade, bank angle, curvature, and friction. The local driving environment is measured in advance with a terrain measurement system to provide terrain data. Furthermore, in order to obtain the information about road conditions that cannot be measured in advance, this work begins by analyzing the response measurements of a preceding vehicle. The response measurements of a preceding vehicle are acquired through Vehicle-to-Vehicle (V2V) or Vehicle-to-Infrastructure (V2I) communication. The identification method analyzes the response measurements of a preceding vehicle to estimate road data. The estimated road data or the pre-measured road data is used as the upcoming driving environment information for the developed vehicle control system. The metric that objectively quantifies vehicle performance, the Performance Margin, is developed to accomplish the control objectives in an efficient manner. The metric is used as a control reference input and continuously estimated to predict current and future vehicle performance. Next, the predictive control algorithm is developed based on the upcoming driving environment and the performance metric. The developed system predicts future vehicle dynamics states using the upcoming driving environment and the Performance Margin. If the algorithm detects the risks of future vehicle dynamics, the control system intervenes between the driver's input commands based on estimated future vehicle states. The developed control system maintains vehicle handling capabilities based on the results of the prediction by regulating the metric into an acceptable range. By these processes, the developed control system ensures that the vehicle maintains stability consistently, and improves vehicle performance for the near future even if there are undesirable and unexpected driving circumstances. To implement and evaluate the integrated systems of this work, the real-time driving simulator, which uses precise real-world driving environment data, has been developed for advanced high computational vehicle control systems. The developed vehicle control system is implemented in the driving simulator, and the results show that the proposed system is a clear improvement on autonomous vehicle systems and ADAS.
Ph. D.

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Markgren, Jonas. "Creating a self-driving terrain vehicle in a simulated environment." Thesis, Umeå universitet, Institutionen för fysik, 2020. http://urn.kb.se/resolve?urn=urn:nbn:se:umu:diva-173273.

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Outside of the city environment, there are many unstructured and rough environments that are challenging in vehicle navigation tasks. In these environments, vehicle vibrations caused by rough terrain can be harmful for humans. In addition, a human operator can not work around the clock. A promising solution is to use artificial intelligence to replace human operators. I test this by using the artificial intelligence technique know as reinforcement learning, with the algorithm Proximal Policy Optimization, to perform some basic locomotion tasks in a simulated environment with a simple terrain vehicle. The terrain vehicle consists of two chassis, each having two wheels attached, connected to each other with an articulation joint that can rotate to turn the vehicle. I show that a trained model can learn to operate the terrain vehicle and complete basic tasks, such as finding and following a path while avoiding obstacles. I tested robustness by evaluating performance on sloped terrains with a model trained to operate on flat ground. The results from the tests with different slopes show that, for most environments, the trained model could handle slopes up to around 7.5-10 degrees without much issue, even though it had no way of detecting the slope. This tells us that the models can perform their tasks quite well even when disturbances are introduced, as long as these disturbances doesn't require them to significantly change their behaviors.

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Gresset, Constance, and David Morda. "Assessing the human barriers and impact of autonomous driving in transportation activities : A multiple case study." Thesis, Jönköping University, IHH, Företagsekonomi, 2021. http://urn.kb.se/resolve?urn=urn:nbn:se:hj:diva-52677.

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Background: The transport industry is facing new challenges such as increased competition between the actors and an increasing shortage of truck drivers. Implementing new technologies such as autonomous driving can represent a solution for companies to increase their competitiveness and gains. However, implementing such an innovative solution leads to a certain resistance to change that has to be dealt with, as well as concerns about the current jobs within the industry. Purpose: The purpose of this thesis is to assess the resistance to change linked to implementing this technology within Logistics Service Providers, provide solutions to overcome this resistance, as well as assessing the impact on jobs. Method: An inductive multiple case study has been used to conduct this research. The data was gathered from 12 semi-structured interviews with experts related to the transport industry. Then, thematic data analysis has been used to provide insights. Conclusion: The results show that the resistance is characterized by barriers to the technology and the resistance from the people, that support and communication is the key factor for successful implementation and that the truck driving professions will evolve considerably.

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Girbés, Juan Vicent. "Clothoid-based Planning and Control in Intelligent Vehicles (Autonomous and Manual-Assisted Driving)." Doctoral thesis, Universitat Politècnica de València, 2016. http://hdl.handle.net/10251/65072.

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[EN] Nowadays, there are many electronic products that incorporate elements and features coming from the research in the field of mobile robotics. For instance, the well-known vacuum cleaning robot Roomba by iRobot, which belongs to the field of service robotics, one of the most active within the sector. There are also numerous autonomous robotic systems in industrial warehouses and plants. It is the case of Autonomous Guided Vehicles (AGVs), which are able to drive completely autonomously in very structured environments. Apart from industry and consumer electronics, within the automotive field there are some devices that give intelligence to the vehicle, derived in most cases from advances in mobile robotics. In fact, more and more often vehicles incorporate Advanced Driver Assistance Systems (ADAS), such as navigation control with automatic speed regulation, lane change and overtaking assistant, automatic parking or collision warning, among other features. However, despite all the advances there are some problems that remain unresolved and can be improved. Collisions and rollovers stand out among the most common accidents of vehicles with manual or autonomous driving. In fact, it is almost impossible to guarantee driving without accidents in unstructured environments where vehicles share the space with other moving agents, such as other vehicles and pedestrians. That is why searching for techniques to improve safety in intelligent vehicles, either autonomous or manual-assisted driving, is still a trending topic within the robotics community. This thesis focuses on the design of tools and techniques for planning and control of intelligent vehicles in order to improve safety and comfort. The dissertation is divided into two parts, the first one on autonomous driving and the second one on manual-assisted driving. The main link between them is the use of clothoids as mathematical formulation for both trajectory generation and collision detection. Among the problems solved the following stand out: obstacle avoidance, rollover avoidance and advanced driver assistance to avoid collisions with pedestrians.
[ES] En la actualidad se comercializan infinidad de productos de electrónica de consumo que incorporan elementos y características procedentes de avances en el sector de la robótica móvil. Por ejemplo, el conocido robot aspirador Roomba de la empresa iRobot, el cual pertenece al campo de la robótica de servicio, uno de los más activos en el sector. También hay numerosos sistemas robóticos autónomos en almacenes y plantas industriales. Es el caso de los vehículos autoguiados (AGVs), capaces de conducir de forma totalmente autónoma en entornos muy estructurados. Además de en la industria y en electrónica de consumo, dentro del campo de la automoción también existen dispositivos que dotan de cierta inteligencia al vehículo, derivados la mayoría de las veces de avances en robótica móvil. De hecho, cada vez con mayor frecuencia los vehículos incorporan sistemas avanzados de asistencia al conductor (ADAS por sus siglas en inglés), tales como control de navegación con regulación automática de velocidad, asistente de cambio de carril y adelantamiento, aparcamiento automático o aviso de colisión, entre otras prestaciones. No obstante, pese a todos los avances siguen existiendo problemas sin resolver y que pueden mejorarse. La colisión y el vuelco destacan entre los accidentes más comunes en vehículos con conducción tanto manual como autónoma. De hecho, la dificultad de conducir en entornos desestructurados compartiendo el espacio con otros agentes móviles, tales como coches o personas, hace casi imposible garantizar la conducción sin accidentes. Es por ello que la búsqueda de técnicas para mejorar la seguridad en vehículos inteligentes, ya sean de conducción autónoma o manual asistida, es un tema que siempre está en auge en la comunidad robótica. La presente tesis se centra en el diseño de herramientas y técnicas de planificación y control de vehículos inteligentes, para la mejora de la seguridad y el confort. La disertación se ha dividido en dos partes, la primera sobre conducción autónoma y la segunda sobre conducción manual asistida. El principal nexo de unión es el uso de clotoides como elemento de generación de trayectorias y detección de colisiones. Entre los problemas que se resuelven destacan la evitación de obstáculos, la evitación de vuelcos y la asistencia avanzada al conductor para evitar colisiones con peatones.
[CAT] En l'actualitat es comercialitzen infinitat de productes d'electrònica de consum que incorporen elements i característiques procedents d'avanços en el sector de la robòtica mòbil. Per exemple, el conegut robot aspirador Roomba de l'empresa iRobot, el qual pertany al camp de la robòtica de servici, un dels més actius en el sector. També hi ha nombrosos sistemes robòtics autònoms en magatzems i plantes industrials. És el cas dels vehicles autoguiats (AGVs), els quals són capaços de conduir de forma totalment autònoma en entorns molt estructurats. A més de en la indústria i en l'electrònica de consum, dins el camp de l'automoció també existeixen dispositius que doten al vehicle de certa intel·ligència, la majoria de les vegades derivats d'avanços en robòtica mòbil. De fet, cada vegada amb més freqüència els vehicles incorporen sistemes avançats d'assistència al conductor (ADAS per les sigles en anglés), com ara control de navegació amb regulació automàtica de velocitat, assistent de canvi de carril i avançament, aparcament automàtic o avís de col·lisió, entre altres prestacions. No obstant això, malgrat tots els avanços segueixen existint problemes sense resoldre i que poden millorar-se. La col·lisió i la bolcada destaquen entre els accidents més comuns en vehicles amb conducció tant manual com autònoma. De fet, la dificultat de conduir en entorns desestructurats compartint l'espai amb altres agents mòbils, tals com cotxes o persones, fa quasi impossible garantitzar la conducció sense accidents. És per això que la recerca de tècniques per millorar la seguretat en vehicles intel·ligents, ja siguen de conducció autònoma o manual assistida, és un tema que sempre està en auge a la comunitat robòtica. La present tesi es centra en el disseny d'eines i tècniques de planificació i control de vehicles intel·ligents, per a la millora de la seguretat i el confort. La dissertació s'ha dividit en dues parts, la primera sobre conducció autònoma i la segona sobre conducció manual assistida. El principal nexe d'unió és l'ús de clotoides com a element de generació de trajectòries i detecció de col·lisions. Entre els problemes que es resolen destaquen l'evitació d'obstacles, l'evitació de bolcades i l'assistència avançada al conductor per evitar col·lisions amb vianants.
Girbés Juan, V. (2016). Clothoid-based Planning and Control in Intelligent Vehicles (Autonomous and Manual-Assisted Driving) [Tesis doctoral no publicada]. Universitat Politècnica de València. https://doi.org/10.4995/Thesis/10251/65072
TESIS

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Mathibela, Bonolo. "Situational awareness in autonomous vehicles : learning to read the road." Thesis, University of Oxford, 2014. http://ora.ox.ac.uk/objects/uuid:f9a788c4-1ce5-4733-be2b-ab3918ed079b.

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This thesis is concerned with the problem of situational awareness in autonomous vehicles. In this context, situational awareness refers to the ability of an autonomous vehicle to perceive the road layout ahead, interpret the implied semantics and gain an awareness of its surrounding - thus reading the road ahead. Autonomous vehicles require a high level of situational awareness in order to operate safely and efficiently in real-world dynamic environments. A system is therefore needed that is able to model the expected road layout in terms of semantics, both under normal and roadwork conditions. This thesis takes a three-pronged approach to this problem: Firstly, we consider reading the road surface. This is formulated in terms of probabilistic road marking classification and interpretation. We then derive the road boundaries using only a 2D laser and algorithms based on geometric priors from Highway Traffic Engineering principles. Secondly, we consider reading the road scene. Here, we formulate a roadwork scene recognition framework based on opponent colour vision in humans. Finally, we provide a data representation for situational awareness that unifies reading the road surface and reading the road scene. This thesis therefore frames situational awareness in autonomous vehicles in terms of both static and dynamic road semantics - and detailed formulations and algorithms are discussed. We test our algorithms on several benchmarking datasets collected using our autonomous vehicle on both rural and urban roads. The results illustrate that our road boundary estimation, road marking classification, and roadwork scene recognition frameworks allow autonomous vehicles to truly and meaningfully read the semantics of the road ahead, thus gaining a valuable sense of situational awareness even at challenging layouts, roadwork sites, and along unknown roadways.

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Batmanian, Saro, and Pasam Naga. "Control and balancing of a small vehicle with two wheels for autonomous driving." Thesis, KTH, Fordonsdynamik, 2019. http://urn.kb.se/resolve?urn=urn:nbn:se:kth:diva-265618.

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Control and balancing of an inverted pendulum has gained a lot of attention over the past few decades due to its unstable properties. This has become a great challenge for control engineers to verify and test the control theory. To control and balance an inverted pendulum, proportional integrated derivative (PID) method or linear quadratic regulator (LQR) method can be used through which a lot of simulations can be done using the represented theories.Since urban population is increasing at a very alarming rate, there is a need to discover new ways of transportation to meet the future challenges and demands. Scania has come up with a new conceptual bus called NXT which aims to develop a modular vehicle that should configure and re-configure themselves between different transportation tasks. NXT vehicle has front and rear drive modules which can be represented as single axle, two-wheeled vehicles which in-turn can be viewed as an inverted pendulum with a huge Center of Gravity. Controlling and balancing of the pod or drive module precisely and accurately is an interesting challenge since it is an unstable inverted pendulum with huge center of gravity (COG). This behaviour of the system has created a research question whether the module is controllable or not.Therefore this thesis focuses on the possibility of controlling the pod which is a two-wheeled inverted pendulum vehicle with a COG offset. Also, the thesis focuses on the construction, mod-elling, testing and validation of a down-scaled model, what sensors are needed to balance the pod precisely, how the sensors must be integrated with the system and how the pod can be controlled remotely from a certain distance by a human. The developed pod houses the technologies like sensors, BLDC motor controllers, hoverboard, Arduino board and Bluetooth transmitters.The Master Thesis starts by presenting an introduction to the inverted pendulum theories, Scania NXT project, information about the research methods, thesis outline and structure . It continues by describing related literature about the inverted pendulums, segways, hoverboards, motor controllers and Arduino boards. Afterwards, the process of deriving a mathematical model, together with simulation in Matlab, Simulink and Simscape is described. Later, construction of the pod is made and lot of effort is put to run the pod. Since the pod needs to be controlled remotely by a human, a remote controlled systemis implemented via mobile phone using an app and finally the thesis is finished with a conclusion and ideas for future work.
Reglering och balancering av en inverterad pendel har väckt stor uppmärksamhet över de senaste decennierna på grund av dess instabila egenskaper. Det har skapat stora utmaningar för regleringenjörer eftersom området tillåter test och verifikation av diverese lösningar. För reglering och balansering av en inverterad pendel, så kan en regulator med proportionell, integral och derivat (PID) konstanter eller en linjär kvadratisk regulator (LQR) användas tillsammans med simuleringar för att bekräfta teorin.I och med att stadsbefolkningen ökar i mycket hög takt, så uppstår behovet av att uppfinna nya transportmedel för att lösa framtida utmaningar och krav. Scania har tagit fram en ny konceptbuss som heter NXT, med målet att utveckla ett modulfordon som kommer att konfigurera och rekonfigurera sig själva mellan olika transportuppgifter. NXT-fordonet har fram- och bakdriv-moduler som kan representeras som enaxlade tvåhjuliga fordon, vilka i sin tur kan betraktas som en inverterad pendel med en stor massa. Att reglera och balansera drivmodulen på ett noggrant sätt är en utmaning eftersom det är ett mycket instabilt system med enorm massa och en okänd tyngdpunkt. Systemets beteende har skapat en forskningsfråga om modulen är reglerbar eller inte.Denna uppsats fokuserar därmed på möjligheterna att kunna reglera drivmodulen samt vilka begränsningar det finns. Uppsatsen fokuserar också på konstruktion, modellering, testning och validering av en nedskalad modell, vilka sensorer som krävs för att balansera drivmodulem, samt hur sensorerna måste integreras med systemet för att kunna fjärstyra fordonet från ett visst avstånd. Utveckingen av en sådan nedskalad modell berör olika områden såsom sensorer, BLDC-motorstyrenheter, hoverboard balanserings scootrar, Arduino kretskort och Bluetooth-sändare/mottagare.Uppsasten börjar med en introduction om olika inverterade pendel teorier, Scania NXT project, forskningsmetoder, en översikt och övergripande struktur. Vidare fortsätter beskrivining av relaterade litteratur om inverterade pendel, Segway, hoverboard, borstlösa motor styrenheter och Arduino kretskort. Senare fortsätter processen för att härleda matematiska modeller som beskirver systemet, tillsammans med simuleringar i Matlab, Simulink och Simscape. Därefter beskrivs konstruktionen av en nedskalad modell av drivmodulen och beskrivning av nödvändiga processer för att få hårdvara och mjkukvara att fungera ihop. Då fordonen ska ha möjlighet att fjärrstyras, så implementerades en bluetooth enhet tillsammans med en programmerbar mobil applikation. Slutligen avlutas uppsatsen med resultat, slutsats och diskussioner och förslag till framtida arbeten.

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Patil, Mayur. "Test Scenario Development Process and Software-in-the-Loop Testing for Automated Driving Systems." The Ohio State University, 2019. http://rave.ohiolink.edu/etdc/view?acc_num=osu1574794282029419.

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Kindstedt, Mathias. "Exploring the Training Data for Online Learning of Autonomous Driving in a Simulated Environment." Thesis, Linköpings universitet, Datorseende, 2020. http://urn.kb.se/resolve?urn=urn:nbn:se:liu:diva-166881.

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The field of autonomous driving is as active as it has ever been, but the reality where an autonomous vehicle can drive on all roads is currently decades away. Instead, using an on-the-fly learning method, such as qHebb learning, a system can,after some demonstration, learn the appearance of any road and take over the steering wheel. By training in a simulator, the amount and variation of training can increase substantially, however, an on-rails auto-pilot does not sufficiently populate the learning space of such a model. This study aims to explore concepts that can increase the variance in the training data whilst the vehicle trains online. Three computationally light concepts are proposed that each manages to result in a model that can navigate through a simple environment, thus performing better than a model trained solely on the auto-pilot. The most noteworthy approach uses multiple thresholds to detect when the vehicle deviates too much and replicates the action of a human correcting its trajectory. After training on less than 300 frames, a vehicle successfully completed the full test environment using this method.
Autonom körning är ett aktivt område inom både industrin och forskarvärlden, men ännu är en verklighet där förarlösa fordon kan ta sig fram oavsett väg, decennier bort. Istället kan man genom att använda en adaptiv inlärningsmodell som qHebb learning uppnå ett system som kan ta sig fram självmant på alla vägar, efter en initial inlärningsperiod. Genom att använda en simulator skulle möjligheten att träna en sådan modell öka avsevärt, likaså variationen av vägtyper och det omgivande landskapet. Dock klarar inte en enformig autopilot att fylla modellens lärningsrymd. Detta arbete stävar efter att utforska koncept som kan öka variationen på träningsdatan, medan fordonet kör. Tre prestandalätta metoder presenteras som alla överträffar autopiloten och resulterar i en modell som lärt sig att följa en väg längs kurvor och raksträckor. Det främsta konceptet använder sig av två tröskelvärden för att korrigera fordonets styrning då den avviker för mycket från den korrekta rutten. Efter träning på färre än 300 bilder lyckas denna metod slutföra alla testsegment utan kollision.

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Edvardsson, Felicia, and Therése Warberg. "Konceptuell utveckling av interiören hos en framtida fullt autonom bil." Thesis, Högskolan i Skövde, Institutionen för ingenjörsvetenskap, 2016. http://urn.kb.se/resolve?urn=urn:nbn:se:his:diva-12794.

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Målet med examensarbetet har varit att samla information åt ett tekniskt konsultföretag för att öka deras kunskap om autonoma system och fordonskommunikation. Statusen på arbetet kring dessa aktiva säkerhetssystem hos olika aktörer och hur systemen implementeras i dagens och framtidens fordon har undersökts genom omfattande litteraturstudier, intervjuer och marknadsanalyser. De autonoma systemen kan samla information från omgivningen genom sensorer och bidra till ett jämnare trafikflöde, ökad säkerhet, lättare bilar och bättre miljö. Genom fordonskommunikationen kan fordon kommunicera med varandra samt infrastrukturen och garantera en säker bilfärd. År 2030 utgörs innerstaden av autonom, elektrifierad kollektivtrafik för att transportera människor på begäran, samtidigt som personbilar till viss del förbjuds. Potentiella behov för människan i en fullt autonom bil har identifierats och diverse produktutvecklingsmetoder har tillämpats för att utforma två konceptuella lösningar för en framtida bilinteriör. Lösningarna visar interaktionen mellan människa och system eftersom underhållning och bekvämlighet blir viktigt i en fullt autonom bil. Respektive lösning är statsägd och rymmer fyra passagerare. I lösningarna är sittplatserna placerade på ett sätt som underlättar kommunikation mellan passagerarna. Passagerarna kan underhållas eller informeras individuellt eller gemensamt via text, ljud och bild.
The goal with this thesis project has been to collect information for a technical consulting company in order to increase their knowledge about autonomous systems and vehicular communication. The status of how various operators work with active safety systems and how the systems are implemented in current and future vehicles has been investigated through extensive literature studies, interviews and market research. The autonomous systems can collect information from the surrounding through sensors and contribute to better traffic efficiency, increased safety, lighter cars and a better environment. Through vehicle communication, the vehicle can communicate with each other in order to guarantee a safe ride. In 2030 the inner city constitutes of autonomous, electrified public transport to transport people on demand, meanwhile private cars are prohibited. Potential needs for the human in a fully, autonomous car has been identified and various product development methods has been applied in order to develop two conceptual solutions for a future car interior. The solutions show the interaction between human and system since entertainment and comfort becomes important in a fully, autonomous car. Each solution is state-owned and holds four passengers. In the solutions, the seats are placed in regard to facilitate communication between the passengers. The passengers can be entertained or informed individually or collectively by text, sound and images.

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Trask, Simon J. "Systems and Safety Engineering in Hybrid-Electric and Semi-Autonomous Vehicles." The Ohio State University, 2019. http://rave.ohiolink.edu/etdc/view?acc_num=osu1555521147257702.

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Hauer, Florian [Verfasser], Alexander [Akademischer Betreuer] Pretschner, Alexander [Gutachter] Pretschner, and Markus [Gutachter] Lienkamp. "On Scenario-Based Testing of Automated and Autonomous Driving Systems / Florian Hauer ; Gutachter: Alexander Pretschner, Markus Lienkamp ; Betreuer: Alexander Pretschner." München : Universitätsbibliothek der TU München, 2021. http://d-nb.info/1238781713/34.

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Magnusson, Filip. "Evaluating Deep Learning Algorithms for Steering an Autonomous Vehicle." Thesis, Linköpings universitet, Programvara och system, 2018. http://urn.kb.se/resolve?urn=urn:nbn:se:liu:diva-153450.

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With self-driving cars on the horizon, vehicle autonomy and its problems is a hot topic. In this study we are using convolutional neural networks to make a robot car avoid obstacles. The robot car has a monocular camera, and our approach is to use the images taken by the camera as input, and then output a steering command. Using this method the car is to avoid any object in front of it. In order to lower the amount of training data we use models that are pretrained on ImageNet, a large image database containing millions of images. The model are then trained on our own dataset, which contains of images taken directly by the robot car while driving around. The images are then labeled with the steering command used while taking the image. While training we experiment with using different amounts of frozen layers. A frozen layer is a layer that has been pretrained on ImageNet, but are not trained on our dataset. The Xception, MobileNet and VGG16 architectures are tested and compared to each other. We find that a lower amount of frozen layer produces better results, and our best model, which used the Xception architecture, achieved 81.19% accuracy on our test set. During a qualitative test the car avoid collisions 78.57% of the time.

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Algers, Björn. "Stereo Camera Calibration Accuracy in Real-time Car Angles Estimation for Vision Driver Assistance and Autonomous Driving." Thesis, Umeå universitet, Institutionen för fysik, 2018. http://urn.kb.se/resolve?urn=urn:nbn:se:umu:diva-149443.

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The automotive safety company Veoneer are producers of high end driver visual assistance systems, but the knowledge about the absolute accuracy of their dynamic calibration algorithms that estimate the vehicle’s orientation is limited. In this thesis, a novel measurement system is proposed to be used in gathering reference data of a vehicle’s orientation as it is in motion, more specifically the pitch and roll angle of the vehicle. Focus has been to estimate how the uncertainty of the measurement system is affected by errors introduced during its construction, and to evaluate its potential in being a viable tool in gathering reference data for algorithm performance evaluation. The system consisted of three laser distance sensors mounted on the body of the vehicle, and a range of data acquisition sequences with different perturbations were performed by driving along a stretch of road in Linköping with weights loaded in the vehicle. The reference data were compared to camera system data where the bias of the calculated angles were estimated, along with the dynamic behaviour of the camera system algorithms. The experimental results showed that the accuracy of the system exceeded 0.1 degrees for both pitch and roll, but no conclusions about the bias of the algorithms could be drawn as there were systematic errors present in the measurements.
Bilsäkerhetsföretaget Veoneer är utvecklare av avancerade kamerasystem inom förarassistans, men kunskapen om den absoluta noggrannheten i deras dynamiska kalibreringsalgoritmer som skattar fordonets orientering är begränsad. I denna avhandling utvecklas och testas ett nytt mätsystem för att samla in referensdata av ett fordons orientering när det är i rörelse, mer specifikt dess pitchvinkel och rollvinkel. Fokus har legat på att skatta hur osäkerheten i mätsystemet påverkas av fel som introducerats vid dess konstruktion, samt att utreda dess potential när det kommer till att vara ett gångbart alternativ för att samla in referensdata för evaluering av prestandan hos algoritmerna. Systemet bestod av tre laseravståndssensorer monterade på fordonets kaross. En rad mätförsök utfördes med olika störningar introducerade genom att köra längs en vägsträcka i Linköping med vikter lastade i fordonet. Det insamlade referensdatat jämfördes med data från kamerasystemet där bias hos de framräknade vinklarna skattades, samt att de dynamiska egenskaperna kamerasystemets algoritmer utvärderades. Resultaten från mätförsöken visade på att noggrannheten i mätsystemet översteg 0.1 grader för både pitchvinklarna och rollvinklarna, men några slutsatser kring eventuell bias hos algoritmerna kunde ej dras då systematiska fel uppstått i mätresultaten.

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Miller, Erik. "Implementation of a Scale Semi-Autonomous Platoon to Test Control Theory Attacks." DigitalCommons@CalPoly, 2019. https://digitalcommons.calpoly.edu/theses/2057.

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With all the advancements in autonomous and connected cars, there is a developing body of research around the security and robustness of driving automation systems. Attacks and mitigations for said attacks have been explored, but almost always solely in software simulations. For this thesis, I led a team to build the foundation for an open source platoon of scale semi-autonomous vehicles. This work will enable future research into implementing theoretical attacks and mitigations. Our 1/10 scale car leverages an Nvidia Jetson, embedded microcontroller, and sensors. The Jetson manages the computer vision, networking, control logic, and overall system control; the embedded microcontroller directly controls the car. A lidar module is responsible for recording distance to the preceding car, and an inertial measurement unit records the velocity of the car itself. I wrote the software for the networking, interprocess, and serial communications, as well as the control logic and system control.

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Mohan, Naveen. "Architecting Safe Automated Driving with Legacy Platforms." Licentiate thesis, KTH, Mekatronik, 2018. http://urn.kb.se/resolve?urn=urn:nbn:se:kth:diva-223687.

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Modern vehicles have electrical architectures whose complexity grows year after year due to feature growth corresponding to customer expectations. The latest of the expectations, automation of the dynamic driving task however, is poised to bring about some of the largest changes seen so far. In one fell swoop, not only does required functionality for automated driving drastically increase the system complexity, it also removes the fall-back of the human driver who is usually relied upon to handle unanticipated failures after the fact. The need to architect thus requires a greater rigour than ever before, to maintain the level of safety that has been associated with the automotive industry. The work that is part of this thesis has been conducted, in close collaboration with our industrial partner Scania CV AB, within the Vinnova FFI funded project ARCHER. This thesis aims to provide a methodology for architecting during the concept phase of development, using industrial practices and principles including those from safety standards such as ISO 26262. The main contributions of the thesis are in two areas. The first area i.e. Part A contributes, (i) an analysis of the challenges of architecting automated driving, and serves as a motivation for the approach taken in the rest of this thesis, i.e. Part B where the contributions include, (ii) a definition of a viewpoint for functional safety according to the definitions of ISO 42010, (iii) a method to systematically extract information from legacy components and (iv) a process to use legacy information and architect in the presence of uncertainty to provide a work product, the Preliminary Architectural Assumptions (PAA), as required by ISO 26262. The contributions of Part B together comprise a methodology to architect the PAA. A significant challenge in working with the industry is finding the right fit between idealized principles and practical utility. The methodology in Part B has been judged fit for purpose by different parts of the organization at Scania and multiple case studies have been conducted to assess its usefulness in collaboration with senior architects. The methodology was found to be conducive in both, generating the PAA of a quality that was deemed suitable to the organization and, to find inadequacies in the architecture that had not been found earlier using the previous non-systematic methods. The benefits have led to a commissioning of a prototype tool to support the methodology that has begun to be used in projects related to automation at Scania. The methodology will be refined as the projects progress towards completion using the experiences gained. A further impact of the work is seen in two patent filings that have originated from work on the case studies in Part B. Emanating from needs discovered during the application of the methods, these filed patents (with no prior publications) outline the future directions of research into reference architectures augmented with safety policies, that are safe in the presence of detectable faults and failures. To aid verification of these ideas, work has begun on identifying critical scenarios and their elements in automated driving, and a flexible simulation platform is being designed and developed at KTH to test the chosen critical scenarios.
Efterfrågan på nya funktioner leder till en ständigt ökande komplexitet i moderna fordon, speciellt i de inbyggda datorsystemen. Införande av autonoma fordon utgör inte bara det mest aktuella exemplet på detta, utan medför också en av de största förändringar som fordonsbranschen sett. Föraren, som ”back-up” för att hantera oväntade situationer och fel, finns inte längre där vid höggradig automation, och motsvarande funktioner måste realiseras i de inbyggda system vilket ger en drastisk komplexitetsökning. Detta ställer systemarkitekter för stora utmaningar för att se till att nuvarande nivå av funktionssäkerhet bibehålls. Detta forskningsarbete har utförts i nära samarbete med Scania CV AB i det Vinnova (FFI)-finansierade projektet ARCHER. Denna licentiatavhandling har som mål att ta fram en metodik för konceptutveckling av arkitekturer, förankrat i industriell praxis och principer, omfattande bl.a. de som beskrivs i funktionssäkerhetsstandards som ISO 26262. Avhandlingen presenterar resultat inom två områden. Det första området, del A, redovisar, (i) en analys av utmaningar inom arkitekturutveckling för autonoma fordon, vilket också ger en motivering för resterande del av avhandlingen. Det andra området, del B, redovisar, (ii) en definition av en ”perspektivmodell” (en s.k. ”viewpoint” enligt ISO 42010) för funktionssäkerhet, (iii) en metod för att systematiskt utvinna information från existerande komponenter, och (iv) en process som tar fram en arbetsprodukt för ISO 26262 – Preliminära Arkitektur-Antaganden (PAA). Denna process använder sig av information från existerande komponenter – resultat (iii) och förenklar hantering av avsaknad/osäker information under arkitekturarbetet. Resultaten från del B utgör tillsammans en metodik för att ta fram en PAA. En utmaning i forskning är att finna en balans mellan idealisering och praktisk tillämpbarhet. Metodiken i del B har utvärderats i flertalet industriella fallstudier på Scania i samverkan med seniora arkitekter från industrin, och har av dessa bedömts som relevant och praktiskt tillämpningsbar. Erfarenheterna visar att metodiken stödjer framtagandet av PAA’s av lämplig kvalitet och ger ett systematiskt sätt att hantera osäkerhet under arkitekturutvecklingen. Specifikt så gjorde metoden det möjligt att identifiera komponent-felmoder där arkitekturen inte var tillräcklig för åstadkomma önskad riskreducering, begränsningar som inte hade upptäckts med tidigare metoder. Ett prototypverktyg för att stödja metodiken har utvecklats och börjat användas på Scania i projekt relaterade till autonoma fordon. Metodiken kommer sannolikt att kunna förfinas ytterligare när dessa projekt går mot sitt slut och mer erfarenheter finns tillgängliga. Arbetet i del B har vidare lett till två patentansökningar avseende koncept som framkommit genom fallstudierna. Dessa koncept relaterar till referensarkitekturer som utökats med policies för personsäkerhet (Eng. ”safety”) för att hantera detekterbara felfall, och pekar ut en riktning för framtida forskning. För att stödja verifiering av dessa koncept har arbete inletts för att identifiera kritiska scenarios för autonom körning. En flexibel simuleringsplattform håller också på att designas för att kunna testa kritiska scenarios.
Vinnova-FFI funded Project ARCHER

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Baaz, Hampus. "NAVIGATION AND PLANNED MOVEMENT OF AN UNMANNED BICYCLE." Thesis, Mälardalens högskola, Akademin för innovation, design och teknik, 2020. http://urn.kb.se/resolve?urn=urn:nbn:se:mdh:diva-48969.

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A conventional bicycle is a stable system given adequate forward velocity. However, the velocity region of stability is limited and depends on the geometric parameters of the bicycle. An autonomous bicycle is just not about maintaining the balance but also controlling where the bicycle is heading. Following paths has been accomplished with bicycles and motorcycles in simulation for a while. Car-like vehicles have followed paths in the real world but few bicycles or motorcycles have done so. The goal of this work is to follow a planned path using a physical bicycle without overcoming the dynamic limitations of the bicycle. Using an iterative design process, controllers for direction and position are developed and improved. Kinematic models are also compared in their ability to simulate the bicycle movement and how controllers in simulation translate to outdoors driving. The result shows that the bicycle can follow a turning path on a residential road without human interaction and that some simulation behaviours do not translate to the real world.

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Björsell, Kajsa, and Josephine Hedman. "Future impacts of self-driving vehicles : A case study on the supply chain of e-commerce to identify important factors for the transport administrators of Sweden." Thesis, Luleå tekniska universitet, Institutionen för ekonomi, teknik och samhälle, 2018. http://urn.kb.se/resolve?urn=urn:nbn:se:ltu:diva-69597.

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The rapid pace of the development of the transport and vehicle industry in combination with megatrends such as digitalization, automation, and electrification can have huge effects on how transport planning and the society evolves. In order to meet goals such as increased traffic safety,improved environment, and reduced congestions a lot needs to be done. Two tools expected to be of significance when creating a more transport efficient society are automation and digitalization, whereby self-driving vehicles (SDVs) is an important area. The race towards fully autonomous vehicles is ongoing and scholars argue that the implementation of SDVs can be faster within freight transportation than passenger transportation. Higher costsavings, as well as decreasing availability on the labor market, are two arguments for why freighttransportation can be autonomous faster. Depending on how ambitious or slow the policy and planning are as well as the development of shared solutions, different future scenarios, as well as penetration rates of SDVs, can come through. One certain trend argued to continue to grow as well as having an impact on the development of SDVs is the rapid growth of e-commerce. This study addresses the uncertainty concerning SDVs from a transport administrator’s perspective by identifying important factors for Trafikverket regarding the implementation of SDVs within freight transportation. Four already developed future plausible scenarios for the year 2030 lay the ground for this study and a case study concerning the supply chain of e-commerce in Sweden is used to delimitate the study. Interviews with distributors were held to conduct the case and two workshops with experts within the transport sector, academia, and authorities, as well as a meeting with a reference group with representatives from Trafikverket were held to collect data. In the workshops, the experts identified trends and system impacts within the four future scenarios. A key insight gained in this study is that SDVs is an area with a lot of insecurity and thus, it needs to be investigated further. One solution to study the subject further is to implement restricted lanes for SDVs to test the technique properly. The results of this study clearly show that even though SDVs is a topical issue, it should not be studied as a solitary subject but rather in a larger context together with other significant factors. Nighttime transports and deliveries, platooning, and electricroads and electric vehicles are three factors that are likely to be implemented very soon and should, therefore, be studied together with SDVs. Moreover, the result from the workshops implies that there will be an increased number of vehicles as well as vehicle kilometers within the distribution of e-commerce packages in the future. In addition, the experts expect SDVs to be present in the year 2030, but the number of SDVs depend on multiple factors.

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ABUKMEIL, MOHANAD. "UNSUPERVISED GENERATIVE MODELS FOR DATA ANALYSIS AND EXPLAINABLE ARTIFICIAL INTELLIGENCE." Doctoral thesis, Università degli Studi di Milano, 2022. http://hdl.handle.net/2434/889159.

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For more than a century, the methods of learning representation and the exploration of the intrinsic structures of data have developed remarkably and currently include supervised, semi-supervised, and unsupervised methods. However, recent years have witnessed the flourishing of big data, where typical dataset dimensions are high, and the data can come in messy, missing, incomplete, unlabeled, or corrupted forms. Consequently, discovering and learning the hidden structure buried inside such data becomes highly challenging. From this perspective, latent data analysis and dimensionality reduction play a substantial role in decomposing the exploratory factors and learning the hidden structures of data, which encompasses the significant features that characterize the categories and trends among data samples in an ordered manner. That is by extracting patterns, differentiating trends, and testing hypotheses to identify anomalies, learning compact knowledge, and performing many different machine learning (ML) tasks such as classification, detection, and prediction. Unsupervised generative learning (UGL) methods are a class of ML characterized by their possibility of analyzing and decomposing latent data, reducing dimensionality, visualizing the manifold of data, and learning representations with limited levels of predefined labels and prior assumptions. Furthermore, explainable artificial intelligence (XAI) is an emerging field of ML that deals with explaining the decisions and behaviors of learned models. XAI is also associated with UGL models to explain the hidden structure of data, and to explain the learned representations of ML models. However, the current UGL models lack large-scale generalizability and explainability in the testing stage, which leads to restricting their potential in ML and XAI applications. To overcome the aforementioned limitations, this thesis proposes innovative methods that integrate UGL and XAI to enable data factorization and dimensionality reduction to improve the generalizability of the learned ML models. Moreover, the proposed methods enable visual explainability in modern applications as anomaly detection and autonomous driving systems. The main research contributions are listed as follows: • A novel overview of UGL models including blind source separation (BSS), manifold learning (MfL), and neural networks (NNs). Also, the overview considers open issues and challenges among each UGL method. • An innovative method to identify the dimensions of the compact feature space via a generalized rank in the application of image dimensionality reduction. • An innovative method to hierarchically reduce and visualize the manifold of data to improve the generalizability in limited data learning scenarios, and computational complexity reduction applications. • An original method to visually explain autoencoders by reconstructing an attention map in the application of anomaly detection and explainable autonomous driving systems. The novel methods introduced in this thesis are benchmarked on publicly available datasets, and they outperformed the state-of-the-art methods considering different evaluation metrics. Furthermore, superior results were obtained with respect to the state-of-the-art to confirm the feasibility of the proposed methodologies concerning the computational complexity, availability of learning data, model explainability, and high data reconstruction accuracy.

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Bartoli, Giacomo. "Edge AI: Deep Learning techniques for Computer Vision applied to embedded systems." Master's thesis, Alma Mater Studiorum - Università di Bologna, 2018. http://amslaurea.unibo.it/16820/.

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In the last decade, Machine Learning techniques have been used in different fields, ranging from finance to healthcare and even marketing. Amongst all these techniques, the ones adopting a Deep Learning approach were revealed to outperform humans in tasks such as object detection, image classification and speech recognition. This thesis introduces the concept of Edge AI: that is the possibility to build learning models capable of making inference locally, without any dependence on expensive servers or cloud services. A first case study we consider is based on the Google AIY Vision Kit, an intelligent camera equipped with a graphic board to optimize Computer Vision algorithms. Then, we test the performances of CORe50, a dataset for continuous object recognition, on embedded systems. The techniques developed in these chapters will be finally used to solve a challenge within the Audi Autonomous Driving Cup 2018, where a mobile car equipped with a camera, sensors and a graphic board must recognize pedestrians and stop before hitting them.

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Patel, Raj Haresh. "Autonomous cars' coordination among legacy vehicles applied to safe braking." Electronic Thesis or Diss., Sorbonne université, 2018. http://www.theses.fr/2018SORUS468.

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Le comportement d'un véhicule autonome peut être affecté par divers facteurs internes tels que défaillance du système de bord, capteur, etc., ou par des facteurs externes tels que manœuvres risquées de la part de voisins immédiats menaçant une collision, des changements brusques de l'état des routes, etc. Cela peut entraîner une défaillance de la manœuvre de coordination, telle que le croisement de plusieurs véhicules à une intersection. Dans de telles situations, lorsque les conditions changent de manière dynamique et que la condition de fonctionnement nominale est violée par des influences internes ou externes, un véhicule autonome doit avoir la capacité d'atteindre la condition de risque minimal. Arrêter le véhicule est l’un des moyens d’atteindre un niveau de risque minimal. La thèse introduit un algorithme d'arrêt sécurisé qui génère des commandes pour véhicules autonomes en tenant compte de la présence de véhicules traditionnels. Un algorithme basé sur un modèle de contrôle prédictif est proposé, qui résiste aux erreurs provenant de la communication, la localisation, la mise en œuvre du contrôle et à la disparité des modèles. Les collisions évitées et la gêne ressentie par le conducteur sont deux paramètres d'évaluation. Les simulations montrent que le contrôleur robuste sous l'influence d'erreurs peut fonctionner aussi bien que le contrôleur non-robuste en l'absence d'erreurs
The behaviour of an autonomous vehicle can be impacted by various internal factors like onboard system failure, sensor failure, etc. or by external factors like risky maneuvers by immediate neighbors threatening a collision, sudden change in road conditions, etc. This can result in a failure of coordination maneuver like multi-vehicle intersection clearance. In such situations when conditions dynamically change and the nominal operational condition is violated by internal or external influences, an autonomous vehicle must have the capability to reach the minimal risk condition. Bringing the vehicle to a halt is one of the ways to achieve minimal risk condition. This thesis introduces a safe stop algorithm which generates controls for multiple autonomous vehicles considering the presence of legacy manually driven vehicles on the road. A Model Predictive Control based algorithm is proposed which is robust to errors in communication, localization, control implementation, and model mismatch. Collisions avoided and discomfort faced by the driver are two evaluation parameters. Simulations show that the robust controller under the influence of errors can perform as well as the non-robust controller in the absence of these errors

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Lindelöf, Gabriel Trim Olof. "Moraliska bedömningar av autonoma systems beslut." Thesis, Linköpings universitet, Institutionen för datavetenskap, 2020. http://urn.kb.se/resolve?urn=urn:nbn:se:liu:diva-166543.

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Samhällsutvecklingen går i en riktning där människor arbetar i allt närmare samarbete med artificiella agenter. För att detta samarbete ska vara på användarens villkor är det viktigt att förstå hur människor uppfattar och förhåller sig till dessa system. Hur dessa agenter bedöms moraliskt är en komponent i denna förståelse. Malle m.fl. (2015) utförde en av de första studierna kring hur normer och skuld appliceras på människa respektive robot. I samma artikel efterfrågades mer forskning kring vilka faktorer hos agenter som påverkar de moraliska bedömningarna. Föreliggande studie tog avstamp i denna frågeställning och avsåg att undersöka hur moralisk godtagbarhet och skuldbeläggning skiljde sig beroende på om agenten var en person, en humanoid robot eller ett autonomt intelligent system utan kropp (AIS). Ett mellangrupps-experiment (N = 119) användes för att undersöka hur agenterna bedömdes för sina beslut i tre olika moraliska dilemman. Deltagares rättfärdigaden bakom bedömningar samt medveten hållning utforskades som förklaringsmodell av skillnader. Medveten hållning avser Dennetts (1971) teori kring huruvida en agent förstås utifrån mentala egenskaper. Resultaten visade att person och robot erhöll liknande godtagbarhet för sina beslut medan AIS fick signifikant lägre snitt. Graden skuld som tillskrevs skiljde sig inte signifikant mellan agenterna. Analysen av deltagares rättfärdiganden gav indikationer på att skuldbedömningarna av de artificiella agenterna inte grundade sig i sådan information som antagits ligga till grund för denna typ av bedömningar. Flera rättfärdiganden påpekade också att det var någon annan än de artificiella agenterna som bar skulden för besluten. Vidare analyser indikerade på att deltagare höll medveten hållning mot person i störst utsträckning följt av robot och sedan AIS. Studien väcker frågor kring huruvida skuld som fenomen går att applicera på artificiella agenter och i vilken utsträckning distribuerad skuld är en faktor när artificiella agenter bedöms.

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Tekin, Mim Kemal. "Vehicle Path Prediction Using Recurrent Neural Network." Thesis, Linköpings universitet, Statistik och maskininlärning, 2020. http://urn.kb.se/resolve?urn=urn:nbn:se:liu:diva-166134.

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Vehicle Path Prediction can be used to support Advanced Driver Assistance Systems (ADAS) that covers different technologies like Autonomous Braking System, Adaptive Cruise Control, etc. In this thesis, the vehicle’s future path, parameterized as 5 coordinates along the path, is predicted by using only visual data collected by a front vision sensor. This approach provides cheaper application opportunities without using different sensors. The predictions are done by deep convolutional neural networks (CNN) and the goal of the project is to use recurrent neural networks (RNN) and to investigate the benefits of using reccurence to the task. Two different approaches are used for the models. The first approach is a single-frame approach that makes predictions by using only one image frame as input and predicts the future location points of the car. The single-frame approach is the baseline model. The second approach is a sequential approach that enables the network the usage of historical information of previous image frames in order to predict the vehicle’s future path for the current frame. With this approach, the effect of using recurrence is investigated. Moreover, uncertainty is important for the model reliability. Having a small uncertainty in most of the predictions or having a high uncertainty in unfamiliar situations for the model will increase success of the model. In this project, the uncertainty estimation approach is based on capturing the uncertainty by following a method that allows to work on deep learning models. The uncertainty approach uses the same models that are defined by the first two approaches. Finally, the evaluation of the approaches are done by the mean absolute error and defining two different reasonable tolerance levels for the distance between the prediction path and the ground truth path. The difference between two tolerance levels is that the first one is a strict tolerance level and the the second one is a more relaxed tolerance level. When using strict tolerance level based on distances on test data, 36% of the predictions are accepted for single-frame model, 48% for the sequential model, 27% and 13% are accepted for single-frame and sequential models of uncertainty models. When using relaxed tolerance level on test data, 60% of the predictions are accepted by single-frame model, 67% for the sequential model, 65% and 53% are accepted for single-frame and sequential models of uncertainty models. Furthermore, by using stored information for each sequence, the methods are evaluated for different conditions such as day/night, road type and road cover. As a result, the sequential model outperforms in the majority of the evaluation results.

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Elhassan, Amro. "Autonomous driving system for reversing an articulated vehicle." Thesis, KTH, Reglerteknik, 2015. http://urn.kb.se/resolve?urn=urn:nbn:se:kth:diva-175373.

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Articulated vehicles are widely used in the economically vital cargo industry as they provide a greater maneuverability than their rigid counterparts. Hence, autonomous driving systems for articulated vehicles have become the subject of intense research in the robotic community. This thesis analyzes the reverse motion of an articulated vehicle, namely a tractor-trailer with one on-axle hitched semitrailer, and develops a full autonomous driving system that enables reverse parking in the presence of static obstacles. The motion controller used in the autonomous driving system is based on a two-level feedback control system, with a path stabilization controller in the first level and a hitch angle controller in the second level. The path planner used is a modified RRT planner where the Dubins path has been incorporated in order to enable the planning towards a goal pose rather than merely a goal region. The modifications made have resulted in several improvements, such as more accurate planning and higher computational efficiency. Using a 1:32 scale remote controlled tractor-trailer, and a Qualisys motion capture system for pose estimation, the autonomous driving system was successfully implemented and validated.

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Djikic, Addi. "Segmentation and Depth Estimation of Urban Road Using Monocular Camera and Convolutional Neural Networks." Thesis, KTH, Robotik, perception och lärande, RPL, 2018. http://urn.kb.se/resolve?urn=urn:nbn:se:kth:diva-235496.

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Deep learning for safe autonomous transport is rapidly emerging. Fast and robust perception for autonomous vehicles will be crucial for future navigation in urban areas with high traffic and human interplay. Previous work focuses on extracting full image depth maps, or finding specific road features such as lanes. However, in urban environments lanes are not always present, and sensors such as LiDAR with 3D point clouds provide a quite sparse depth perception of road with demanding algorithmic approaches. In this thesis we derive a novel convolutional neural network that we call AutoNet. It is designed as an encoder-decoder network for pixel-wise depth estimation of an urban drivable free-space road, using only a monocular camera, and handled as a supervised regression problem. AutoNet is also constructed as a classification network to solely classify and segment the drivable free-space in real- time with monocular vision, handled as a supervised classification problem, which shows to be a simpler and more robust solution than the regression approach. We also implement the state of the art neural network ENet for comparison, which is designed for fast real-time semantic segmentation and fast inference speed. The evaluation shows that AutoNet outperforms ENet for every performance metrics, but shows to be slower in terms of frame rate. However, optimization techniques are proposed for future work, on how to advance the frame rate of the network while still maintaining the robustness and performance. All the training and evaluation is done on the Cityscapes dataset. New ground truth labels for road depth perception are created for training with a novel approach of fusing pre-computed depth maps with semantic labels. Data collection with a Scania vehicle is conducted, mounted with a monocular camera to test the final derived models. The proposed AutoNet shows promising state of the art performance in regards to road depth estimation as well as road classification.
Deep learning för säkra autonoma transportsystem framträder mer och mer inom forskning och utveckling. Snabb och robust uppfattning om miljön för autonoma fordon kommer att vara avgörande för framtida navigering inom stadsområden med stor trafiksampel. I denna avhandling härleder vi en ny form av ett neuralt nätverk som vi kallar AutoNet. Där nätverket är designat som en autoencoder för pixelvis djupskattning av den fria körbara vägytan för stadsområden, där nätverket endast använder sig av en monokulär kamera och dess bilder. Det föreslagna nätverket för djupskattning hanteras som ett regressions problem. AutoNet är även konstruerad som ett klassificeringsnätverk som endast ska klassificera och segmentera den körbara vägytan i realtid med monokulärt seende. Där detta är hanterat som ett övervakande klassificerings problem, som även visar sig vara en mer simpel och mer robust lösning för att hitta vägyta i stadsområden. Vi implementerar även ett av de främsta neurala nätverken ENet för jämförelse. ENet är utformat för snabb semantisk segmentering i realtid, med hög prediktions- hastighet. Evalueringen av nätverken visar att AutoNet utklassar ENet i varje prestandamätning för noggrannhet, men visar sig vara långsammare med avseende på antal bilder per sekund. Olika optimeringslösningar föreslås för framtida arbete, för hur man ökar nätverk-modelens bildhastighet samtidigt som man behåller robustheten.All träning och utvärdering görs på Cityscapes dataset. Ny data för träning samt evaluering för djupskattningen för väg skapas med ett nytt tillvägagångssätt, genom att kombinera förberäknade djupkartor med semantiska etiketter för väg. Datainsamling med ett Scania-fordon utförs även, monterad med en monoculär kamera för att testa den slutgiltiga härleda modellen. Det föreslagna nätverket AutoNet visar sig vara en lovande topp-presterande modell i fråga om djupuppskattning för väg samt vägklassificering för stadsområden.

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Pieger, Matúš. "Sledování řidiče." Master's thesis, Vysoké učení technické v Brně. Fakulta elektrotechniky a komunikačních technologií, 2021. http://www.nusl.cz/ntk/nusl-442532.

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This master’s thesis deals with the design of systems for data collection which describe the driver’s behaviour in a car. This data is used to detect risky behaviour that the driver may commit due to inattention caused by the use of either lower or higher levels of driving automation. The thesis first describes the existing safety systems, especially in relation to the driver. Then it deals with the design of the necessary measuring scenes and the implementation of new systems based on the processing of input images which are obtained via the Intel RealSense D415 stereo camera. Every system is tested in a real vehicle environment. In the end the thesis contains an evaluation regarding the detection reliability of the created algorithms, it considers their shortcomings and possible improvements.

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Tuma, Fischer Sebastian, and Jojje Sundblad. "Autonomous Compaction Roller : Temporarily convert a non autonomous compaction machine to become autonomous during endurance testing." Thesis, Blekinge Tekniska Högskola, Institutionen för maskinteknik, 2018. http://urn.kb.se/resolve?urn=urn:nbn:se:bth-16728.

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How can a non-autonomous compaction roller be converted to become temporarily autonomous while it performs a 500hours endurance test? Particularlysince the compaction rollers in question is not built to be autonomous and shall not be autonomous after the endurance test is completed. The autonomous system shall also be adaptable to all compaction rollers which Dynapac is developing and shall be moved to another machine when the endurance test is completed. In this thesis a concept is engineered of how the whole autonomous system will work and a prototype is fabricated of how to convert the current manual mechanical steering to be performed by a computer. The steering prototype has been tested on a Dynapac CC4200 double drumasphalt compaction roller and worked as intended. To develop this, anextensive risk analysis is also established andwith it a requirements list of what's needed to be fulfilled when performing autonomous testing of a compaction roller. The work has been done using the method “design thinking” which is a collection of multiple methods to create new concepts and ideas. The final concept resulted in a navigation system which uses GNSS for path planning and limitation of the operation area. It also uses radar to detect foreign objects in its path to prevent a collision. Multiple systems arealso proposed to be used for malfunction detection of the roller, which is a major part of a human operator’sjob when testing out new machines. The test track for the machine was undefined and also hadto be engineeredas part of the concept. It resultedin closing the area of operation with a mesh fence to prevent access to the area from unauthorised personnel and geo-fence to prevent the machine from escaping. Access to the area is only granted to authorized personnel and only when the autonomous rolleris shut off. Due to the machines in question isn’t fully developed, theycan’t be trusted enough to have people inside the area of operation asthe autonomous machineis operating.
Hur kanen icke-autonom vägvältomvandlas tillatt bli tillfälligt autonom medan den utför ett 500timmar långttidsprov?Särskilt sedanvägvältenifrågainte ärbyggd för att vara autonom och ska intevara autonom efter attlångtidsprovetär slutfört. Det autonoma systemet skaävenkunna anpassas tillalla vältar som Dynapac utvecklar och ska flyttas till en annan maskin närlångtidsprovetär klart. Idenna avhandling konstrueras ett koncept för hur hela det autonoma systemet kommer att fungera ochenprototyp tillverkaspå hur man konverterar den nuvarande manuella mekaniska styrningen till attstyras av en dator. Styrprototypen testades på en Dynapac CC4200 asfaltsvält med dubbla valsar ochfungerade bra. En omfattande riskanalys utvecklades ochlika såen kravlista över vad som behöveruppnås vid autonom testning av en vägvält. Arbetet har gjorts med hjälp av metoden “designthinking”, vilket är en samling av flera metoder för att skapa nya koncept och idéer. Det slutgiltigakonceptet resulterade i ett navigationssystem som använder GNSS för navigering och begränsning avkörområdet. Den använder också radar för att upptäcka främmande föremål i sin vägvilketförhindrarkollision. Flera system föreslås användasförfunktionsfelsdetektering på välten, vilket är en viktig delav en mänskligoperatörs arbetevid provning av nya maskiner. Maskinen kommer att vara i ett slutetområde som är avskilt med ett nätstängsel.Tillträde till området ges endast till behörig personal ochendast när den autonoma välten är avstängd. På grund avmaskinerna ifråga inte är fullt utvecklade,kan de inte litas på tillräckligt för att ha personer inom körområdet medan det autonoma systemet är idrift.

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Qiu, Yesiliang. "Autonomous Tick Collection Robot: Platform Development and Driving System Control." University of Cincinnati / OhioLINK, 2020. http://rave.ohiolink.edu/etdc/view?acc_num=ucin1613752543210849.

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Ekehult, Joanna. "Risk analysis of software execution in an autonomous driving system." Thesis, KTH, Skolan för elektroteknik och datavetenskap (EECS), 2020. http://urn.kb.se/resolve?urn=urn:nbn:se:kth:diva-278501.

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Autonomous vehicles have the potential to offer efficient ways of moving and improvethe safety of driving. For this to occur, it must be ensured that the autonomousvehicles have a safe and reliable behaviour in nearly all situations andunder nearly all circumstances. The system that enables autonomy relies on astack of complex software functionalities, where the response and execution timesare hard to predict. It is therefore essential to create effective tools and frameworksfor evaluating the performance of the autonomous driving system in a riskyscenario. The aim of this thesis is to create and evaluate a framework for analysingthe risks of an autonomous driving system. The approach is based on an abstractmodel of the main components and interactions of the autonomous system. It providesa manner for systematically analysing the system’s behaviour through simulationswithout requiring timely and costly testing, or a very detailed and complexmodel. Specifically, the use of the method for analysing the autonomous vehicle’stiming behaviour in a risky scenario is investigated.The developed framework is used to evaluate the ability of a vehicle to stop beforecolliding with a static obstacle. In such scenario, the model-based approach foranalysing the risks for an autonomous system is feasible and effective and canprovide useful information during the development process.
Autonoma fordon kan potentiellt erbjuda både säkrare och effektivare transportmöjligheter.Men om det ska bli möjligt måste man kunna verifiera att det autonomafordonet har ett pålitligt och säkert beteende i nästan alla situationer ochunder nästan alla förhållanden. Systemet som möjliggör autonom körning byggerpå en komplex mjukvarustack, där utfallet och exekveringstiden är svåra attutsäga. Det är därför är det väsentligt att man utvecklar verktyg och strukturersom kan förutsäga säkerheten hos ett autonomt system. Syftet med detta arbeteär att utveckla och utvärdera ett verktyg för att analysera riskerna hos ett systemför autonom körning. Tillvägagångssättet är baserat på en abstrakt modellöver de huvudsakliga komponenterna och interaktionerna hos det autonoma systemet.Genom enkla simulationer istället för tidskrävande och kostsamma tester,eller mycket detaljerade och komplexa modeller, kan metoden förse användarenmed ett sätt att systematiskt analysera systemets beteende. Specifikt undersökshuruvida metoden kan användas för att analysera det autonoma systemets tidsegenskapervid ett riskfyllt scenario.Den utvecklade strukturen används för att utvärdera om fordonet hinner stannainnan det kolliderar med ett statiskt hinder. I ett sådant scenario kan man dra slutsatsenatt det modellbaserade tillvägagångssättet för att analysera riskerna hos ettautonomt system är görbart och effektivt, och kan ge värdefull information underutvecklingsarbetet.

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Tomar, Abhineet Singh. "Modern Electrical/Electronic Infrastructure for Commercial Trucks : Generic Input/Output nodes for sensors and actuators in Commercial Trucks." Thesis, KTH, Radio Systems Laboratory (RS Lab), 2017. http://urn.kb.se/resolve?urn=urn:nbn:se:kth:diva-220183.

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The presence of electrical and electronic circuits in commercial trucks has increased at a very fast rate during recent decades. With advancements in embedded systems and the introduction of electric controls in the automotive industry, the design of complex electric systems for the vehicles has become one of the major design challenges. In the commercial truck industry, the development cycles are almost a decade long. Therefore, it is a big challenge to introduce a new architecture to accommodate the modern automotive technologies in the upcoming generation of trucks. Currently, the commercial truck industry relies highly on a federated electrical/electronic (E/E) architecture. In this architecture, Electronic Control Units (ECU) are responsible for computation and Input/Output operations. These ECUs are clustered into different domains based on their respective functions. However, these domains are not isolated from each other. These modules communicate with each other using a vehicular network, which is typically a controller area network in the current trucks. In the automotive industry, automation is increasing at a fast pace. As the level of automation increases, the need for high computation also increases, which increases the overall costs. This study aims to address this problem by introducing an integrated E/E architecture where all the computational power is concentrated at one place (or perhaps two or three places to allow for redundancy). This study proposes to introduce a lowcost replacement for the current ECUs with more limited computational power but with generic input/output interfaces. This thesis provides the reader with some background of the current E/E architecture of commercial trucks and introduces the reader to ECUs. Additionally, the relevant network architectures and protocols are explained. A potential solution, based upon the centralized computation based E/E architecture and its implementation are discussed followed by a detailed analysis of the replacements for ECUs. The result of this analysis, if adopted, should result in a reduction of manufacturing and design costs, as well as make the production and maintenance process easier. Moreover, this should also have environmental benefits by reducing fuel consumption.
Förekomsten av elektronik och elektriska kretsar I kommersiella lastbilar has ökat i en väldigt snabb takt under de senaste decennierna. Med framsteg inom inbyggda system och introduktionen av elektroniska styrsystem i fordonsindustrin så har komplexa elektroniska system blivit en av de största designutmaningarna. I den kommersiella lastbilsindustrin där utvecklingscyklerna är nästan ett decennium, är det en stor utmaning att introducera ny arkitektur som tillgodoser all den nya teknologin som införlivas i fordonet. För närvarande så förlitar sig den kommersiella lastbilsindustrin mycket på en federated elektrisk/elektronisk (E/E) arkitektur. I denna arkitektur är elektroniska styrenheter (ECU) ansvariga för beräkningar och I/O (Input/Output) operationer. Dessa ECU:er är samlade i olika domäner baserade på dess funktioner. Domänerna är dock inte isolerade från varandra. De här modulerna kommunicerar därför med varandra med hjälp av ett fordonsnätverk, typiskt en CAN (Controller Area Network) i nuvarande lastbilar. I fordonsindustrin ökar automatiseringen i en snabb fart. I takt med att automatiseringen ökar så ökar även behovet av snabba och energiintensiva beräkningar, vilket i sin tur ökar den totala kostnaden. Denna studie har som mål att adressera det här problemet genom att introducera en integrated E/E arkitektur där all beräkningskraft är koncentrerad till en plats (eller två eller tre platser för att tillåta överskott). Den här studien föreslår att introducera en ersättning av nuvarande ECU:er till en låg kostnad, med lägre beräkningskraft och generiska I/O gränssnitt. Studien föreslår också ersättningar av nuvarande fordonsnätverk. Den här uppsatsen förser läsaren med viss bakgrund till den nuvarande E/E arkitekturen för kommersiella lastbilar och introducerar läsaren till ECU:er. Dessutom förklaras de relevanta nätverksarkitekturerna och protokollen. En potentiell lösning som baseras på den integrated E/E arkitekturen och dess implementering diskuteras med fokus på en detaljerad analys av ersättningarna till ECU:er. Resultatet av den här analysen skulle, om den adopteras, medföra minskning av tillverknings- och designkostnader samt leda till en förenkling av produktion och underhåll. Utöver det så bör det även ha miljöfördelar genom minskad bränsleförbrukning.

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Rosenstatter, Thomas. "Modelling the Level of Trust in a Cooperative Automated Vehicle Control System." Thesis, Högskolan i Halmstad, Akademin för informationsteknologi, 2016. http://urn.kb.se/resolve?urn=urn:nbn:se:hh:diva-32046.

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Vehicle-to-Vehicle communication is the key technology for achieving increased perception for automated vehicles where the communication allows virtual sensing with the use of sensors placed in other vehicles. In addition, this technology also allows recognising objects that are out-of-sight. This thesis presents a Trust System that allows a vehicle to make more reliable and robust decisions. The system evaluates the current situation and generates a Trust Index indicating the level of trust in the environment, the ego vehicle, and the other vehicles. Current research focuses on securing the communication between the vehicles themselves, but does not verify the content of the received data on a system level. The proposed Trust System evaluates the received data according to sensor accuracy, behaviour of other vehicles, and the perception of the local environment. The results show that the proposed method is capable of correctly identifying various situations and discusses how the Trust Index can be used to make more robust decisions.

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Adolfsson, Alexander, and Daniel Arrhenius. "Overseeing Intersection System for Autonomous Vehicle Guidance." Thesis, KTH, Skolan för elektroteknik och datavetenskap (EECS), 2019. http://urn.kb.se/resolve?urn=urn:nbn:se:kth:diva-254219.

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Intersections represents one of the most common accident sites in traffic today. The biggest cause of accidents is obstructed view and subpar communication between vehicles. Since autonomous vehicles rely on sensors that require a direct view intersections are some of the most complex situations. Where the potential for inter vehicular communication exists between modern vehicles, it is absent in the older generation. An overseeing intersection system can fill this function during the transition period to fully autonomous traffic. This project aimed to implement an intersection system to assist autonomous vehicles through a crossroad. The assist system’s objective was to collect and transmit data from cars close to the junction to the autonomous vehicles nearby. The concept was tested in simulations by having models traverse a crossroad to evaluate how it utilised the external information. No persistent conclusion could be made due to insufficient simulation environment and vehicle model control.

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Hellner, Simon, and Henrik Syvertsson. "Neurala nätverk försjälvkörande fordon : Utforskande av olika tillvägagångssätt." Thesis, Karlstads universitet, Institutionen för matematik och datavetenskap (from 2013), 2021. http://urn.kb.se/resolve?urn=urn:nbn:se:kau:diva-84560.

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Artificiella neurala nätverk (ANN) har ett brett tillämpningsområde och blir allt relevantare på flera håll, inte minst för självkörande fordon. För att träna nätverken användsmeta-algoritmer. Nätverken kan styra fordonen med hjälp av olika typer av indata. I detta projekt har vi undersökt två meta-algoritmer: genetisk algoritm (GA) och gradient descent tillsammans med bakåtpropagering (GD & BP). Vi har även undersökt två typer av indata: avståndssensorer och linjedetektering. Vi redogör för teorin bakom de metoder vi har försökt implementera. Vi lyckades inte använda GD & BP för att träna nätverk att köra fordon, men vi redogör för hur vi försökte. I resultatdelen redovisar vi hur det med GA gick att träna ANN som använder avståndssensorer och linjedetektering som indata. Sammanfattningsvis lyckades vi implementera självkörande fordon med två olika typer av indata.
Artificial Neural Networks (ANN) have a broad area of application and are growing increasingly relevant, not least in the field of autonomous vehicles. Meta algorithms are used to train networks, which can control a vehicle using several kinds of input data. In this project we have looked at two meta algorithms: genetic algorithm (GA), and gradient descent with backpropagation (GD & BP). We have looked at two types of input to the ANN: distance sensors and line detection. We explain the theory behind the methods we have tried to implement. We did not succeed in using GD & BP to train ANNs to control vehicles, but we describe our attemps. We did however succeeded in using GA to train ANNs using a combination of distance sensors and line detection as input. In summary we managed to train ANNs to control vehicles using two methods of input, and we encountered interesting problems along the way.

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Schennings, Jacob. "Deep Convolutional Neural Networks for Real-Time Single Frame Monocular Depth Estimation." Thesis, Uppsala universitet, Avdelningen för systemteknik, 2017. http://urn.kb.se/resolve?urn=urn:nbn:se:uu:diva-336923.

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Vision based active safety systems have become more frequently occurring in modern vehicles to estimate depth of the objects ahead and for autonomous driving (AD) and advanced driver-assistance systems (ADAS). In this thesis a lightweight deep convolutional neural network performing real-time depth estimation on single monocular images is implemented and evaluated. Many of the vision based automatic brake systems in modern vehicles only detect pre-trained object types such as pedestrians and vehicles. These systems fail to detect general objects such as road debris and roadside obstacles. In stereo vision systems the problem is resolved by calculating a disparity image from the stereo image pair to extract depth information. The distance to an object can also be determined using radar and LiDAR systems. By using this depth information the system performs necessary actions to avoid collisions with objects that are determined to be too close. However, these systems are also more expensive than a regular mono camera system and are therefore not very common in the average consumer car. By implementing robust depth estimation in mono vision systems the benefits from active safety systems could be utilized by a larger segment of the vehicle fleet. This could drastically reduce human error related traffic accidents and possibly save many lives. The network architecture evaluated in this thesis is more lightweight than other CNN architectures previously used for monocular depth estimation. The proposed architecture is therefore preferable to use on computationally lightweight systems. The network solves a supervised regression problem during the training procedure in order to produce a pixel-wise depth estimation map. The network was trained using a sparse ground truth image with spatially incoherent and discontinuous data and output a dense spatially coherent and continuous depth map prediction. The spatially incoherent ground truth posed a problem of discontinuity that was addressed by a masked loss function with regularization. The network was able to predict a dense depth estimation on the KITTI dataset with close to state-of-the-art performance.

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Bosello, Michael. "Integrating BDI and Reinforcement Learning: the Case Study of Autonomous Driving." Master's thesis, Alma Mater Studiorum - Università di Bologna, 2020. http://amslaurea.unibo.it/21467/.

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Recent breakthroughs in machine learning are paving the way to the vision of software 2.0 era, which foresees the replacement of traditional software development with such techniques for many applications. In the context of agent-oriented programming, we believe that mixing together cognitive architectures like the BDI one and learning techniques could trigger new interesting scenarios. In that view, our previous work presents Jason-RL, a framework that integrates BDI agents and Reinforcement Learning (RL) more deeply than what has been already proposed so far in the literature. The framework allows the development of BDI agents having both explicitly programmed plans and plans learned by the agent using RL. The two kinds of plans are seamlessly integrated and can be used without differences. Here, we take autonomous driving as a case study to verify the advantages of the proposed approach and framework. The BDI agent has hard-coded plans that define high-level directions while fine-grained navigation is learned by trial and error. This approach – compared to plain RL – is encouraging as RL struggles in temporally extended planning. We defined and trained an agent able to drive in a track with an intersection, at which it has to choose the correct path to reach the assigned target. A first step towards porting the system in the real-world has been done by building a 1/10 scale racecar prototype which learned how to drive in a simple track.

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Kalibjian, J. R. "A Packet Based, Data Driven Telemetry System for Autonomous Experimental Sub-Orbital Spacecraft." International Foundation for Telemetering, 1993. http://hdl.handle.net/10150/608857.

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International Telemetering Conference Proceedings / October 25-28, 1993 / Riviera Hotel and Convention Center, Las Vegas, Nevada
A data driven telemetry system is described that responds to the rapid nature in which experimental satellite telemetry content is changed during the development process. It also meets the needs of a diverse experiment in which the many phases of a mission may contain radically different types of telemetry data. The system emphasizes mechanisms for achieving high redundancy of critical data. A practical example of such an implementation, Brilliant Pebbles Flight Experiment Three (FE-3), is cited.

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Aramrattana, Maytheewat. "Modelling and Simulation for Evaluation of Cooperative Intelligent Transport System Functions." Licentiate thesis, Högskolan i Halmstad, Centrum för forskning om inbyggda system (CERES), 2016. http://urn.kb.se/resolve?urn=urn:nbn:se:vti:diva-12683.

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Future vehicles are expected to be equipped with wireless communication technology, that enables them to be “connected” to each others and road infrastructures. Complementing current autonomous vehicles and automated driving systems, the wireless communication allows the vehicles to interact, cooperate, and be aware of its surroundings beyond their own sensors’ range. Such sys- tems are often referred to as Cooperative Intelligent Transport Systems (C-ITS), which aims to provide extra safety, efficiency, and sustainability to transporta- tion systems. Several C-ITS applications are under development and will require thorough testing and evaluation before their deployment in the real-world. C- ITS depend on several sub-systems, which increase their complexity, and makes them difficult to evaluate. Simulations are often used to evaluate many different automotive applications, including C-ITS. Although they have been used extensively, simulation tools dedicated to determine all aspects of C-ITS are rare, especially human factors aspects, which are often ignored. The majority of the simulation tools for C-ITS rely heavily on different combinations of network and traffic simulators. The human factors issues have been covered in only a few C-ITS simulation tools, that involve a driving simulator. Therefore, in this thesis, a C-ITS simulation framework that combines driving, network, and traffic simulators is presented. The simulation framework is able to evaluate C-ITS applications from three perspectives; a) human driver; b) wireless communication; and c) traffic systems. Cooperative Adaptive Cruise Control (CACC) and its applications are chosen as the first set of C-ITS functions to be evaluated. Example scenarios from CACC and platoon merging applications are presented, and used as test cases for the simulation framework, as well as to elaborate potential usages of it. Moreover, approaches, results, and challenges from composing the simulation framework are presented and discussed. The results shows the usefulness of the proposed simulation framework.

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Fraedrich, Eva. "Autonomes Fahren." Doctoral thesis, Humboldt-Universität zu Berlin, 2018. http://dx.doi.org/10.18452/19223.

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Autonomes Fahren könnte Autonutzung und -besitz grundlegend verändern – mit erheblichen Auswirkungen darauf, wie mit dem Automobil umgegangen wird, wie Mobilität und Verkehr künftig organisiert und städtebauliche und Verkehrsinfrastrukturen gestaltet werden. Ziel der Arbeit ist es, zu einer frühzeitigen und umfassenden Auseinandersetzung mit der Technik aus empirisch-sozialwissenschaftlicher Sicht beizutragen, sowie wesentliche Einflussfaktoren und Dynamiken der Technikentwicklung zu identifizieren, um diese gestaltend begleiten zu können. Bei technologiebasierter Entwicklung ist eine Vorhersage von möglichen Entwicklungspfaden schwierig, und Akzeptanz gilt als Schlüsselfaktor für die erfolgreiche Produkteinführung. Sie vollzieht sich mittels soziotechnischer Konstruktions- und Veränderungsprozesse und ist abhängig von Personen, deren Einstellungen, Erwartungen und Handlungen, ihrer Umwelt, ihrer Werte- und Normrahmungen sowie Veränderungen im Laufe der Zeit. Diese Parameter werden in der Debatte derzeit noch wenig beachtet. Verschiedene qualitative Methoden bilden die Grundlage für eine erste Exploration und Strukturierung des noch wenig bekannten Untersuchungsgegenstands. Die Ergebnisse zeigen, dass Akzeptanz des autonomen Fahrens wesentlich vom Zusammenspiel individueller und gesellschaftlicher Einflussfaktoren abhängt – die nicht alleine über Einstellungsparameter erfasst werden können. Sie lassen sich erst vor dem Hintergrund von handlungsleitenden, kollektiven Orientierungen zu aktuellen Autonutzungspraktiken verstehen. Gleichzeitig ist ein konsistenter, in sich geschlossener Entwicklungspfad zum autonomen Fahren derzeit noch nicht absehbar, und es sind einerseits Entwicklungen möglich, die das System der Mobilität grundlegend verändern könnten. Andererseits sind aber auch Veränderungen denkbar, die das bestehende System eher ergänzen, als es radikal zu transformieren. Vor diesem Hintergrund ergeben sich je spezifische Implikationen für die weitere Forschung.
Autonomous driving could fundamentally transform car use and ownership and considerably change the way how we interact with the automobile, how mobility and transport are organized in the future and how urban and transportation infrastructures are designed. The objective of this study is to engage empirical, social sciences in a timely and comprehensive debate on autonomous driving, so the key factors and dynamics of this technological development can be identified and shaped. Forecasting development trajectories of technology-based developments proves especially difficult, and acceptance is thought to be a key factor for a successful product implementation. Acceptance takes place in the context of sociotechnical construction and transformation processes; it is dependent on individuals, their attitudes, expectations and actions, their environment, their value- and norm-framing, and on changes over time. User perception, evaluation and contextualization in relation to autonomous driving have largely gone unheeded, even though they are deemed central to technology acceptance. A set of distinct qualitative methods served to explore and structure a research topic little known to date. In sum, the results indicate that acceptance of autonomous driving fundamentally relies on the interaction of individual and societal factors that cannot be determined through attitudinal parameters only. They are better understood against the background of implicit and habitual orientations towards current car use and ownership practices. At the same time, the studies have shown that a consistent and determined development path cannot be predicted yet. While there are chances for the mobility system to undergo a fundamental transformation with the implementation of autonomous vehicles – on both supply and demand sides – potential changes could also rather complement the existing system. Specific implications for future research will be discussed in the thesis.

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47

Meindl, Jan. "Návrh a realizace elektroniky a software autonomního mobilního robotu." Master's thesis, Vysoké učení technické v Brně. Fakulta strojního inženýrství, 2017. http://www.nusl.cz/ntk/nusl-320117.

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The master's thesis deals with the design and realization of embedded control system and software of the autonomous mobile robot DACEP. The research section focuses on the selection of sensory equipment. Moreover, the design of the embedded control system and the communication interface between this system and the master PC is described in detail, followed by the design of localization and navigation software that uses ROS framework. The section is written as instructive as possible for the development of robots of similar construction. Finally the development of a graphical interface for robot diagnostics and remote control is depicted.

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Bohm, Felicia, and Klara Häger. "Introduction of Autonomous Vehicles in the Swedish Traffic System : Effects and Changes Due to the New Self-Driving Car Technology." Thesis, Uppsala universitet, Fasta tillståndets fysik, 2015. http://urn.kb.se/resolve?urn=urn:nbn:se:uu:diva-253996.

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Vehicles that are able to drive partly or fully without human interaction are called autonomous. Several companies work towards an implementation on the commercial market. This project studies autonomous vehicles through simulations of road capacity, emissions and fuel consumption together with discussions about the implementation in the Swedish context. Barriers seen as the most critical are technology, user acceptance and social factors and laws and regulations. Simulations of today’s conventional vehicle fleet are performed and compared to corresponding simulations with autonomous features included. A part of the Uppsala traffic network is simulated and key indicators average delay, average number of stops and average speed are studied. Simulation results for a high vehicle flow, corresponding to a maximum hour in the chosen network, show that the implementation will improve the road capacity parameters. Delay and number of stops decrease with 56 respectively 54 percent and speed increases with 34 percent, which are all desirable changes. Corresponding results for a low vehicle flow is a deterioration of delay and speed with 1.3 and 0.38 percent and an improvement of number of stops corresponding to 2.9 percent. Results for the low vehicle flow are not as distinct as for high flow and this pattern repeats in results from calculations for emission and fuel consumption. A workshop is held to discuss autonomous vehicle’s impact on the Swedish urban development. The participants of the workshop contributed with discussions about behavioral changes, conflicts of interest and laws and regulations in terms of autonomous vehicles.

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Ljungberg, Sebastian, and Fredrik Schalling. "HAMMS : Managing a mix of human driven and autonomous vehicles in four-way intersections." Thesis, KTH, Maskinkonstruktion (Inst.), 2017. http://urn.kb.se/resolve?urn=urn:nbn:se:kth:diva-226687.

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The purpose of this report is to improve the flow of trafficin intersections through the use of a dynamic algorithm.People spend on average more than six weeks commuting towork in Stockholm every year. A large part of the time thatis spent in traffic is due to the time delay in intersections.In this report, sensors that measure speed and distanceto the vehicle are used instead of detectors that only knowif a car is there or not. There are existing solutions that canoptimise the flow for autonomous cars but since the trafficmay consist of a mix of autonomous and human drivenvehicles during the upcoming 40 years those solutions arenot usable for some time.In this work, a system that can handle both autonomousand human driven vehicles is created. The limitation of thesystem is that it can only receive two cars coming from differentdirections simultaneously. The system does not workfor car queues. The system measures the speed of- and thedistance to the vehicles continuously.According to the simulations that were made the algorithmthat has been designed through this project is moretime efficient than the system that is in place today, assumingthat the assumptions that were made for the currentsystem are correct.The conclusion in this report is that it is possible tomake a system that is more time efficient than the one thatis in use today.
Syftet med den här rapporten är att förbättra flödet ikorsningar genom en dynamisk algoritm. Människor sitterdrygt 6 veckor i bilköer varje år. En stor del av av denspenderande tiden i traffiken är på grund av att fordonbehöver stanna i korsningar.I den här rapporten har sensorer som mäter hastighetoch distans använts istället för dagens detektorer som endastkänner av om ett fordon kör över detektorn eller inte.Det finns andra rapporter med lösningar för att öka flödeti korsningar för självkörande bilar men om man kollar pådet kommande 40 åren kommer det troligtsvis att vara enblandning av självkörande och mänskligt körda bilar.I det här arbetet skapas ett system som kan interageramed både mänskligt körda och autonoma bilar. Begränsningarnai det här systemet är att systemet endast kan taemot två bilar som kommer från olika ingångar i korsningensamtidigt. Systemet fungerar inte för bilköer. Systemet mäterden nuvarande hastigheten och distansen på fordonen.Systemet fungerar för alla olika kombinationer av mänskligtoch självkörande bilar.Resultatet av den här rapporten är att en algoritm harutvecklats och är mer tidseffektivt än systemet som användsi Sverige idag, med våra antaganden om systemet som harutveklats i den här rapporten och systemet som användsidag. Resultatet är baserat på korsningar där bara två bilarmöts utan köer.Slutsatsen av den här rapporten är att det är möjligtatt göra ett system som är mer tidseffektivt än systemetvi använder oss av idag, men vi kan inte säkertsätlla attsystemet i den här rapporten är mer robus och driftsäkertän det som används i Sverige idag.

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Nilsson, Lovisa. "Data-Driven Methods for Sonar Imaging." Thesis, Linköpings universitet, Datorseende, 2021. http://urn.kb.se/resolve?urn=urn:nbn:se:liu:diva-176249.

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Reconstruction of sonar images is an inverse problem, which is normally solved with model-based methods. These methods may introduce undesired artifacts called angular and range leakage into the reconstruction. In this thesis, a method called Learned Primal-Dual Reconstruction, which combines a data-driven and a model-based approach, is used to investigate the use of data-driven methods for reconstruction within sonar imaging. The method uses primal and dual variables inspired by classical optimization methods where parts are replaced by convolutional neural networks to iteratively find a solution to the reconstruction problem. The network is trained and validated with synthetic data on eight models with different architectures and training parameters. The models are evaluated on measurement data and the results are compared with those from a purely model-based method. Reconstructions performed on synthetic data, where a ground truth image is available, show that it is possible to achieve reconstructions with the data-driven method that have less leakage than reconstructions from the model-based method. For reconstructions performed on measurement data where no ground truth is available, some variants of the learned model achieve a good result with less leakage.

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Dissertations / Theses on the topic 'Autonomous Driving Systems'

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