Academic literature on the topic 'Autonomous Driving Systems'
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Journal articles on the topic "Autonomous Driving Systems"
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Walch, Marcel, Kristin Mühl, Martin Baumann, and Michael Weber. "Autonomous Driving." International Journal of Mobile Human Computer Interaction 9, no. 2 (April 2017): 58–74. http://dx.doi.org/10.4018/ijmhci.2017040104.
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Autonomous vehicles will need de-escalation strategies to compensate when reaching system limitations. Car-driver handovers can be considered one possible method to deal with system boundaries. The authors suggest a bimodal (auditory and visual) handover assistant based on user preferences and design principles for automated systems. They conducted a driving simulator study with 30 participants to investigate the take-over performance of drivers. In particular, the authors examined the effect of different warning conditions (take-over request only with 4 and 6 seconds time budget vs. an additional pre-cue, which states why the take-over request will follow) in different hazardous situations. Their results indicated that all warning conditions were feasible in all situations, although the short time budget (4 seconds) was rather challenging and led to a less safe performance. An alert ahead of a take-over request had the positive effect that the participants took over and intervened earlier in relation to the appearance of the take-over request. Overall, the authors' evaluation showed that bimodal warnings composed of textual and iconographic visual displays accompanied by alerting jingles and spoken messages are a promising approach to alert drivers and to ask them to take over.
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Yaakub, Salma, and Mohammed Hayyan Alsibai. "A Review on Autonomous Driving Systems." International Journal of Engineering Technology and Sciences 5, no. 1 (June 20, 2018): 1–16. http://dx.doi.org/10.15282/ijets.v5i1.2800.
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Autonomous vehicles are one of the promising solutions to reduce traffic crashes and improve mobility and traffic system. An autonomous vehicle is preferable because it helps in reducing the need for redesigning the infrastructure and because it improves the vehicle power efficiency in terms of cost and time taken to reach the destination. Autonomous vehicles can be divided into 3 types: Aerial vehicles, ground vehicles and underwater vehicles. General, four basic subsystems are integrated to enable a vehicle to move by itself which are: Position identifying and navigation system, surrounding environment situation analysis system, motion planning system and trajectory control system. In this paper, a review on autonomous vehicles and their related technological applications is presented to highlight the aspects of this industry as a part of industry 4.0 concept. Moreover, the paper discusses the best autonomous driving systems to be applied on our wheelchair project which aims at converting a manual wheelchair to a smart electric wheelchair
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Henschke, Adam. "Trust and resilient autonomous driving systems." Ethics and Information Technology 22, no. 1 (November 19, 2019): 81–92. http://dx.doi.org/10.1007/s10676-019-09517-y.
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V S, Amar. "Autonomous Driving using CNN." International Journal for Research in Applied Science and Engineering Technology 9, no. VI (June 30, 2021): 3633–36. http://dx.doi.org/10.22214/ijraset.2021.35771.
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Human beings are currently addicted to automation and robotics technologies. The state-of-the-art in deep learning technologies and AI is the subject of this autonomous driving. Driving with automated driving systems promises to be safe, enjoyable, and efficient.. It is preferable to train in a virtual environment first and then move to a real-world one. Its goal is to enable a vehicle to recognise its surroundings and navigate without the need for human intervention. The raw pixels from a single front-facing camera were directly transferred to driving commands using a convolution neural network (CNN). This end-to-end strategy proved to be remarkably effective, The system automatically learns internal representations of the essential processing stages such as detecting useful road components using only the human steering angle as the training signal. We never expressly taught it to recognise the contour of roadways, for example. In comparison to explicit issue decomposition, such as lane marking detection, Our end-to-end solution optimises all processing processes at the same time, including path planning and control. We believe that this will lead to improved performance and smaller systems in the long run. Internal components will self-optimize to maximise overall system performance, resulting in improved performance.
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Lee, Heung-Gu, Dong-Hyun Kang, and Deok-Hwan Kim. "Human–Machine Interaction in Driving Assistant Systems for Semi-Autonomous Driving Vehicles." Electronics 10, no. 19 (October 1, 2021): 2405. http://dx.doi.org/10.3390/electronics10192405.
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Currently, the existing vehicle-centric semi-autonomous driving modules do not consider the driver’s situation and emotions. In an autonomous driving environment, when changing to manual driving, human–machine interface and advanced driver assistance systems (ADAS) are essential to assist vehicle driving. This study proposes a human–machine interface that considers the driver’s situation and emotions to enhance the ADAS. A 1D convolutional neural network model based on multimodal bio-signals is used and applied to control semi-autonomous vehicles. The possibility of semi-autonomous driving is confirmed by classifying four driving scenarios and controlling the speed of the vehicle. In the experiment, by using a driving simulator and hardware-in-the-loop simulation equipment, we confirm that the response speed of the driving assistance system is 351.75 ms and the system recognizes four scenarios and eight emotions through bio-signal data.
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LIM, Kyung-Il. "Fifth-Generation Technology in Autonomous Driving Systems." Physics and High Technology 29, no. 3 (March 31, 2020): 21–26. http://dx.doi.org/10.3938/phit.29.009.
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Blasinski, Henryk, Joyce Farrell, Trisha Lian, Zhenyi Liu, and Brian Wandell. "Optimizing Image Acquisition Systems for Autonomous Driving." Electronic Imaging 2018, no. 5 (January 28, 2018): 161–1. http://dx.doi.org/10.2352/issn.2470-1173.2018.05.pmii-161.
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Bhat, Anand, Shunsuke Aoki, and Ragunathan Rajkumar. "Tools and Methodologies for Autonomous Driving Systems." Proceedings of the IEEE 106, no. 9 (September 2018): 1700–1716. http://dx.doi.org/10.1109/jproc.2018.2841339.
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Vitas, Dijana, Martina Tomic, and Matko Burul. "Traffic Light Detection in Autonomous Driving Systems." IEEE Consumer Electronics Magazine 9, no. 4 (July 1, 2020): 90–96. http://dx.doi.org/10.1109/mce.2020.2969156.
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Baber, J., J. Kolodko, T. Noel, M. Parent, and L. Vlacic. "Cooperative autonomous driving - Intelligent vehicles sharing city roads cooperative autonomous driving." IEEE Robotics & Automation Magazine 12, no. 1 (March 2005): 44–49. http://dx.doi.org/10.1109/mra.2005.1411418.
Full textDissertations / Theses on the topic "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 validationRoad 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.
Books on the topic "Autonomous Driving Systems"
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Shi, Weisong, and Liangkai Liu. Computing Systems for Autonomous Driving. Cham: Springer International Publishing, 2021. http://dx.doi.org/10.1007/978-3-030-81564-6.
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Joseph, Lentin, and Amit Kumar Mondal. Autonomous Driving and Advanced Driver-Assistance Systems (ADAS). Boca Raton: CRC Press, 2021. http://dx.doi.org/10.1201/9781003048381.
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Waschl, Harald, Ilya Kolmanovsky, and Frank Willems, eds. Control Strategies for Advanced Driver Assistance Systems and Autonomous Driving Functions. Cham: Springer International Publishing, 2019. http://dx.doi.org/10.1007/978-3-319-91569-2.
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Trimble, Tammy E., Stephanie Baker, Jason Wagner, Wendy Wagner, Lisa Loftus-Otway, Brad Mallory, Susanna Gallun, et al. Implications of Connected and Automated Driving Systems, Vol. 4: Autonomous Vehicle Action Plan. Washington, D.C.: Transportation Research Board, 2018. http://dx.doi.org/10.17226/25292.
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Zuev, Sergey, Ruslan Maleev, and Aleksandr Chernov. Energy efficiency of electrical equipment systems of autonomous objects. ru: INFRA-M Academic Publishing LLC., 2021. http://dx.doi.org/10.12737/1740252.
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When considering the main trends in the development of modern autonomous objects (aircraft, combat vehicles, motor vehicles, floating vehicles, agricultural machines, etc.) in recent decades, two key areas can be identified. The first direction is associated with the improvement of traditional designs of autonomous objects (AO) with an internal combustion engine (ICE) or a gas turbine engine (GTD). The second direction is connected with the creation of new types of joint-stock companies, namely electric joint-stock companies( EAO), joint-stock companies with combined power plants (AOKEU).
The energy efficiency is largely determined by the power of the generator set and the battery, which is given to the electrical network in various driving modes.
Most of the existing methods for calculating power supply systems use the average values of disturbing factors (generator speed, current of electric energy consumers, voltage in the on-board network) when choosing the characteristics of the generator set and the battery. At the same time, it is obvious that when operating a motor vehicle, these parameters change depending on the driving mode. Modern methods of selecting the main parameters and characteristics of the power supply system do not provide for modeling its interaction with the power unit start-up system of a motor vehicle in operation due to the lack of a systematic approach.
The choice of a generator set and a battery, as well as the concept of the synthesis of the power supply system is a problem studied in the monograph.
For all those interested in electrical engineering and electronics.
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Trimble, Tammy E., Stephanie Baker, Jason Wagner, Myra Blanoo, Wendy Wagner, Lisa Loftus-Otway, Brad Mallory, et al. Implications of Connected and Automated Driving Systems, Vol. 5: Developing the Autonomous Vehicle Action Plan. Washington, D.C.: Transportation Research Board, 2018. http://dx.doi.org/10.17226/25291.
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Shi, Weisong, and Liangkai Liu. Computing Systems for Autonomous Driving. Springer International Publishing AG, 2022.
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Shi, Weisong, and Liangkai Liu. Computing Systems for Autonomous Driving. Springer International Publishing AG, 2021.
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Ziefle, Martina, Houbing Song, Guido Dartmann, Anke Schmeink, and Volker Lücken. Smart Transportation: AI Enabled Mobility and Autonomous Driving. Taylor & Francis Group, 2021.
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Ziefle, Martina, Houbing Song, Guido Dartmann, Anke Schmeink, and Volker Lücken. Smart Transportation: AI Enabled Mobility and Autonomous Driving. Taylor & Francis Group, 2021.
Find full textBook chapters on the topic "Autonomous Driving Systems"
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Matthaei, Richard, Andreas Reschka, Jens Rieken, Frank Dierkes, Simon Ulbrich, Thomas Winkle, and Markus Maurer. "Autonomous Driving." In Handbook of Driver Assistance Systems, 1519–56. Cham: Springer International Publishing, 2015. http://dx.doi.org/10.1007/978-3-319-12352-3_61.
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Matthaei, Richard, Andreas Reschka, Jens Rieken, Frank Dierkes, Simon Ulbrich, Thomas Winkle, and Markus Maurer. "Autonomous Driving." In Handbook of Driver Assistance Systems, 1–31. Cham: Springer International Publishing, 2015. http://dx.doi.org/10.1007/978-3-319-09840-1_61-1.
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Pavone, Marco. "Autonomous Mobility-on-Demand Systems for Future Urban Mobility." In Autonomous Driving, 387–404. Berlin, Heidelberg: Springer Berlin Heidelberg, 2016. http://dx.doi.org/10.1007/978-3-662-48847-8_19.
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Iclodean, Călin, Bogdan Ovidiu Varga, and Nicolae Cordoș. "Autonomous Driving Systems." In Autonomous Vehicles for Public Transportation, 69–138. Cham: Springer International Publishing, 2022. http://dx.doi.org/10.1007/978-3-031-14678-7_3.
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Shi, Weisong, and Liangkai Liu. "Autonomous Driving Landscape." In Computing Systems for Autonomous Driving, 1–18. Cham: Springer International Publishing, 2021. http://dx.doi.org/10.1007/978-3-030-81564-6_1.
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Shi, Weisong, and Liangkai Liu. "Autonomous Driving Simulators." In Computing Systems for Autonomous Driving, 143–56. Cham: Springer International Publishing, 2021. http://dx.doi.org/10.1007/978-3-030-81564-6_6.
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Behere, Sagar, and Martin Törngren. "Systems Engineering and Architecting for Intelligent Autonomous Systems." In Automated Driving, 313–51. Cham: Springer International Publishing, 2016. http://dx.doi.org/10.1007/978-3-319-31895-0_13.
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Hammoud, Ahmad, Azzam Mourad, Hadi Otrok, and Zbigniew Dziong. "Data-Driven Federated Autonomous Driving." In Mobile Web and Intelligent Information Systems, 79–90. Cham: Springer International Publishing, 2022. http://dx.doi.org/10.1007/978-3-031-14391-5_6.
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Liu, Shaoshan, Liyun Li, Jie Tang, Shuang Wu, and Jean-Luc Gaudiot. "Perception in Autonomous Driving." In Creating Autonomous Vehicle Systems, 51–67. Cham: Springer International Publishing, 2018. http://dx.doi.org/10.1007/978-3-031-01802-2_3.
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Liu, Shaoshan, Liyun Li, Jie Tang, Shuang Wu, and Jean-Luc Gaudiot. "Introduction to Autonomous Driving." In Creating Autonomous Vehicle Systems, 1–14. Cham: Springer International Publishing, 2018. http://dx.doi.org/10.1007/978-3-031-01802-2_1.
Full textConference papers on the topic "Autonomous Driving Systems"
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Furst, Simon. "System/ Software Architecture for Autonomous Driving Systems." In 2019 IEEE International Conference on Software Architecture Companion (ICSA-C). IEEE, 2019. http://dx.doi.org/10.1109/icsa-c.2019.00013.
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"Session AD: Autonomous Driving." In 2021 16th International Conference on Computer Engineering and Systems (ICCES). IEEE, 2021. http://dx.doi.org/10.1109/icces54031.2021.9686118.
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Biral, Francesco, Enrico Bertolazzi, Daniele Bortoluzzi, and Paolo Bosetti. "Development and Testing of an Autonomous Driving Module for Critical Driving Conditions." In ASME 2008 International Mechanical Engineering Congress and Exposition. ASMEDC, 2008. http://dx.doi.org/10.1115/imece2008-68487.
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In the last years a great effort has been devoted to the development of autonomous vehicles able to drive in a high range of speeds in semi-structured and unstructured environments. This article presents and discusses the software framework for Hardware-In-the-Loop (HIL) and Software-In-the-Loop (SIL) analysis that has been designed for developing and testing of control laws and mission functionalities of semi-autonomous and autonomous vehicles. The ultimate goal of this project is to develop a robotic system, named RUMBy, able to autonomously plan and execute accurate optimal manoeuvres both in normal and in critical driving situations and to be used as a test platform for advanced decision and autonomous driving algorithms. RUMBy’s hardware is a 1:6 scale gasoline engine R/C car with onboard telemetry and control systems. RUMBy’s software consists of three main modules: the manager module that coordinates the other modules and take high level decision; the motion planner module which is based on a Nonlinear Receding Horizon Control (NRHC) algorithm; the actuation module that produces the driving command for the vehicle. The article describes the details of RUMBy architecture and discusses its modular configuration that easily allows HIL and SIL tests.
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van den Broek, Thijs H. A., Jeroen Ploeg, and Bart D. Netten. "Advisory and autonomous cooperative driving systems." In 2011 IEEE International Conference on Consumer Electronics (ICCE). IEEE, 2011. http://dx.doi.org/10.1109/icce.2011.5722582.
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Stocco, Andrea, Michael Weiss, Marco Calzana, and Paolo Tonella. "Misbehaviour prediction for autonomous driving systems." In ICSE '20: 42nd International Conference on Software Engineering. New York, NY, USA: ACM, 2020. http://dx.doi.org/10.1145/3377811.3380353.
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Wang, Keying. "Safe Reconfiguration of Autonomous Driving Systems." In 2020 IEEE MIT Undergraduate Research Technology Conference (URTC). IEEE, 2020. http://dx.doi.org/10.1109/urtc51696.2020.9668860.
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Ouarnoughi, Hamza, Mohamed Neggaz, Berkay Gulcan, Ozcan Ozturk, and Smail Niar. "Hierarchical Platform for Autonomous Driving." In INTESA2019: INTelligent Embedded Systems Architectures and Applications Workshop 2019. New York, NY, USA: ACM, 2019. http://dx.doi.org/10.1145/3372394.3372400.
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"Image Processing for Autonomous Driving." In 2019 International Conference on Systems, Signals and Image Processing (IWSSIP). IEEE, 2019. http://dx.doi.org/10.1109/iwssip.2019.8787260.
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Laurenza, Maicol, Gianluca Pepe, and Antonio Carcaterra. "Auto-Sapiens Autonomous Driving Vehicle." In 6th International Conference on Vehicle Technology and Intelligent Transport Systems. SCITEPRESS - Science and Technology Publications, 2020. http://dx.doi.org/10.5220/0009419403610369.
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Laurenza, Maicol, Gianluca Pepe, and Antonio Carcaterra. "Auto-Sapiens Autonomous Driving Vehicle." In 6th International Conference on Vehicle Technology and Intelligent Transport Systems. SCITEPRESS - Science and Technology Publications, 2020. http://dx.doi.org/10.5220/0009419400002550.
Full textReports on the topic "Autonomous Driving Systems"
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Wang, Shenlong, and David Forsyth. Safely Test Autonomous Vehicles with Augmented Reality. Illinois Center for Transportation, August 2022. http://dx.doi.org/10.36501/0197-9191/22-015.
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This work exploits augmented reality to safely train and validate autonomous vehicles’ performance in the real world under safety-critical scenarios. Toward this goal, we first develop algorithms that create virtual traffic participants with risky behaviors and seamlessly insert the virtual events into real images perceived from the physical world. The resulting composed images are photorealistic and physically grounded. The manipulated images are fed into the autonomous vehicle during testing, allowing the self-driving vehicle to react to such virtual events within either a photorealistic simulator or a real-world test track and real hardware systems. Our presented technique allows us to develop safe, hardware-in-the-loop, and cost-effective tests for self-driving cars to respond to immersive safety-critical traffic scenarios.
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Razdan, Rahul. Unsettled Topics Concerning Human and Autonomous Vehicle Interaction. SAE International, December 2020. http://dx.doi.org/10.4271/epr2020025.
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This report examines the current interaction points between humans and autonomous systems, with a particular focus on advanced driver assistance systems (ADAS), the requirements for human-machine interfaces as imposed by human perception, and finally, the progress being made to close the gap. Autonomous technology has the potential to benefit personal transportation, last-mile delivery, logistics, and many other mobility applications enormously. In many of these applications, the mobility infrastructure is a shared resource in which all the players must cooperate. In fact, the driving task has been described as a “tango” where we—as humans—cooperate naturally to enable a robust transportation system. Can autonomous systems participate in this tango? Does that even make sense? And if so, how do we make it happen?
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Hemphill, Jeff. Unsettled Issues in Drive-by-Wire and Automated Driving System Availability. SAE International, January 2022. http://dx.doi.org/10.4271/epr2022002.
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While many observers think that autonomy is right around the corner, there many unsettled issues. One such issue is availability, or how the vehicle behaves in the event of a failure of one of its systems such as those with the latest “by-wire” technologies. Handling of failures at a technical actuation level could involve many aspects, including time of operation after first fault, function/performance after first fault, and exposure after first fault. All of these and other issues are affected by software and electronic and mechanical hardware. Drive-by-wire and Automated Driving System Availability discusses the necessary systems approach required to address these issues. Establishing an industry path forward for these topics will simplify system development and provide a framework for consistent regulation and liability, which is an enabler for the launch of autonomous vehicles.
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Qin, Tong, Zhen Chen, John Jakeman, and Dongbin Xiu. Data-driven learning of non-autonomous systems. Office of Scientific and Technical Information (OSTI), June 2020. http://dx.doi.org/10.2172/1763550.
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Quinn, Brian, Jordan Bates, Michael Parker, and Sally Shoop. A detailed approach to autonomous vehicle control through Ros and Pixhawk controllers. Engineer Research and Development Center (U.S.), November 2021. http://dx.doi.org/10.21079/11681/42460.
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A Polaris MRZR military utility vehicle was used as a testing platform to develop a novel, low cost yet feature-rich, approach to adding remote operation and autonomous driving capability to a military vehicle. The main concept of operation adapts steering and throttle output from a low cost commercially available Pixhawk autopilot controller and translates the signal into the necessary inputs for the Robot Operating System (ROS) based drive by wire system integrated into the MRZR. With minimal modification these enhancements could be applied to any vehicle with similar ROS integration. This paper details the methods and testing approach used to develop this autonomous driving capability.
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Porcel Magnusson, Cristina. Unsettled Topics Concerning Coating Detection by LiDAR in Autonomous Vehicles. SAE International, January 2021. http://dx.doi.org/10.4271/epr2021002.
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Autonomous vehicles (AVs) utilize multiple devices, like high-resolution cameras and radar sensors, to interpret the driving environment and achieve full autonomy. One of these instruments—the light detection and ranging (LiDAR) sensor—utilizes pulsed infrared (IR) light, typically at wavelengths of 905 nm or 1,550 nm, to calculate object distance and position. Exterior automotive paint covers an area larger than any other exterior material. Therefore, understanding how LiDAR wavelengths interact with vehicle coatings is extremely important for the safety of future automated driving technologies. Sensing technologies and materials are two different industries that have not directly interacted in the perception and system sense. With the new applications in the AV industry, multidisciplinary approaches need to be taken to ensure reliability and safety in the future. Unsettled Topics Concerning Coating Detection by LiDAR in Autonomous Vehicles provides a transversal view of different industry segments, from pigment and coating manufacturers to LiDAR components and vehicle system development and integration. The report includes a structured decomposition of the different variables and technologies involved.
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Levine, Edward R. Multi-Scale Model-Driven Sampling with Autonomous Systems at a National Littoral Laboratory: Turbulence Characterization from an AUV. Fort Belvoir, VA: Defense Technical Information Center, September 1999. http://dx.doi.org/10.21236/ada630605.
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Levine, Edward R. Renewal of Multi-Scale Model-Driven Sampling with Autonomous Systems at a National Littoral Laboratory: Turbulence Characterization with an AUV. Fort Belvoir, VA: Defense Technical Information Center, September 2001. http://dx.doi.org/10.21236/ada625153.
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Martinez, Kimberly D., and Gaojian Huang. Exploring the Effects of Meaningful Tactile Display on Perception and Preference in Automated Vehicles. Mineta Transportation Institute, October 2022. http://dx.doi.org/10.31979/mti.2022.2164.
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There is an existing issue in human-machine interaction, such that drivers of semi-autonomous vehicles are still required to take over control of the vehicle during system limitations. A possible solution may lie in tactile displays, which can present status, direction, and position information while avoiding sensory (e.g., visual and auditory) channels overload to reliably help drivers make timely decisions and execute actions to successfully take over. However, limited work has investigated the effects of meaningful tactile signals on takeover performance. This study synthesizes literature investigating the effects of tactile displays on takeover performance in automated vehicles and conducts a human-subject study to design and test the effects of six meaningful tactile signal types and two pattern durations on drivers’ perception and performance during automated driving. The research team performed a literature review of 18 articles that conducted human-subjects experiments on takeover performance utilizing tactile displays as takeover requests. Takeover performance in these studies were highlighted, such as response times, workload, and accuracy. The team then conducted a human-subject experiment, which included 16 participants that used a driving simulator to present 30 meaningful vibrotactile signals, randomly across four driving sessions measuring for reaction times (RTs), interpretation accuracy, and subjective ratings. Results from the literature suggest that tactile displays can present meaningful vibrotactile patterns via various in-vehicle locations to help improve drivers’ performance during the takeover and can be used to assist in the design of human-machine interfaces (HMI) for automated vehicles. The experiment yielded results illustrating higher urgency patterns were associated with shorter RTs and higher intuitive ratings. Also, pedestrian status and headway reduction signals presented shorter RTs and increased confidence ratings compared to other tactile signal types. Finally, the signal types that yielded the highest accuracy were the surrounding vehicle and navigation signal types. Implications of these findings may lie in informing the design of next-generation in-vehicle HMIs and future human factors studies on human-automation interactions.
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Rarasati, Niken, and Rezanti Putri Pramana. Giving Schools and Teachers Autonomy in Teacher Professional Development Under a Medium-Capability Education System. Research on Improving Systems of Education (RISE), January 2023. http://dx.doi.org/10.35489/bsg-rise-ri_2023/050.
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A mature teacher who continuously seeks improvement should be recognised as a professional who has autonomy in conducting their job and has the autonomy to engage in a professional community of practice (Hyslop-Margison and Sears, 2010). In other words, teachers’ engagement in professional development activities should be driven by their own determination rather than extrinsic sources of motivation. In this context, teachers’ self-determination can be defined as a feeling of connectedness with their own aspirations or personal values, confidence in their ability to master new skills, and a sense of autonomy in planning their own professional development path (Stupnisky et al., 2018; Eyal and Roth, 2011; Ryan and Deci, 2000). Previous studies have shown the advantages of providing teachers with autonomy to determine personal and professional improvement. Bergmark (2020) found that giving teachers the opportunity to identify areas of improvement based on teaching experience expanded the ways they think and understand themselves as teachers and how they can improve their teaching. Teachers who plan their own improvement showed a higher level of curiosity in learning and trying out new things. Bergmark (2020) also shows that a continuous cycle of reflection and teaching improvement allows teachers to recognise that the perfect lesson does not exist. Hence, continuous reflection and improvement are needed to shape the lesson to meet various classroom contexts. Moreover, Cheon et al. (2018) found that increased teacher autonomy led to greater teaching efficacy and a greater tendency to adopt intrinsic (relative to extrinsic) instructional goals. In developed countries, teacher autonomy is present and has become part of teachers’ professional life and schools’ development plans. In Finland, for example, the government is responsible for providing resources and services that schools request, while school development and teachers’ professional learning are integrated into a day-to-day “experiment” performed collaboratively by teachers and principals (Niemi, 2015). This kind of experience gives teachers a sense of mastery and boosts their determination to continuously learn (Ryan and Deci, 2000). In low-performing countries, distributing autonomy of education quality improvement to schools and teachers negatively correlates with the countries’ education outcomes (Hanushek et al., 2011). This study also suggests that education outcome accountability and teacher capacity are necessary to ensure the provision of autonomy to improve education quality. However, to have teachers who can meet dynamic educational challenges through continuous learning, de Klerk & Barnett (2020) suggest that developing countries include programmes that could nurture teachers’ agency to learn in addition to the regular content and pedagogical-focused teacher training materials. Giving autonomy to teachers can be challenging in an environment where accountability or performance is measured by narrow considerations (teacher exam score, administrative completion, etc.). As is the case in Jakarta, the capital city of Indonesia, teachers tend to attend training to meet performance evaluation administrative criteria rather than to address specific professional development needs (Dymoke and Harrison, 2006). Generally, the focus of the training relies on what the government believes will benefit their teaching workforce. Teacher professional development (TPD) is merely an assignment for Jakarta teachers. Most teachers attend the training only to obtain attendance certificates that can be credited towards their additional performance allowance. Consequently, those teachers will only reproduce teaching practices that they have experienced or observed from their seniors. As in other similar professional development systems, improvement in teaching quality at schools is less likely to happen (Hargreaves, 2000). Most of the trainings were led by external experts or academics who did not interact with teachers on a day-to-day basis. This approach to professional development represents a top-down mechanism where teacher training was designed independently from teaching context and therefore appears to be overly abstract, unpractical, and not useful for teachers (Timperley, 2011). Moreover, the lack of relevancy between teacher training and teaching practice leads to teachers’ low ownership of the professional development process (Bergmark, 2020). More broadly, in the Jakarta education system, especially the public school system, autonomy was never given to schools and teachers prior to establishing the new TPD system in 2021. The system employed a top-down relationship between the local education agency, teacher training centres, principals, and teachers. Professional development plans were usually motivated by a low teacher competency score or budgeted teacher professional development programme. Guided by the scores, the training centres organised training that could address knowledge areas that most of Jakarta's teachers lack. In many cases, to fulfil the quota as planned in the budget, the local education agency and the training centres would instruct principals to assign two teachers to certain training without knowing their needs. Realizing that the system was not functioning, Jakarta’s local education agency decided to create a reform that gives more autonomy toward schools and teachers in determining teacher professional development plan. The new system has been piloted since November 2021. To maintain the balance between administrative evaluation and addressing professional development needs, the new initiative highlights the key role played by head teachers or principals. This is based on assumption that principals who have the opportunity to observe teaching practice closely could help teachers reflect and develop their professionalism. (Dymoke and Harrison, 2006). As explained by the professional development case in Finland, leadership and collegial collaboration are also critical to shaping a school culture that could support the development of professional autonomy. The collective energies among teachers and the principal will also direct the teacher toward improving teaching, learning, and caring for students and parents (Hyslop-Margison and Sears, 2010; Hargreaves, 2000). Thus, the new TPD system in Jakarta adopts the feature of collegial collaboration. This is considered as imperative in Jakarta where teachers used to be controlled and join a professional development activity due to external forces. Learning autonomy did not exist within themselves. Hence, teachers need a leader who can turn the "professional development regulation" into a culture at schools. The process will shape teachers to do professional development quite autonomously (Deci et al., 2001). In this case, a controlling leadership style will hinder teachers’ autonomous motivation. Instead, principals should articulate a clear vision, consider teachers' individual needs and aspirations, inspire, and support professional development activities (Eyal and Roth, 2011). This can also be called creating a professional culture at schools (Fullan, 1996). In this Note, we aim to understand how the schools and teachers respond to the new teacher professional development system. We compare experience and motivation of different characteristics of teachers.