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Relevant bibliographies by topics / COMPUTER ASSISTED DRIVING

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Academic literature on the topic 'COMPUTER ASSISTED DRIVING'

Author: Grafiati

Published: 4 June 2025

Last updated: 23 June 2025

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Journal articles on the topic "COMPUTER ASSISTED DRIVING"

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Nicolescu, Florin Adrian, Georgia Cezara Avram, Andrei Mario Ivan, and Adrian Theodor Mantea. "Computer Assisted Selection of Servomotor Driving System for Linear Motion NC Axis Experimental Stand." Applied Mechanics and Materials 762 (May 2015): 61–66. http://dx.doi.org/10.4028/www.scientific.net/amm.762.61.

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The paper presents the works performed by the authors in the field of structural and functional optimization numerically controlled (NC) axes. The study includes two computing applications developed by second author of the paper in a PhD thesis related on NC axes’ structural and functional optimization. The first computing application is used for calculating the total reflected inertia of a linear motion NC axis (total inertial loads' reducing on the driving motor's shaft level). The second computing application is used for both preliminary selection of the driving servomotor (by checking first the accomplishment of the kinematic criterion) and a secondary selection of the electric motor (by checking in a second stage the accomplishment of the static and dynamic criterion). By mean of both software applications optimal matching of servomotor driving system with available NC axis mechanical structure may be determined. The analyzed linear motion NC axis is part of an experimental stand (existing in the MMS department from EMTS faculty), that supplementary to the driving servomotor, includes a belt drive transmission, a ball screw - bearings assembly and a driven element guided by ball rail system.

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Nicolescu, Florin Adrian, Georgia Cezara Avram, Andrei Mario Ivan, and Adrian Theodor Mantea. "Brushless Servomotor’s Thermal Behavior Computer Assisted Evaluation for a Linear Motion NC Axis Experimental Stand." Applied Mechanics and Materials 762 (May 2015): 21–26. http://dx.doi.org/10.4028/www.scientific.net/amm.762.21.

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The paper presents works carried out by the authors in the field of NC axes’ structural and functional optimization. This paper includes the results obtained by using a MathCAD application (developed in the doctoral thesis of the second author) for the servomotor's thermal behavior computer assisted evaluation. The analyzed servomotor is included in the driving system of a linear motion NC axis experimental testing stand, (existing in MMS department from EMTS Faculty). The NC axis of the experimental stand integrates a FAGOR brushless servomotor, a 1:1 ratio belt drive intermediary transmission, a ball screw - bearings assembly (lead screw - ball nut - bearings) and a driven element guided through a ball rail system. The MathCAD application was developed in order to perform specific calculation for servomotor's thermal energy dissipation and maximum operating temperature evaluation, and allows to perform the assisted final check and optimum selection of the electrical driving servomotors based on these results. The paper presents, through some screenshots from running the MathCAD application, the computer assisted evaluation procedure and results of the brushless servomotor's thermal behavior analysis corresponding to analyzed NC axis (included in the experimental stand).

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Yan, Haibo. "Vehicle Safety-Assisted Driving Technology Based on Computer Artificial Intelligence Environment." Computational Intelligence and Neuroscience 2022 (June 18, 2022): 1–10. http://dx.doi.org/10.1155/2022/4390394.

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In this paper, we propose an assisted driving system implemented with a Jetson nano-high-performance embedded platform by using machine vision and deep learning technologies. The vehicle dynamics model is established under multiconditional assumptions, the path planner and path tracking controller are designed based on the model predictive control algorithm, and the local desired path is reasonably planned in combination with the behavioral decision system. The behavioral decision algorithm based on finite state machine reasonably transforms the driving state according to the environmental changes, realizes the following of the target vehicle speed, and can take effective emergency braking in time when there is a collision danger. The system can complete the motion planning by the model predictive control algorithm and control the autonomous vehicle to smoothly track the replanned local desired path to complete the lane change overtaking action, which can meet the demand of ADAS. The path planner is designed based on the MPC algorithm, solving the objective function with obstacle avoidance function, planning the optimal path that can avoid a collision, and using 5th order polynomial to fit the output local desired path points. In 5∼8 s time, the target vehicle decelerates to 48 km/h; the autonomous vehicle immediately makes a deceleration action and gradually reduces the speed difference between the two vehicles until it reaches the target speed, at which time the distance between the two vehicles is close to the safe distance, obtained by the simulation test results. The system can still accurately track the target when the vehicle is driving on a curve and timely control the desired speed change of the vehicle, and the target vehicle always maintains a safe distance. The system can be used within 50 meters.

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Ehlgen, Tobias, Tomas Pajdla, and Dieter Ammon. "Eliminating Blind Spots for Assisted Driving." IEEE Transactions on Intelligent Transportation Systems 9, no. 4 (2008): 657–65. http://dx.doi.org/10.1109/tits.2008.2006815.

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Intelligence and Neuroscience, Computational. "Retracted: Vehicle Safety-Assisted Driving Technology Based on Computer Artificial Intelligence Environment." Computational Intelligence and Neuroscience 2023 (December 13, 2023): 1. http://dx.doi.org/10.1155/2023/9759151.

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Cui, Jianwei, Yucheng Shang, Siji Yu, and Yuanbo Wang. "Research on Intelligent Wheelchair Multimode Human–Computer Interaction and Assisted Driving Technology." Actuators 13, no. 6 (2024): 230. http://dx.doi.org/10.3390/act13060230.

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The traditional wheelchair focuses on the “human-chair” motor function interaction to ensure the elderly and people with disabilities’ basic travel. For people with visual, hearing, physical disabilities, etc., the current wheelchairs show shortcomings in terms of accessibility and independent travel for this group. Therefore, this paper develops an intelligent wheelchair with multimodal human–computer interaction and autonomous navigation technology. Firstly, it researches the multimodal human–computer interaction technology of occupant gesture recognition, speech recognition, and head posture recognition and proposes a wheelchair control method of three-dimensional head posture mapping the two-dimensional plane. After testing, the average accuracy of the gesture, head posture and voice control modes of the motorized wheelchair proposed in this study reaches more than 95 percent. Secondly, the LiDAR-based smart wheelchair indoor autonomous navigation technology is investigated to realize the autonomous navigation of the wheelchair by constructing an environment map, using A* and DWA algorithms for global and local path planning, and adaptive Monte Carlo simulation algorithms for real-time localization. Experiments show that the position error of the wheelchair is within 10 cm, and the heading angle error is less than 5° during the autonomous navigation. The multimode human–computer interaction and assisted driving technology proposed in this study can partially compensate and replace the functional deficiencies of the disabled population and improve the quality of life of the elderly and disabled population.

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Huang, Xueda, Shaowen Wang, Guanqiu Qi, et al. "Driver Distraction Detection Based on Cloud Computing Architecture and Lightweight Neural Network." Mathematics 11, no. 23 (2023): 4862. http://dx.doi.org/10.3390/math11234862.

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Distracted behavior detection is an important task in computer-assisted driving. Although deep learning has made significant progress in this area, it is still difficult to meet the requirements of the real-time analysis and processing of massive data by relying solely on local computing power. To overcome these problems, this paper proposes a driving distraction detection method based on cloud–fog computing architecture, which introduces scalable modules and a model-driven optimization based on greedy pruning. Specifically, the proposed method makes full use of cloud–fog computing to process complex driving scene data, solves the problem of local computing resource limitations, and achieves the goal of detecting distracted driving behavior in real time. In terms of feature extraction, scalable modules are used to adapt to different levels of feature extraction to effectively capture the diversity of driving behaviors. Additionally, in order to improve the performance of the model, a model-driven optimization method based on greedy pruning is introduced to optimize the model structure to obtain a lighter and more efficient model. Through verification experiments on multiple driving scene datasets such as LDDB and Statefarm, the effectiveness of the proposed driving distraction detection method is proved.

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Bieri, Rahel, Michael Jäger, Nicole Gruber, Tobias Nef, René M. Müri, and Urs P. Mosimann. "A novel computer test to assess driving-relevant cognitive functions – a pilot study." International Psychogeriatrics 26, no. 2 (2013): 229–38. http://dx.doi.org/10.1017/s104161021300183x.

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ABSTRACTBackground:The assessment of driving-relevant cognitive functions in older drivers is a difficult challenge as there is no clear-cut dividing line between normal cognition and impaired cognition and not all cognitive functions are equally important for driving.Methods:To support decision makers, the Bern Cognitive Screening Test (BCST) for older drivers was designed. It is a computer-assisted test battery assessing visuo-spatial attention, executive functions, eye–hand coordination, distance judgment, and speed regulation. Here we compare the performance in BCST with the performance in paper and pencil cognitive screening tests and the performance in the driving simulator testing of 41 safe drivers (without crash history) and 14 unsafe drivers (with crash history).Results:Safe drivers performed better than unsafe drivers in BCST (Mann–Whitney U test: U = 125.5; p = 0.001) and in the driving simulator (Student's t-test: t(44) = –2.64, p = 0.006). No clear group differences were found in paper and pencil screening tests (p > 0.05; ns). BCST was best at identifying older unsafe drivers (sensitivity 86%; specificity 61%) and was also better tolerated than the driving simulator test with fewer dropouts.Conclusions:BCST is more accurate than paper and pencil screening tests, and better tolerated than driving simulator testing when assessing driving-relevant cognition in older drivers.

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Geng, Wei Feng. "Design and Implementation of SMS-Based Business English Education System." Advanced Materials Research 971-973 (June 2014): 2564–67. http://dx.doi.org/10.4028/www.scientific.net/amr.971-973.2564.

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With increasing popularity and development of computer and network technology, the driving force behind business English teaching reform and providing development. Internet assisted teaching of English in many ways irreplaceable advantages, it is with traditional teaching methods are complementary and mutually balanced, played a traditional teaching does not have the function and role in business English teaching. Based on the features and role of the Internet to support teaching business English courses, explore network-assisted college English teaching model professional business English.

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Hammond, Philip. "From computer-assisted to data-driven: Journalism and Big Data." Journalism 18, no. 4 (2016): 408–24. http://dx.doi.org/10.1177/1464884915620205.

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Despite claims of continuity, contemporary data journalism is quite different from the earlier tradition of computer-assisted reporting. Although it echoes earlier claims about being scientific and democratic, these qualities are understood as resulting from better data access rather than as being something achieved by the journalist. In the context of Big Data in particular, human subjectivity tends to be downgraded in importance, even understood as getting in the way if it means hubristically theorising about causation rather than working with correlation and allowing the data to speak. Increasing ‘datafication’ is not what is driving changes in the profession, however. Rather, the impact of Big Data tends to be understood in ways that are consonant with pre-existing expectations, which are shaped by the broader contemporary post-humanist political context. The same is true in academic analysis, where actor–network theory seems to be emerging as the dominant paradigm for understanding data journalism, but in largely uncritical ways.

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Dissertations / Theses on the topic "COMPUTER ASSISTED DRIVING"

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Chow, Der-Lin. "Computer-Assisted Robotic Suturing." Case Western Reserve University School of Graduate Studies / OhioLINK, 2017. http://rave.ohiolink.edu/etdc/view?acc_num=case1496677783008987.

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Wang, Lixum. "The use of parallel texts in language learning : computer software and teaching materials for English and Chinese." Thesis, University of Birmingham, 2000. http://ethos.bl.uk/OrderDetails.do?uin=uk.bl.ethos.368990.

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Sugg, Jennifer Buehler. "Nurse Educators' Perspectives of Supplemental Computer-Assisted Formative Assessment in an Associate Degree Nursing Program." ScholarWorks, 2015. https://scholarworks.waldenu.edu/dissertations/606.

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Despite the implementation of various strategies to improve outcomes, the pass rates for the National Council Licensure Exam for Registered Nurses (NCLEX-RN) for an associate degree nursing (ADN) program continue to decrease. This study examined the use of a supplemental computer-assisted formative assessment (SCAFA) as a strategy for NCLEX-RN success. A qualitative case study with a theoretical framework based on constructivism was designed to investigate nurse educators' perspectives of this particular strategy for successful outcomes. To explore these perspectives, data were collected from face-to-face interviews with nurse educators and from program documents from 1 ADN program in the southeastern United States. Guiding research questions explored nurse educators' perceptions of SCAFA and determined if and how data from these assessments were utilized. The data were analyzed using lean coding to determine emerging themes. The findings showed that a lack of consistency in the use of this tool diminishes the effectiveness of this supplemental strategy. Additional themes that emerged: educator and student attitudes, orientation and SCAFA process, resource allocation, training and preparation, and data-driven decision making. These findings were used to design a professional development project focused on the effective use of SCAFA throughout the nursing program. The study and project are expected to promote positive social change by contributing to the body of evidence on computer-assisted formative assessment, bolstering student and nurse educator learning, increasing the number of nursing students who are prepared to successfully pass the NCLEX-RN, improving program outcomes, and contributing to the professional nursing workforce.

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Singley, Bradford G. "Computer-Based Instruction for Engineering Education in the Developing World." Diss., CLICK HERE for online access, 2007. http://contentdm.lib.byu.edu/ETD/image/etd2090.pdf.

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Snow, Janet P. "Information and communication technology driven teaching and learning opportunities in support of environmental education processes : a case of the eno-environment online programme at Treverton Preparatory School, South Africa /." Thesis, Rhodes University, 2008. http://eprints.ru.ac.za/1623/.

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Martí, Juan Gerard. "Data-driven methods to characterize heterogeneity in Alzheimer’s disease using cross-sectional and longitudinal data." Doctoral thesis, Universitat Pompeu Fabra, 2021. http://hdl.handle.net/10803/671269.

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Alzheimer's disease (AD), the most common form of dementia, is an incurable neurodegenerative disease that affects millions of elderly people worldwide. Detecting the disease in its early stages is the key for a more effective treatment. AD is a multifactorial disease, where several biomarkers represent different pathophysiological processes in the brain, with distinct progression paths over time. Methods to facilitate the integration and interpretation of longitudinal, heterogeneous medical data could be of benefit for a better understanding of the disease and its progression. In this thesis, we present statistical and machine learning methods and studies for early detection and to assess disease progression. Contributions of this thesis are as follows: First, we present a review on machine learning applications in AD using longitudinal neuroimaging data: we analyze their approach to typical challenges in longitudinal data analysis and show that machine learning methods using this type of data have potential to improve disease progression modelling and computer-aided diagnosis. Our second contribution is a study of AD subtyping using novel plasma-based blood biomarkers. We used a multivariate, unsupervised multiple kernel learning method over blood-based biomarkers to find subgroups of patients defined by distinctive blood biomarker profiles, and we analyze those subgroups using cross-sectional and longitudinal neuroimaging data. Our third contribution is a novel method based on recurrent, multimodal variational autoencoders to model the progression of the disease. It can use a variable number of modalities and time-points across different subjects, and we show its performance quantitatively and qualitatively. Our fourth and final contribution is an analysis of the impact of APOE ε4 gene dose and its association with age on hippocampal shape, assessed with multivariate surface analysis, using a cognitive healthy, ε4-enriched cohort.<br>La malaltia d'Alzheimer, o simplement Alzheimer, és una malaltia neurodegenerativa incurable que afecta milions de persones a tot el món. La detecció de la malaltia en els seus primers estadis és clau per a un tractament més eficaç. L’Alzheimer és una malaltia multifactorial, on diversos marcadors representen processos fisiopatològics diferents al cervell, amb diferents tipus de progressió al llarg del temps. Mètodes per facilitar la integració i la interpretació de dades mèdiques longitudinals i heterogènies poden ser útils per aconseguir una millor comprensió de la malaltia i la seva progressió. En aquesta tesi presentem mètodes estadístics i d’aprenentatge automàtic per a la detecció precoç i per avaluar la progressió de la malaltia. Les contribucions d’aquesta tesi són les següents: En primer lloc, presentem una revisió de mètodes d’aprenentatge automàtic aplicats a l'Alzheimer que utilitza dades de neuroimatge longitudinals: analitzem el seu enfocament als desafiaments típics en l’anàlisi d'aquestes dades i demostrem que els mètodes d’aprenentatge automàtic que utilitzen dades longitudinals tenen potencial per a millorar la modelització de la progressió de la malaltia i el diagnòstic assistit per ordinador. La nostra segona contribució és un estudi de subtipatge de l'Alzheimer mitjançant biomarcadors sanguinis basats en plasma. Utilitzem un mètode multivariat i no supervisat en biomarcadors sanguinis per trobar subgrups de pacients definits per perfils distintius d'aquests biomarcadors, i analitzem aquests subgrups utilitzant dades de neuroimatge transversal i longitudinal. La nostra tercera contribució és un mètode basat en "recurrent variational autoencoders", un tipus de xarxa neuronal, per modelar la progressió de la malaltia. El mètode utilitza un nombre variable de modalitats i adquisicions en diferents pacients, i mostrem el seu rendiment quantitativament i qualitativament. La nostra quarta i última contribució és una anàlisi de l’impacte del gen APOE ε4 i la seva associació amb l’edat en la superfície de l’hipocamp, evaluada amb un anàlisi multivariat sobre una cohort amb alta proporció de pacients sans amb el gen ε4.

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Diaw, Samba. "Spem4mde : un métamodèle et un environnement pour la modélisation et la mise en œuvre assistée de processus IDM." Phd thesis, Université Toulouse le Mirail - Toulouse II, 2011. http://tel.archives-ouvertes.fr/tel-00668956.

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L'avènement de l'IDM (Ingénierie Dirigée par les Modèles) a suscité beaucoup d'intérêt de la part des organisations qui de fait commencent à transformer leur processus de développement traditionnel en un processus de développement dirigé par les modèles, appelé aussi processus IDM.Au moment où ces processus commencent à émerger, nous notons l'absence d'un langage dédié pour les modéliser et les mettre en œuvre. Le standard SPEM 2.0 propose des concepts génériques qui sont supposés être capables de décrire tout type de processus logiciel. Cependant, les concepts de SPEM ne capturent pas la nature exacte des processus IDM. D'autre part, une autre insuffisance majeure de SPEM réside dans le fait qu'il n'intègre pas les concepts relatifs à la mise en œuvre des processus.L'objectif de cette thèse est triple : (1) proposer une extension de SPEM dans laquelle les concepts centraux des processus IDM sont réifiés ; (2) proposer un langage dédié à la modélisation comportementale des processus IDM ; (3) proposer une architecture conceptuelle d'un environnement logiciel d'aide à la modélisation et à la mise en œuvre des processus IDM.Pour valider notre approche, un prototype a été développé sous l'environnement TOPCASED. Ce prototype fournit d'une part un éditeur graphique pour la modélisation structurelle et comportementale des processus IDM et d'autre part un environnement de mise en œuvre s'appuyant sur les modèles comportementaux des processus. Nous avons également appliqué notre approche à une étude de cas significatif: le processus UWE (UML-based Web Engineering), qui est un processus IDM dédié au développement d'applications web.

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Swan, Richard Heywood. "Deriving Operational Principles for the Design of Engaging Learning Experiences." Diss., CLICK HERE for online access, 2008. http://contentdm.lib.byu.edu/ETD/image/etd2573.pdf.

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Badin, Julien. "Ingénierie hautement productive et collaborative à base de connaissances métier : vers une méthodologie et un méta-modèle de gestion des connaissances en configurations." Phd thesis, Université de Technologie de Belfort-Montbeliard, 2011. http://tel.archives-ouvertes.fr/tel-00911883.

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Ces travaux de recherche concernent le domaine de l'ingénierie des connaissances pour laconception de produits et plus particulièrement les phases amont du couple produit-simulation dansle processus de conception.Au cours de ces différentes phases amont, les acteurs d'un même projet utilisent simultanément denombreuses modélisations géométriques et comportementales du produit. Ils peuvent aussi avoirrecours à plusieurs outils logiciels hétérogènes, communiquant très difficilement entre eux.Dans ce contexte, le partage des connaissances entre les différents modèles métiers apparait commeune nécessité. En effet, concevoir un produit implique une gestion des connaissances d'unegranularité fine en tenant compte de leur niveau de maturité et de leur cohérence.Le recours à de nouvelles méthodes et de nouveaux outils est alors nécessaires dans le but desoutenir l'approche globale PLM et continuer à optimiser et rationaliser le processus de conceptionde produits.Dans ce cadre, nous proposons une approche qualifiée de KCM - Knowledge ConfigurationManagement, basée sur la gestion des connaissances de granularité fine, en configurations. Cetteapproche est de nature à favoriser la collaboration entre les acteurs d'un projet, en améliorant lacapitalisation, la traçabilité, la réutilisation et la cohérence des connaissances, utiliséessimultanément dans plusieurs activités en parallèle du processus de conception.Les principaux résultats de notre travail de recherche se structurent autour de trois axes :* Une méthodologie de gestion des connaissances en configurations qualifiée de KCMethod.* Un méta-modèle, baptisé KCModel, de structuration des concepts manipulés parKCMethod.* Une maquette de faisabilité sous forme d'outil logiciel ADES, permettant d'expérimenteret valider notre approche.L'ensemble des résultats obtenus s'articule autour d'une solution logicielle de nouvelle génération,qualifiée de KCManager, permettant de déployer en entreprise l'ensemble de la démarche proposée.

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Wu, Shi-Yi, and 吳師毅. "A Study on Vision-Based Computer Assisted Driving by Omni-directional Images." Thesis, 2005. http://ndltd.ncl.edu.tw/handle/77317539018618327655.

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碩士<br>國立交通大學<br>資訊科學系所<br>93<br>A vision-based computer assisted driving system with the capabilities of analyzing vehicle movements and tracking neighboring vehicles using an omni-directional camera is proposed. A vehicle with computation and image grabbing capabilities is used as a test bed. An automatic learning process is designed for extraction of the features in grabbed images without manual instructions. Through this learning process, useful features for analyzing the vehicle movement and tracking neighboring vehicles are obtained. A vehicle movement analysis method is used to detect and analyze the current vehicle movement. Also, a vehicle tracking method is proposed to detect and track neighboring vehicles surround our vehicle. Furthermore, a risk condition detection method using a finite state-transition model is proposed to detect risk conditions in car driving. Finally, experimental results showing the flexibility of the proposed methods for computer-assisted driving in outdoor environment are also included. ii

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Books on the topic "COMPUTER ASSISTED DRIVING"

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Donaghy, Catherine. A command-driven computer assisted learning package (ComCAL). The author], 1985.

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Organisation for Economic Co-operation and Development, ed. Inspired by technology, driven by pedagogy: A systematic approach to technology-based school innovations. OECD, 2010.

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Kerckhoffs, Roy C. P. Patient specific modeling of the cardiovascular system: Technology-driven personalized medicine. Springer, 2010.

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Gray, Tracy, and Heidi Silver-Pacuilla. Breakthrough Teaching and Learning: How Educational and Assistive Technologies are Driving Innovation. Springer, 2014.

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Gray, Tracy, and Heidi Silver-Pacuilla. Breakthrough Teaching and Learning: How Educational and Assistive Technologies Are Driving Innovation. Springer, 2011.

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Martens, Luise. Konzeption Einer Simulatorgestuetzten Lkw-Fahrer-Ausbildung MIT Automatisch Ablaufenden Uebungen. Lang GmbH, Internationaler Verlag der Wissenschaften, Peter, 2001.

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Fuchs, Kevin, and Peter A. Henning. Computer-Driven Instructional Design with INTUITEL. River Publishers, 2022.

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Fuchs, Kevin, and Peter A. Henning. Computer-Driven Instructional Design with INTUITEL. River Publishers, 2022.

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Fuchs, Kevin, and Peter A. Henning. Computer-Driven Instructional Design with INTUITEL. River Publishers, 2022.

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Computer-Driven Instructional Design with INTUITEL: An Intelligent Tutoring Interface for Technology-Enhanced Learning. River Publishers, 2017.

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Book chapters on the topic "COMPUTER ASSISTED DRIVING"

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Lavallee, S., P. Cinquin, J. Demongeot, A. L. Benabid, I. Marque, and M. Djaid. "Computer Assisted Driving of a Needle into the Brain." In CAR’89 Computer Assisted Radiology / Computergestützte Radiologie. Springer Berlin Heidelberg, 1989. http://dx.doi.org/10.1007/978-3-642-52311-3_76.

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Sotelo, Miguel Ángel, Miguel Ángel García, and Ramón Flores. "Vision Based Intelligent System for Autonomous and Assisted Downtown Driving." In Computer Aided Systems Theory - EUROCAST 2003. Springer Berlin Heidelberg, 2003. http://dx.doi.org/10.1007/978-3-540-45210-2_30.

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Hina, Manolo Dulva, Andrea Ortalda, Assia Soukane, and Amar Ramdane-Cherif. "Machine Learning-Assisted Cognition of Driving Context and Avoidance of Road Obstacles." In Communications in Computer and Information Science. Springer International Publishing, 2020. http://dx.doi.org/10.1007/978-3-030-49559-6_7.

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García-Garrido, Miguel Ángel, Miguel Ángel Sotelo, and Ernesto Martín-Gorostiza. "Fast Road Sign Detection Using Hough Transform for Assisted Driving of Road Vehicles." In Lecture Notes in Computer Science. Springer Berlin Heidelberg, 2005. http://dx.doi.org/10.1007/11556985_71.

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Pacini, Federico, and Luca Fanucci. "Design and Preliminary Validation of an Assisted Driving System for Obstacle Avoidance Based on Reinforcement Learning Applied to Electrified Wheelchairs." In Lecture Notes in Computer Science. Springer Nature Switzerland, 2024. http://dx.doi.org/10.1007/978-3-031-62849-8_51.

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de Weerd, Jacco, Esther Tan, and Slavi Stoyanov. "Fostering Interdisciplinary Knowledge Construction in Computer-Assisted Collaborative Concept Mapping." In Data Driven Approaches in Digital Education. Springer International Publishing, 2017. http://dx.doi.org/10.1007/978-3-319-66610-5_32.

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Berge, Christian Schulte zu, Ankur Kapoor, and Nassir Navab. "Orientation-Driven Ultrasound Compounding Using Uncertainty Information." In Information Processing in Computer-Assisted Interventions. Springer International Publishing, 2014. http://dx.doi.org/10.1007/978-3-319-07521-1_25.

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Mollard, R. "Shape modeling driven by the product design." In CARS 2002 Computer Assisted Radiology and Surgery. Springer Berlin Heidelberg, 2002. http://dx.doi.org/10.1007/978-3-642-56168-9_93.

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Hadley, Gregory. "Reading and Data-Driven Learning." In The Palgrave Encyclopedia of Computer-Assisted Language Learning. Springer Nature Switzerland, 2024. https://doi.org/10.1007/978-3-031-51447-0_64-1.

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Hirschi, Kevin, and Okim Kang. "Pronunciation and Data-Driven Learning." In The Palgrave Encyclopedia of Computer-Assisted Language Learning. Springer Nature Switzerland, 2025. https://doi.org/10.1007/978-3-031-51447-0_161-1.

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Conference papers on the topic "COMPUTER ASSISTED DRIVING"

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Zhang, Zheyuan. "The Aircraft Assisted Driving Method Based on Machine Vision." In 2024 3rd International Conference on Automation, Robotics and Computer Engineering (ICARCE). IEEE, 2024. https://doi.org/10.1109/icarce63054.2024.00032.

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Liu, Kai, and Xiaojun Song. "Study of Assisted Driving System Based on Multi-Sensor Information Fusion." In 2024 IEEE 2nd International Conference on Image Processing and Computer Applications (ICIPCA). IEEE, 2024. http://dx.doi.org/10.1109/icipca61593.2024.10709186.

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He, Chuangxin, Qiang Zhang, Lei Gong, Zhonghua Miao, and Teng Sun. "Research on Assisted Driving Method for Cotton Pickers Based on RTK-GPS." In 2024 5th International Conference on Intelligent Computing and Human-Computer Interaction (ICHCI). IEEE, 2024. https://doi.org/10.1109/ichci63580.2024.10807916.

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Dai, Jiarong, Jinsheng Wang, Yuanyuan Lv, Rui Zheng, Fengyue Chen, and Liping Wu. "Research on Intelligent Lane Changing Control System for Assisted Autonomous Driving Vehicles of Different Styles." In 2024 6th International Conference on Artificial Intelligence and Computer Applications (ICAICA). IEEE, 2024. https://doi.org/10.1109/icaica63239.2024.10823041.

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Doula, Achref, Max Mühlhäuser, and Alejandro Sanchez Guinea. "AR-CP: Uncertainty-Aware Perception in Adverse Conditions with Conformal Prediction and Augmented Reality For Assisted Driving." In 2024 IEEE/CVF Conference on Computer Vision and Pattern Recognition Workshops (CVPRW). IEEE, 2024. http://dx.doi.org/10.1109/cvprw63382.2024.00026.

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Kuchinski, E. J., D. B. Anderson, H. Mindlin, B. C. Syrett, and R. Pathania. "EDEAC-PC: EPRI Database on Environmentally Assisted Cracking in Nuclear Components." In CORROSION 1992. NACE International, 1992. https://doi.org/10.5006/c1992-92109.

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Abstract The EDEAC-PC computer program incorporates a versatile database which includes measurements for over 3000 fatigue crack growth tests on various materials in nuclear power plant environments. The program facilitates data searching and display through a menu driven interface. Data for ferritic steels, stainless steels and nickel-base alloys are included and can be searched to assess effects of selected test conditions on crack growth rates. Guidance is also provided for external assessment of duty load spectra, analysis of nondestructive inspection data and other analytical procedures.

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Tian, Yuhe, Ayooluwa Akintola, Yazhou Jiang, et al. "Reinforcement Learning-Driven Process Design: A Hydrodealkylation Example." In Foundations of Computer-Aided Process Design. PSE Press, 2024. http://dx.doi.org/10.69997/sct.119603.

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In this work, we present a follow-up work of reinforcement learning (RL)-driven process design using the Institute for Design of Advanced Energy Systems Process Systems Engineering (IDAES-PSE) Framework. Herein, process designs are generated as stream inlet-outlet matrices and optimized using the IDAES platform, the objective function value of which is the reward to RL agent. Deep Q-Network is employed as the RL agent including a series of convolutional neural network layers and fully connected layers to compute the actions of adding or removing any stream connections, thus creating a new process design. The process design is then informed back to the RL agent to refine its learning. The iteration continues until the maximum number of steps is reached with feasible process designs generated. To further expedite the RL search of the design space which can comprise the selection of any candidate unit(s) with arbitrary stream connections, we investigate the role of RL reward function and their impacts on exploring more complicated versus intensified process configurations. A sub-space search strategy is also developed to branch the combinatorial design space to accelerate the discovery of feasible process design solutions particularly when a large pool of candidate process units is selected by the user. The potential of the enhanced RL-assisted process design strategy is showcased via a hydrodealkylation example.

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Zhang, Jinfeng, Fuqi Qiao, and Weihao Ma. "Data-Driven Analysis and Path Optimization Model for Fuel Consumption of Sail-Assisted Ships." In 2024 9th International Conference on Information Science, Computer Technology and Transportation (ISCTT). IEEE, 2024. https://doi.org/10.1109/isctt62319.2024.10875556.

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Wang, Yiqing, Mingming Song, Ye Xia, Wenjun Cao, and Limin Sun. "Joint Input-State Estimation Based on Recurrent Neural Network Assisted-Augmented Kalman Filter." In IABSE Symposium, Tokyo 2025: Environmentally Friendly Technologies and Structures: Focusing on Sustainable Approaches. International Association for Bridge and Structural Engineering (IABSE), 2025. https://doi.org/10.2749/tokyo.2025.0338.

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<p>The joint estimation of system states and unknown input loads in dynamic civil structures, based on limited observations, has garnered significant attention in recent years. A widely used method for this is the augmented Kalman filter (AKF), which extends the state vector to include unknown inputs, allowing for simultaneous state and input estimation. However, like the classical Kalman filter (KF), the AKF is highly sensitive to the tuning of hyperparameters—specifically, the covariance matrices of process and measurement noise—and to inaccuracies in the state-space model, which hampers its accuracy and robustness in practical applications. To address these challenges, this study proposes a recurrent neural network-assisted AKF (AKFNet) for joint input-response estimation. The AKFNet is a hybrid model that combines data-driven and model-driven approaches by incorporating a recurrent neural network (RNN) into the AKF's recursive framework. Rather than relying solely on state-space information to compute the Kalman gain, the RNN module learns to refine this computation from real data, thereby enhancing the filter's ability to integrate model predictions with measured data. This approach mitigates the limitations of traditional AKF methods in civil engineering, particularly the challenges posed by unknown noise covariances and model errors. The proposed AKFNet is thoroughly validated through both numerical simulations and experimental tests, demonstrating its superiority over the AKF for real-time state and input estimation from sparse response data, even in the presence of complex noise and model errors.</p>

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Uchino, Toru, Junichi Narisawa, Yuhei Sato, and Koji Kumazawa. "Multi-Joined Pile Driving Machine with a Computer Assisted Guiding System." In 10th International Symposium on Automation and Robotics in Construction. International Association for Automation and Robotics in Construction (IAARC), 1993. http://dx.doi.org/10.22260/isarc1993/0047.

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Reports on the topic "COMPUTER ASSISTED DRIVING"

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Edwards, Harvey M., and Rachel Markwald. Design and Integration of a Driving Simulator With Eye-Tracking Capabilities in the Computer Assisted Rehabilitation Environment (CAREN). Defense Technical Information Center, 2014. http://dx.doi.org/10.21236/ada612243.

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