Dissertations / Theses on the topic 'Intelligent vehicles'
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Aycard, Olivier. "Contribution to Perception for Intelligent Vehicles." Habilitation à diriger des recherches, Université de Grenoble, 2010. http://tel.archives-ouvertes.fr/tel-00545774.
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Perceiving or understanding the environment surrounding of a vehicle is a very important step in building driving assistant systems or autonomous vehicles. In this thesis, we focus on using laser scanner as a main perception sensor in context of dynamic outdoor environments. To solve this problem, we have to deal with 3 main tasks: (1) identify static part and dynamic entities moving in the environment, (2) use static part of the environment to build a map of the environment and localize the vehicle inside this map: this task is know as "Simultaneous Localization And Mapping" (SLAM) and finally (3) Detect And Track Moving Objects (DATMO). Regarding SLAM, the first contribution of this research is made by a grid-based approach\footnote{An occupancy grid is a decomposition of the environment in rectangular cells where each cell contains the probability that it is occupied by an obstacle.} to solve both problems of SLAM and detection of moving objects. To correct vehicle location from odometry we introduce a new fast incremental scan matching method that works reliably in dynamic outdoor environments. After good vehicle location is estimated, the surrounding map is updated incrementally and moving objects are detected without a priori knowledge of the targets. Our second contribution is an efficient, precise and multiscale representation of 2D/3D environment. This representation is actually an extension of occupancy grid where (1) only cells corresponding to occupied part of the environment are stored and updated (2) where cells are represented by a cluster of gaussian to have a fine representation of the environment and (3) where several occupancy grids are used to store and update a multiscale representation of the environment. Regarding DATMO, we firstly present a method of simultaneous detection, classification and tracking moving objects. A model-based approach is introduced to interpret the laser measurement sequence over a sliding window of time by hypotheses of moving object trajectories. The data-driven Markov chain Monte Carlo (DDMCMC) technique is used to explore the hypothesis space and effectively find the most likely solution. An other important problem to solve regarding DATMO is the definition of an appropriate dynamic model. In practice, objects can change their dynamic behaviors over time (e.g. : stopped, moving, accelerating, etc...). To adapt to these changing behaviors, a multiple dynamic model is generally required. But, this set of dynamic models and interactions between these models are always given a priori. Our second contribution on DATMO is a method to guide in the choice of motion models and in the estimation of interactions between these motion models. The last part of this thesis reports integration of these contributions on different experimental platforms in the framework of some national and european projects. Evaluations are presented which confirm the robustness and reliability of our contributions.
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Yamane, Hayato, Kazuya Tanaka, Nobutaka Kito, Daisuke Yamamoto, and Katashi Nagao. "Attentive Townvehicle : Communicating Personal Intelligent Vehicles." INTELLIGENT MEDIA INTEGRATION NAGOYA UNIVERSITY / COE, 2004. http://hdl.handle.net/2237/10353.
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Sörme, Jacob. "Intelligent Charging Algorithm for Electric Vehicles." Thesis, KTH, Skolan för elektroteknik och datavetenskap (EECS), 2020. http://urn.kb.se/resolve?urn=urn:nbn:se:kth:diva-280808.
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Electric vehicles play an important role in creating a fossil free transport sector. Making the vehicles efficient involves many new areas outside the manufacturing process, such as chargers, power grids and electricity markets. This thesis models the charging of electric vehicles using a Markov Decision Process and uses Reinforcement Learning solution models to derive an intelligent charging algorithm. This algorithm can take concepts such as electricity price, battery degradation and electrical losses into account in order to minimise the overall operational costs, and add more value to the use of electric vehicles. Models of how voltage varies in a battery is used and data on causes of battery degradation are derived from modern papers within battery technology. The intelligent charging algorithm is compared to baseline charging algorithms, one of which correspond to how charging is regularly performed today. Vehicle-to Grid is a promising future technology where electric vehicles can discharge some of their energy back to the grid in order to alleviate the stress of a power grid constrained by increasing demand as well an increasing penetration of intermittent sustainable sources of electricity such as wind and solar. Simulations are performed over scenarios with different electricity prices and the implications of being able to utilise Vehicle-to-Grid is studied. Results from simulations show that the intelligent charging algorithm effectively can reduce costs by approximately 30% on average compared to regular charging when the charging sessions last for 7 hours. Vehicle-to-Grid was seen to only be able to reduce costs in simulations with inexpensive batteries on days when there was a large difference in electricity price. The intelligent charging was able to save as much as 500 SEK for long charging sessions with expensive batteries, and powerful chargers. Results show a promising future for an intelligent charging algorithm to be used in order to improve the efficiency of electric vehicle charging.Elektriska fordon spelar en viktig roll för målet att skapa en tranportindustri som inte förlitar sig på fossila bränslen. Utmaningen att göra elektriska fordon så effektiva som möjligt innefattar många nya områden som ligger utanför det faktiska tillverkandet, som laddinfrastruktur, elnät och marknader för elektricitetshandel. Detta examensarbete modellerar laddning av elektriska fordon med Markov-beslutsprocesser och använder algoritmer från förstärkt inlärning för att ta fram en intelligent laddalgoritm. Denna algoritm kan ta indata från koncept som elpris och batteridegradering samt räkna med elektriska förluster, allt för att minska driftkostnad och göra det mer värdefullt att använda elfordon. Modeller för hur spänning varierar används och data för hur batterier degraderas används från moderna rapporter inom batteriteknologi. Den intelligenta laddalgoritmen jämförs med andra tillvägagångssätt att ladda, bland annat ett som motsvarar hur laddning ofta utförs idag. Vehicle-to-Grid är en lovande framtida teknologi som innebär att elektriska fordon kan ladda ur energi ur sina batterier och sälja tillbaka till elnätet för att reducera belastningar i nätet, dels på grund av ökad efterfrågan men också på grund av att elnätet i framtiden kan bestå av mindre pålitliga men förnyelsebara energikällor som solceller och vindkraft. Simuleringar körs över situationer med olika elpris och effekterna av att kunna använda Vehicle-to-Grid studeras. Resultat visar på att intelligent laddning kan spara ungefär 30% av kostnaderna i snitt. Simuleringarna visar att Vehicle-to-Grid endast kan spara kostnader då batterierna är billiga och då elpriset uppvisar stora variationer. Den intelligenta laddningsalgoritmen kun de spara upp till 500 SEK vid laddsessioner som varade en lång tid, med dyra batterier och med kraftfulla laddare. Resultaten visar på en lovande framtid för intelligenta laddalgorimer att användas för att öka effektiviteten inom laddning av elektriska fordon.
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Graham, James. "Intelligent power management for unmanned vehicles." Thesis, Loughborough University, 2015. https://dspace.lboro.ac.uk/2134/18026.
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Unmanned Air Vehicles (UAVs) are becoming more widely used in both military and civilian applications. Some of the largest UAVs have power systems equivalent to that of a military strike jet making power management an important aspect of their design. As they have developed, the amount of power needed for loads has increased. This has placed increase strain on the on-board generators and a need for higher reliability. In normal operation these generators are sized to be able to power all on-board systems with out overheating. Under abnormal operating conditions these generators may start to overheat, causing the loss of the generator's power output. The research presented here aims to answer two main questions: 1) Is it possible to predict when an overheat fault will occur based on the expected power usage defined by mission profiles? 2) Can an overheat fault be prevented while still allowing power to be distributed to necessary loads to allow mission completion? This is achieved by a load management algorithm, which adjusts the load profile for a mission, by either displacing the load to spare generators, or resting the generator to cool it down. The result is that for non-catastrophic faults the faulty generator does not need to be fully shut down and missions can continue rather than having to be aborted. This thesis presents the development of the load management system including the algorithm, prediction method and the models used for prediction. Ultimately, the algorithms developed are tested on a generator test rig. The main contribution of this work is the design of a prognostic load management algorithm. Secondary contributions are the use of a lumped parameter thermal model within a condition monitoring application, and the creation of a system identification model to describe the thermal dynamics of a generator.
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Li, Li Wang Fei-Yue. "Advanced motion control and sensing for intelligent vehicles." New York : Springer, 2007. http://www.myilibrary.com?id=113830.
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Nagao, Katashi, Kazutoshi Kozakai, Meguru Ito, Issei Naruta, and Shigeki Ohira. "Attentive Townvehicle Environment-Aware Personal Intelligent Vehicles." INTELLIGENT MEDIA INTEGRATION NAGOYA UNIVERSITY / COE, 2005. http://hdl.handle.net/2237/10371.
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Stephens, Michael. "Intelligent adaptive control of remotely operated vehicles." Thesis, University of Liverpool, 1993. http://ethos.bl.uk/OrderDetails.do?uin=uk.bl.ethos.240889.
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Chamitoff, Gregory Errol. "Robust intelligent flight control for hypersonic vehicles." Thesis, Massachusetts Institute of Technology, 1992. http://hdl.handle.net/1721.1/44275.
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Glenn, Bradley C. "Intelligent Control of Parallel Hybrid Electric Vehicles." The Ohio State University, 1999. http://rave.ohiolink.edu/etdc/view?acc_num=osu1391600950.
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Ozgunalp, Umar. "Vision based lane detection for intelligent vehicles." Thesis, University of Bristol, 2016. http://ethos.bl.uk/OrderDetails.do?uin=uk.bl.ethos.691261.
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Most vehicle accidents are due to driver error or slow reaction time. To prevent or minimize the consequences of these accidents, Advanced Driver Assistance Systems (ADAS) are introduced and lane detection is one of the most important building blocks of ADAS. Thus, the main focus of this thesis is lane detection. In this thesis, initially, lane detection algorithms based on a single camera as a sensor are investigated and proposed. First, an Inverse Perspective Mapping (IPM) based lane detection algorithm is proposed, where the global lane orientation and lane connectivity in this direction is exploited to increase the Signal to Noise Ratio (SNR). Using the initially estimated lane orientation, feature map is iteratively shifted and matched with itself to eliminate noise. Furthermore, based on the global lane orientation, an accurate, and linear Region of Interest (ROI) is efficiently formed using a I-D likelihood accumulator, where lane pairs are fitted to the feature points in estimated ROI. Later, an extension to the Symmetrical Local Threshold (SLT) is proposed for more accurate feature map extraction. Despite low computational complexity of the SLT, the algorithm outperformed all of the tested lane feature extractors in the Road Marking (ROMA) data sets. However, the main drawback of this algorithm is it cannot supply orientation information for the feature points. The proposed extension to the SLT, both reduced the noise (tested using ROMA data sets) , and outputs orientation information for the extracted feature points. Then, the extracted feature map and feature point orientations, are exploited for an efficient lane detection, where lane categorization is achieved by using a mask in the Hough domain. Although, single camera can be used for lane detection, single camera cannot supply depth information. Thus, many lane detection algorithms using single camera input are based on assumptions such as flat road assumption. However, 3D input can be utilized for lane detection application on non-flat roads.
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Knight, Peter Robin. "Artificial intelligence and mathematical models for intelligent management of aircraft data." Thesis, University of Southampton, 2012. https://eprints.soton.ac.uk/355717/.
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Increasingly, large volumes of aircraft data are being recorded in an effort to adapt aircraft maintenance procedures from being time-based towards condition-based techniques. This study uses techniques of artificial intelligence and develops mathematical models to analyse this data to enable improvements to be made in aircraft management, affordability, availability, airworthiness and performance. In addition, it highlights the need to assess the integrity of data before further analysis and presents the benefits of fusing all relevant data sources together. The research effort consists of three separate investigations that were undertaken and brought together in order to provide a unified set of methods aimed at providing a safe, reliable, effective and efficient overall procedure. The three investigations are: 1. The management of helicopter Health Usage Monitoring System (HUMS) Condition Indicators (CIs) and their analysis, using a number of techniques, including adaptive thresholds and clustering. These techniques were applied to millions of CI values from Chinook HUMS data. 2. The identification of fixed-wing turbojet engine performance degradation, using anomaly detection techniques, applied to thousands of in-service engine runs from Tornado aircraft. 3. The creation of models to identify unusual aircraft behaviour, such as uncommanded flight control movements. Two Chinook helicopter systems were modelled and the models were applied to over seven hundred in-service flights. In each case, the existing techniques were directed toward a condition-based maintenance approach, giving improved detection and earlier warning of faults.
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Zhou, Depu. "Real-time Animal Detection System for Intelligent Vehicles." Thesis, Université d'Ottawa / University of Ottawa, 2014. http://hdl.handle.net/10393/31272.
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Animal and Vehicle Collisions (AVCs) have been a growing concern in North America since the abundant wildlife resources and increases of automobiles. Such problems cause hundreds of people deaths, thousands of human injuries, billions of dollars in property damage and countless of wildlife deaths every year. To address these challenges, smart cars have to be equipped with Advanced Driver Assistance Systems (ADAS) able to detect dangerous animals (e.g., moose, elk and cow), which cross the road, and warn the driver about the imminent accident. In this thesis, we explore the performance of different image features and classification algorithms in animal detection application, and design a real-time animal detection system following three criteria: detection accuracy, detection time and system energy consumption. In order to pursue high detection rate
but low time and energy consumption, a double-stage detection system is proposed. In the first stage, we use the LBP adopting AdaBoost algorithm which provides the next stage by a set of region of interests containing target animals and other false positive targets. Afterward, the second stage rejects the false positive ROIs by two HOG-SVM based sub-classifiers. To build and evaluate the animal detector, we create our own database, which will be updated by adding new samples. Through an extensive set of evaluations, we note that the double-stage system is able to detect about 85% of target animals.
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LI, LI. "Issues in Control and Monitoring of Intelligent Vehicles." Diss., Tucson, Arizona : University of Arizona, 2005. http://etd.library.arizona.edu/etd/GetFileServlet?file=file:///data1/pdf/etd/azu%5Fetd%5F1218%5F1%5Fm.pdf&type=application/pdf.
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Wang, Yifei. "Advanced road and obstacle analysis for intelligent vehicles." Thesis, University of Bristol, 2012. http://ethos.bl.uk/OrderDetails.do?uin=uk.bl.ethos.556742.
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Road and obstacle analysis are two of the essential building blocks in both Driver Assistance Systems (DAS) and Autonomous Transportation Systems (ATS). Our research focus is to develop computationally efficient algorithm for accurate de- tection of the road boundaries and potential obstacles ba ed on prior knowledge of the highway and urban environments. In this thesis, a novel lane feature extrac- tion algorithm is introduced. It incorporates the global lane shape information to accurately extract feature points that overlap with the lane boundaries. It can be used a a general framework to improve or refine the feature map obtained with a diverse range of local feature extractors. At the lane tracking stage, the performance of the Gaussian Particle Filter (GPF), Gaussian Sum Particle Filter (GSPF) and Sampling Importance Resampling (SIR) particle filter are compared. The GSPF shows a preferable characteristic which is suitable for the lane track- ing application and leads to the best results. For motion-based obstacle detec- tion, we propose a computationally efficient image warping algorithm for motion compensation. This algorithm achieves higher efficiency as well as identical re- sults to perspective mapping based approaches. Furthermore, we investigated stereo vision based obstacle detection and developed a disparity calculation algo- rithm using multi-pass aggregation and local optimisation which utilises the prior knowledge of the traffic scene. This algorithm achieves comparable results to the global optimisation based algorithms with lower computational complexity. Dur- ing the obstacle detection stage, the G-Disparity image. which encloses disparity gradient information, is proposed. Using G-Disparity in conjunction with the -V-Di parity images allows more efficient obstacle extraction with performance improvement over the conventional U- V-Disparity based approaches.
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Zhao, Weihong. "A Novel Animal Detection Technique for Intelligent Vehicles." Thesis, Université d'Ottawa / University of Ottawa, 2018. http://hdl.handle.net/10393/38045.
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The animal-vehicle collision has been a topic of concern for years, especially in North America. To mitigate the problem, this thesis focuses on animal detection based on the onboard camera for intelligent vehicles.
In the domain of image classification and object detection, the methods of shape matching and local feature crafting have reached the technical plateau for decades. The development of Convolutional Neural Network (CNN) brings a new breakthrough. The evolution of CNN architectures has dramatically improved the performance of image classification. Effective frameworks on object detection through CNN structures are thus boosted. Notably, the family of Region-based Convolutional Neural Networks (R-CNN) perform well by combining region proposal with CNN. In this thesis, we propose to apply a new region proposal method|Maximally Stable Extremal Regions (MSER) in Fast R-CNN to construct the animal detection framework.
MSER algorithm detects stable regions which are invariant to scale, rotation and viewpoint changes. We generate regions of interest by dealing with the result of MSER algorithm in two ways: by enclosing all the pixels from the resulted pixel-list with a minimum enclosing rectangle (the PL MSER) and by fitting the resulted elliptical region to an approximate box (the EL MSER). We then preprocess the bounding boxes of PL MSER and EL MSER to improve the recall of detection. The preprocessing steps consist of filtering out undesirable regions by aspect ratio model, clustering bounding boxes to merge the overlapping regions, modifying and then enlarging the regions to cover the entire animal. We evaluate the two region proposal methods by the measurement of recall over IOU-threshold curve. The proposed MSER method can cover the expected regions better than Edge Boxes and Region Proposal Network (RPN) in Faster R-CNN. We apply the MSER region proposal method to the framework of R-CNN and Fast R-CNN. The experiments on the animal database with moose, deer, elk, and horses show that Fast R-CNN with MSER achieves better accuracy and faster speed than R-CNN with MSER. Concerning the two ways of MSER, the experimental results show that PL MSER is faster than EL MSER and EL MSER gains higher precision than PL MSER. Also, by altering the structure of network used in Fast R-CNN, we verify that network stacking more layers achieves higher accuracy and recall.
In addition, we compare the Fast R-CNN framework using MSER region proposal with the state-of-the-art Faster R-CNN by evaluating the experimental results of on our animal database. Using the same CNN structure, the proposed Fast R-CNN with MSER gains a higher average accuracy of the animal detection 0.73, compared to 0.42 of Faster R-CNN. In terms of detection quality, the proposed Fast R-CNN with MSER achieves better IoU histogram than that of Faster R-CNN.
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Alves, de Lima Danilo. "Sensor-based navigation applied to intelligent electric vehicles." Thesis, Compiègne, 2015. http://www.theses.fr/2015COMP2191/document.
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La navigation autonome des voitures robotisées est un domaine largement étudié avec plusieurs techniques et applications dans une démarche coopérative. Elle intègre du contrôle de bas niveau jusqu’à la navigation globale, en passant par la perception de l’environnement, localisation du robot, et autres aspects dans une approche référencée capteurs. Bien qu’il existe des travaux très avancés, ils présentent encore des problèmes et limitations liés aux capteurs utilisés et à l’environnement où la voiture est insérée.Ce travail aborde le problème de navigation des voitures robotisées en utilisant des capteurs à faible coût dans des milieux urbains. Dans cette thèse, nous avons traité le problème concernant le développement d’un système global de navigation autonome référencée capteur appliqué à un véhicule électrique intelligent, équipé avec des caméras et d’autres capteurs. La problématique traitée se décline en trois grands domaines de la navigation robotique : la perception de l’environnement, le contrôle de la navigation locale et la gestion de la navigation globale. Dans la perception de l’environnement, une approche de traitement d’image 2D et 3D a été proposé pour la segmentation de la route et des obstacles. Cette méthode est appliquée pour extraire aussi des caractéristiques visuelles, associées au milieu de la route, pour le contrôle de la navigation locale du véhicule. Avec les données perçues, une nouvelle méthode hybride de navigation référencée capteur et d’évitement d’obstacle a été appliquée pour le suivi de la route. Cette méthode est caractérisée par la validation d’une stratégie d’asservissement visuel (contrôleur délibératif) dans une nouvelle formulation de la méthode “fenêtre dynamique référencée image" (Dynamic Window Approach - DWA, en anglais) (contrôleur réactif). Pour assurer la navigation globale de la voiture, nous proposons l’association des données de cartes numériques afin de gérer la navigation locale dans les points critiques du chemin, comme les intersections de routes. Des essais dans les scénarios difficiles, avec une voiture expérimentale, et aussi en environnement simulé, montrent la viabilité de la méthode proposéeAutonomous navigation of car-like robots is a large domain with several techniques and applications working in cooperation. It ranges from low-level control to global navigation, passing by environment perception, robot localization, and many others in asensor-based approach. Although there are very advanced works, they still presenting problems and limitations related to the environment where the car is inserted and the sensors used. This work addresses the navigation problem of car-like robots based on low cost sensors in urban environments. For this purpose, an intelligent electric vehicle was equipped with vision cameras and other sensors to be applied in three big areas of robot navigation : the Environment Perception, Local Navigation Control, and Global Navigation Management. In the environment perception, a 2D and 3D image processing approach was proposed to segment the road area and detect the obstacles. This segmentation approach also provides some image features to local navigation control.Based on the previous detected information, a hybrid control approach for vision based navigation with obstacle avoidance was applied to road lane following. It is composed by the validation of a Visual Servoing methodology (deliberative controller) in a new Image-based Dynamic Window Approach (reactive controller). To assure the car’s global navigation, we proposed the association of the data from digital maps in order tomanage the local navigation at critical points, like road intersections. Experiments in a challenging scenario with both simulated and real experimental car show the viabilityof the proposed methodology
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Fraser, Robert James C. "Embedded command and control infrastructures for intelligent autonomous systems." Thesis, University of Southampton, 1994. https://eprints.soton.ac.uk/250158/.
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The issue of Command and Control (C2) is generally associated with the management infrastructure of large scale systems for warfare, public utilities and public transportation, and is concerned with ensuring that the distributed human elements of command and control can be fully integrated into a coherent, total system. Intelligent Autonomous Systems (IASs) are a class of complex systems that perform tasks autonomously in uncertain, dynamic environments, the management of which can be viewed from the perspective of embedded command and control systems. This thesis establishes a vision for the modular construction of intelligent autonomous embedded C2 systems, which defines a complex integration problem characterised by distributed intelligence, world knowledge and control, concurrent processing on heterogeneous platforms, and real-time performance requirements. It concludes that by adopting an appropriate systems infrastructure model, based on Object Technology, it is possible to view the construction of embedded C2 systems as the integration of a temporally assembled collection of reusable components. To support this metaphor it is necessary to construct a common reference model, or standards framework, for the representation and specification of modular C2 systems. This framework must support the coherent long term development and evolution in system capability, ensuring that systems are extensible, robust and perform correctly. In this research, which draws together the themes of other published research in object oriented systems and robotics, classical AI models for intelligent systems architectures are used to specify the overall system structure, with open systems technologies supporting the interoperation of elements within the architecture. All elements of this system are modelled in terms of objects, with well defined, implementation independent interfaces. This approach enables the system to be specified in terms of an object model, and the development process to be framed in terms of object technology, defining a new approach to IAS development. The implementation of an On-board Command and Control System for an Autonomous Underwater Vehicle is used to validate these concepts. The further application of emergent industrial standards in distributed object oriented systems means that this kind of component-based integration is scaleable, providing a near-term solution to generic command and control problems, including Computer Integrated Manufacturing and large scale autonomous systems, where individual autonomous systems, such as robots, form elements of a complete, total intelligent system, for application to areas such as fully automated factories and cooperating intelligent autonomous vehicles for construction sites.
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Rufus, Freeman Jr. "Intelligent approaches to mode transition control." Diss., Georgia Institute of Technology, 2000. http://hdl.handle.net/1853/13281.
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Foster, Christopher A. "Intelligent control study of drive-by-wire agricultural vehicles." Access to citation, abstract and download form provided by ProQuest Information and Learning Company; downloadable PDF file 3.19 Mb., 221 p, 2006. http://gateway.proquest.com/openurl?url_ver=Z39.88-2004&res_dat=xri:pqdiss&rft_val_fmt=info:ofi/fmt:kev:mtx:dissertation&rft_dat=xri:pqdiss:3221073.
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Grünwald, Norbert. "A Mixed-Reality Platform for Robotics and Intelligent Vehicles." Thesis, Högskolan i Halmstad, Intelligenta system (IS-lab), 2012. http://urn.kb.se/resolve?urn=urn:nbn:se:hh:diva-17856.
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Mixed Reality is the combination of the real world with a virtual one. In robotics thisopens many opportunities to improve the existing ways of development and testing. Thetools that Mixed Reality gives us, can speed up the development process and increasesafety during the testing stages. They can make prototyping faster and cheaper, and canboost the development and debugging process thanks to visualization and new opportunitiesfor automated testing.In this thesis the steps to build a working prototype demonstrator of a Mixed Realitysystem are covered. From selecting the required components, over integrating them intofunctional subsystems, to building a fully working demonstration system.The demonstrator uses optical tracking to gather information about the real world environment.It incorporates this data into a virtual representation of the world. This allowsthe simulation to let virtual and physical objects interact with each other. The results ofthe simulation are then visualized back into the real world.The presented system has been implemented and successfully tested at the HalmstadUniversity.
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Feng, Jingwen. "Traffic Sign Detection and Recognition System for Intelligent Vehicles." Thesis, Université d'Ottawa / University of Ottawa, 2014. http://hdl.handle.net/10393/31449.
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Road traffic signs provide instructions, warning information, to regulate driver behavior. In addition, these signs provide a reliable guarantee for safe and convenient driving. The Traffic Sign Detection and Recognition (TSDR) system is one of the primary applications for Advanced Driver Assistance Systems (ADAS). TSDR has obtained a great deal of attention over the recent years. But, it is still a challenging field of image processing.
In this thesis, we first created our own dataset for North American Traffic Signs, which is still being updated. We then decided to choose Histogram Orientation Gradients (HOG) and Support Vector Machines (SVMs) to build our system after comparing them with some other techniques. For better results, we tested different HOG parameters to find the best combination. After this, we developed a TSDR system using HOG, SVM and our new color information extraction algorithm. To reduce time-consumption, we used the Maximally Stable Extremal Region (MSER) to replace the HOG and SVM detection stage. In addition, we developed a new approach based on Global Positioning System (GPS) information other than image processing. At last, we tested these three systems; the results show that all of them can recognize traffic signs with a good accuracy rate. The MSER based system is faster than the one using only HOG and SVM; and, the GPS based system is even faster than the MSER based system.
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Pearson, Andrew Raymond. "Intelligent fault tolerant control schemes for autonomous underwater vehicles." Thesis, University of Plymouth, 2002. http://hdl.handle.net/10026.1/2098.
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The area of autonomous underwater vehicles (AUVs) is an increasingly important area of research, with AUVs being capable of handling a far wider range of missions than either an inhabited underwater vehicle or a remotely operated vehicle (ROV). One of the major drawbacks of such vehicles is the inability of their control systems to handle faults occurring within the vehicle during a mission. This study aims to develop enhancements to an existing control system in order to increase its fault tolerance to both sensor and actuator faults. Faults occurring within the sensors for both the yaw and roll channels of the AUV are considered. Novel fuzzy inference systems (FISs) are developed and tuned using both the adaptive neuro-fuzzy inference system (ANFIS) and simulated annealing tuning methods. These FISs allow the AUV to continue operating after a fault has occurred within the sensors. Faults occurring within the actuators which control the canards of the AUV and hence the yaw channel are also examined. Actuator recovery FISs capable of handling faults occurring within the actuators are developed using both the simulated annealing and tabu search methods of tuning FISs. The fault tolerance of the AUV is then further enhanced by the development of an error estimation FIS that is used to replace an error sensor. It concludes that the novel FISs designed and developed within the thesis provide an improved performance to both sensor and actuator faults in comparison to benchmark control systems. Therefore having these FISs embedded within the overall control scheme ensure the AUV is fault tolerant to a range of selected failures.
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Zhou, Yuteng. "Computer Vision System-On-Chip Designs for Intelligent Vehicles." Digital WPI, 2018. https://digitalcommons.wpi.edu/etd-dissertations/162.
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Intelligent vehicle technologies are growing rapidly that can enhance road safety, improve transport efficiency, and aid driver operations through sensors and intelligence. Advanced driver assistance system (ADAS) is a common platform of intelligent vehicle technologies. Many sensors like LiDAR, radar, cameras have been deployed on intelligent vehicles. Among these sensors, optical cameras are most widely used due to their low costs and easy installation. However, most computer vision algorithms are complicated and computationally slow, making them difficult to be deployed on power constraint systems. This dissertation investigates several mainstream ADAS applications, and proposes corresponding efficient digital circuits implementations for these applications. This dissertation presents three ways of software / hardware algorithm division for three ADAS applications: lane detection, traffic sign classification, and traffic light detection. Using FPGA to offload critical parts of the algorithm, the entire computer vision system is able to run in real time while maintaining a low power consumption and a high detection rate. Catching up with the advent of deep learning in the field of computer vision, we also present two deep learning based hardware implementations on application specific integrated circuits (ASIC) to achieve even lower power consumption and higher accuracy.
The real time lane detection system is implemented on Xilinx Zynq platform, which has a dual core ARM processor and FPGA fabric. The Xilinx Zynq platform integrates the software programmability of an ARM processor with the hardware programmability of an FPGA. For the lane detection task, the FPGA handles the majority of the task: region-of-interest extraction, edge detection, image binarization, and hough transform. After then, the ARM processor takes in hough transform results and highlights lanes using the hough peaks algorithm. The entire system is able to process 1080P video stream at a constant speed of 69.4 frames per second, realizing real time capability.
An efficient system-on-chip (SOC) design which classifies up to 48 traffic signs in real time is presented in this dissertation. The traditional histogram of oriented gradients (HoG) and support vector machine (SVM) are proven to be very effective on traffic sign classification with an average accuracy rate of 93.77%. For traffic sign classification, the biggest challenge comes from the low execution efficiency of the HoG on embedded processors. By dividing the HoG algorithm into three fully pipelined stages, as well as leveraging extra on-chip memory to store intermediate results, we successfully achieved a throughput of 115.7 frames per second at 1080P resolution. The proposed generic HoG hardware implementation could also be used as an individual IP core by other computer vision systems.
A real time traffic signal detection system is implemented to present an efficient hardware implementation of the traditional grass-fire blob detection. The traditional grass-fire blob detection method iterates the input image multiple times to calculate connected blobs. In digital circuits, five extra on-chip block memories are utilized to save intermediate results. By using additional memories, all connected blob information could be obtained through one-pass image traverse. The proposed hardware friendly blob detection can run at 72.4 frames per second with 1080P video input. Applying HoG + SVM as feature extractor and classifier, 92.11% recall rate and 99.29% precision rate are obtained on red lights, and 94.44% recall rate and 98.27% precision rate on green lights.
Nowadays, convolutional neural network (CNN) is revolutionizing computer vision due to learnable layer by layer feature extraction. However, when coming into inference, CNNs are usually slow to train and slow to execute. In this dissertation, we studied the implementation of principal component analysis based network (PCANet), which strikes a balance between algorithm robustness and computational complexity. Compared to a regular CNN, the PCANet only needs one iteration training, and typically at most has a few tens convolutions on a single layer. Compared to hand-crafted features extraction methods, the PCANet algorithm well reflects the variance in the training dataset and can better adapt to difficult conditions. The PCANet algorithm achieves accuracy rates of 96.8% and 93.1% on road marking detection and traffic light detection, respectively. Implementing in Synopsys 32nm process technology, the proposed chip can classify 724,743 32-by-32 image candidates in one second, with only 0.5 watt power consumption.
In this dissertation, binary neural network (BNN) is adopted as a potential detector for intelligent vehicles. The BNN constrains all activations and weights to be +1 or -1. Compared to a CNN with the same network configuration, the BNN achieves 50 times better resource usage with only 1% - 2% accuracy loss. Taking car detection and pedestrian detection as examples, the BNN achieves an average accuracy rate of over 95%. Furthermore, a BNN accelerator implemented in Synopsys 32nm process technology is presented in our work. The elastic architecture of the BNN accelerator makes it able to process any number of convolutional layers with high throughput. The BNN accelerator only consumes 0.6 watt and doesn't rely on external memory for storage.
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Lu, Wenjie. "Contributions to Lane Marking Based Localization for Intelligent Vehicles." Thesis, Paris 11, 2015. http://www.theses.fr/2015PA112017/document.
Full textAbstract:
Les applications pour véhicules autonomes et les systèmes d’aide avancée à la conduite (Advanced Driving Assistance Systems - ADAS) mettent en oeuvre des processus permettant à des systèmes haut niveau de réaliser une prise de décision. Pour de tels systèmes, la connaissance du positionnement précis (ou localisation) du véhicule dans son environnement est un pré-requis nécessaire. Cette thèse s’intéresse à la détection de la structure de scène, au processus de localisation ainsi qu’à la modélisation d’erreurs. A partir d’un large spectre fonctionnel de systèmes de vision, de l’accessibilité d’un système de cartographie ouvert (Open Geographical Information Systems - GIS) et de la large diffusion des systèmes de positionnement dans les véhicules (Global Positioning System - GPS), cette thèse étudie la performance et la fiabilité d’une méthode de localisation utilisant ces différentes sources. La détection de marquage sur la route réalisée par caméra monoculaire est le point de départ permettant de connaître la structure de la scène. En utilisant, une détection multi-noyau avec pondération hiérarchique, la méthode paramétrique proposée effectue la détection et le suivi des marquages sur la voie du véhicule en temps réel. La confiance en cette source d’information a été quantifiée par un indicateur de vraisemblance. Nous proposons ensuite un système de localisation qui fusionne des informations de positionnement (GPS), la carte (GIS) et les marquages détectés précédemment dans un cadre probabiliste basé sur un filtre particulaire. Pour ce faire, nous proposons d’utiliser les marquages détectés non seulement dans l’étape de mise en correspondance des cartes mais aussi dans la modélisation de la trajectoire attendue du véhicule. La fiabilité du système de localisation, en présence d’erreurs inhabituelles dans les différentes sources d’information, est améliorée par la prise en compte de différents indicateurs de confiance. Ce mécanisme est par la suite utilisé pour identifier les sources d’erreur. Cette thèse se conclut par une validation expérimentale des méthodes proposées dans des situations réelles de conduite. Leurs performances ont été quantifiées en utilisant un véhicule expérimental et des données en libre accès sur internetAutonomous Vehicles (AV) applications and Advanced Driving Assistance Systems (ADAS) relay in scene understanding processes allowing high level systems to carry out decision marking. For such systems, the localization of a vehicle evolving in a structured dynamic environment constitutes a complex problem of crucial importance. Our research addresses scene structure detection, localization and error modeling. Taking into account the large functional spectrum of vision systems, the accessibility of Open Geographical Information Systems (GIS) and the widely presence of Global Positioning Systems (GPS) onboard vehicles, we study the performance and the reliability of a vehicle localization method combining such information sources. Monocular vision–based lane marking detection provides key information about the scene structure. Using an enhanced multi-kernel framework with hierarchical weights, the proposed parametric method performs, in real time, the detection and tracking of the ego-lane marking. A self-assessment indicator quantifies the confidence of this information source. We conduct our investigations in a localization system which tightly couples GPS, GIS and lane makings in the probabilistic framework of Particle Filter (PF). To this end, it is proposed the use of lane markings not only during the map-matching process but also to model the expected ego-vehicle motion. The reliability of the localization system, in presence of unusual errors from the different information sources, is enhanced by taking into account different confidence indicators. Such a mechanism is later employed to identify error sources. This research concludes with an experimental validation in real driving situations of the proposed methods. They were tested and its performance was quantified using an experimental vehicle and publicly available datasets
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Zamzuri, Hairi. "Intelligent model-based robust control for tilting railway vehicles." Thesis, Loughborough University, 2008. https://dspace.lboro.ac.uk/2134/33896.
Full textAbstract:
High-speed trains have become one of the main means of public transportation around the world. The use of tilting train technologies on high-speed trains has contributed to cost effectiveness by reducing journey time between two places without the need to develop a new high-speed rail track infrastructure. Current technologies in tilting railway vehicles use a 'precedence' control scheme. This scheme uses a measurement from the front vehicle to capture 'precedence' information. Research on local sensor loop control strategies is still important to overcome the complexity of using precedence control technique. Work using conventional and modern control approaches has been investigated by previous researches. This study further extends these by investigating a particular intelligent control technique using fuzzy logic in designing the local feedback tilt control scheme.
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Gao, Yuan. "Depth information based object detection systems for intelligent vehicles." Thesis, University of Bristol, 2017. http://ethos.bl.uk/OrderDetails.do?uin=uk.bl.ethos.715749.
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Yang, Tao. "visual tracking and object motion prediction for intelligent vehicles." Thesis, Bourgogne Franche-Comté, 2019. http://www.theses.fr/2019UBFCA005.
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Le suivi d’objets et la prédiction de mouvement sont des aspects importants pour les véhicules autonomes. Tout d'abord, nous avons développé une méthode de suivi mono-objet en utilisant le compressive tracking, afin de corriger le suivi à base de flux optique et d’arriver ainsi à un compromis entre performance et vitesse de traitement. Compte tenu de l'efficacité de l'extraction de caractéristiques comprimées (compressive features), nous avons appliqué cette méthode de suivi au cas multi-objets pour améliorer les performances sans trop ralentir la vitesse de traitement. Deuxièmement, nous avons amélioré la méthode de suivi mono-objet basée sur DCF en utilisant des caractéristiques provenant d’un CNN multicouches, une analyse de fiabilité spatiale (via un masque d'objet) ainsi qu’une stratégie conditionnelle de mise à jour de modèle. Ensuite, nous avons appliqué la méthode améliorée au cas du suivi multi-objets. Les VGGNet-19 et DCFNet pré-entraînés sont testés respectivement en tant qu’extracteurs de caractéristiques. Le modèle discriminant réalisé par DCF est pris en compte dans l’étape d'association des données. Troisièmement, deux modèles LSTM (seq2seq et seq2dense) pour la prédiction de mouvement des véhicules et piétons dans le système de référence de la caméra sont proposés. En se basant sur des données visuelles et un nuage de points 3D (LiDAR), un système de suivi multi-objets basé sur un filtre de Kalman avec un détecteur 3D sont utilisés pour générer les trajectoires des objets à tester. Les modèles proposées et le modèle de régression polynomiale, considéré comme méthode de référence, sont comparés et évaluésObject tracking and motion prediction are important for autonomous vehicles and can be applied in many other fields. First, we design a single object tracker using compressive tracking to correct the optical flow tracking in order to achieve a balance between performance and processing speed. Considering the efficiency of compressive feature extraction, we apply this tracker to multi-object tracking to improve the performance without slowing down too much speed. Second, we improve the DCF based single object tracker by introducing multi-layer CNN features, spatial reliability analysis (through a foreground mask) and conditionally model updating strategy. Then, we apply the DCF based CNN tracker to multi-object tracking. The pre-trained VGGNet-19 and DCFNet are tested as feature extractors respectively. The discriminative model achieved by DCF is considered for data association. Third, two proposed LSTM models (seq2seq and seq2dense) for motion prediction of vehicles and pedestrians in the camera coordinate are proposed. Based on visual data and 3D points cloud (LiDAR), a Kalman filter based multi-object tracking system with a 3D detector are used to generate the object trajectories for testing. The proposed models, and polynomial regression model, considered as baseline, are compared for evaluation
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Plebe, Alice. "Cognitively Guided Modeling of Visual Perception in Intelligent Vehicles." Doctoral thesis, Università degli studi di Trento, 2021. http://hdl.handle.net/11572/299909.
Full textAbstract:
This work proposes a strategy for visual perception in the context of autonomous driving. Despite the growing research aiming to implement self-driving cars, no artificial system can claim to have reached the driving performance of a human, yet. Humans---when not distracted or drunk---are still the best drivers you can currently find. Hence, the theories about the human mind and its neural organization could reveal precious insights on how to design a better autonomous driving agent. This dissertation focuses specifically on the perceptual aspect of driving, and it takes inspiration from four key theories on how the human brain achieves the cognitive capabilities required by the activity of driving. The first idea lies at the foundation of current cognitive science, and it argues that thinking nearly always involves some sort of mental simulation, which takes the form of imagery when dealing with visual perception. The second theory explains how the perceptual simulation takes place in neural circuits called convergence-divergence zones, which expand and compress information to extract abstract concepts from visual experience and code them into compact representations. The third theory highlights that perception---when specialized for a complex task as driving---is refined by experience in a process called perceptual learning. The fourth theory, namely the free-energy principle of predictive brains, corroborates the role of visual imagination as a fundamental mechanism of inference. In order to implement these theoretical principles, it is necessary to identify the most appropriate computational tools currently available. Within the consolidated and successful field of deep learning, I select the artificial architectures and strategies that manifest a sounding resemblance with their cognitive counterparts. Specifically, convolutional autoencoders have a strong correspondence with the architecture of convergence-divergence zones and the process of perceptual abstraction. The free-energy principle of predictive brains is related to variational Bayesian inference and the use of recurrent neural networks. In fact, this principle can be translated into a training procedure that learns abstract representations predisposed to predicting how the current road scenario will change in the future. The main contribution of this dissertation is a method to learn conceptual representations of the driving scenario from visual information. This approach forces a semantic internal organization, in the sense that distinct parts of the representation are explicitly associated to specific concepts useful in the context of driving. Specifically, the model uses as few as 16 neurons for each of the two basic concepts here considered: vehicles and lanes. At the same time, the approach biases the internal representations towards the ability to predict the dynamics of objects in the scene. This property of temporal coherence allows the representations to be exploited to predict plausible future scenarios and to perform a simplified form of mental imagery. In addition, this work includes a proposal to tackle the problem of opaqueness affecting deep neural networks. I present a method that aims to mitigate this issue, in the context of longitudinal control for automated vehicles.
A further contribution of this dissertation experiments with higher-level spaces of prediction, such as occupancy grids, which could conciliate between the direct application to motor controls and the biological plausibility.
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Plebe, Alice. "Cognitively Guided Modeling of Visual Perception in Intelligent Vehicles." Doctoral thesis, Università degli studi di Trento, 2021. http://hdl.handle.net/11572/299909.
Full textAbstract:
This work proposes a strategy for visual perception in the context of autonomous driving. Despite the growing research aiming to implement self-driving cars, no artificial system can claim to have reached the driving performance of a human, yet. Humans---when not distracted or drunk---are still the best drivers you can currently find. Hence, the theories about the human mind and its neural organization could reveal precious insights on how to design a better autonomous driving agent. This dissertation focuses specifically on the perceptual aspect of driving, and it takes inspiration from four key theories on how the human brain achieves the cognitive capabilities required by the activity of driving. The first idea lies at the foundation of current cognitive science, and it argues that thinking nearly always involves some sort of mental simulation, which takes the form of imagery when dealing with visual perception. The second theory explains how the perceptual simulation takes place in neural circuits called convergence-divergence zones, which expand and compress information to extract abstract concepts from visual experience and code them into compact representations. The third theory highlights that perception---when specialized for a complex task as driving---is refined by experience in a process called perceptual learning. The fourth theory, namely the free-energy principle of predictive brains, corroborates the role of visual imagination as a fundamental mechanism of inference. In order to implement these theoretical principles, it is necessary to identify the most appropriate computational tools currently available. Within the consolidated and successful field of deep learning, I select the artificial architectures and strategies that manifest a sounding resemblance with their cognitive counterparts. Specifically, convolutional autoencoders have a strong correspondence with the architecture of convergence-divergence zones and the process of perceptual abstraction. The free-energy principle of predictive brains is related to variational Bayesian inference and the use of recurrent neural networks. In fact, this principle can be translated into a training procedure that learns abstract representations predisposed to predicting how the current road scenario will change in the future. The main contribution of this dissertation is a method to learn conceptual representations of the driving scenario from visual information. This approach forces a semantic internal organization, in the sense that distinct parts of the representation are explicitly associated to specific concepts useful in the context of driving. Specifically, the model uses as few as 16 neurons for each of the two basic concepts here considered: vehicles and lanes. At the same time, the approach biases the internal representations towards the ability to predict the dynamics of objects in the scene. This property of temporal coherence allows the representations to be exploited to predict plausible future scenarios and to perform a simplified form of mental imagery. In addition, this work includes a proposal to tackle the problem of opaqueness affecting deep neural networks. I present a method that aims to mitigate this issue, in the context of longitudinal control for automated vehicles.
A further contribution of this dissertation experiments with higher-level spaces of prediction, such as occupancy grids, which could conciliate between the direct application to motor controls and the biological plausibility.
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Bridgelall, Raj. "Pavement Performance Evaluation Using Connected Vehicles." Diss., North Dakota State University, 2015. http://hdl.handle.net/10365/25000.
Full textAbstract:
Roads deteriorate at different rates from weathering and use. Hence, transportation agencies must assess the ride quality of a facility regularly to determine its maintenance needs. Existing models to characterize ride quality produce the International Roughness Index (IRI), the prevailing summary of roughness. Nearly all state agencies use Inertial Profilers to produce the IRI. Such heavily instrumented vehicles require trained personnel for their operation and data interpretation. Resource constraints prevent the scaling of these existing methods beyond 4% of the network. This dissertation developed an alternative method to characterize ride quality that uses regular passenger vehicles. Smartphones or connected vehicles provide the onboard sensor data needed to enable the new technique. The new method provides a single index summary of ride quality for all paved and unpaved roads. The new index is directly proportional to the IRI. A new transform integrates sensor data streams from connected vehicles to produce a linear energy density representation of roughness. The ensemble average of indices from different speed ranges converges to a repeatable characterization of roughness. The currently used IRI is undefined at speeds other than 80 km/h. This constraint mischaracterizes roughness experienced at other speeds. The newly proposed transform integrates the average roughness indices from all speed ranges to produce a speed-independent characterization of ride quality. This property avoids spatial wavelength bias, which is a critical deficiency of the IRI. The new method leverages the emergence of connected vehicles to provide continuous characterizations of ride quality for the entire roadway network. This dissertation derived precision bounds of deterioration forecasting for models that could utilize the new index. The results demonstrated continuous performance improvements with additional vehicle participation. With practical traversal volumes, the achievable precision of forecast is within a few days. This work also quantified capabilities of the new transform to localize roadway anomalies that could pose travel hazards. The methods included derivations of the best sensor settings to achieve the desired performances. Several case studies validated the findings. These new techniques have the potential to save agencies millions of dollars annually by enabling predictive maintenance practices for all roadways, worldwide.Mountain Plains Consortium (MPC)
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Kubba, Ammar Issam Salih. "Intelligent tyre technologies." Thesis, University of Birmingham, 2018. http://etheses.bham.ac.uk//id/eprint/8363/.
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This thesis reports an investigation about range of intelligent tyre technologies. A piezoelectric transducer was utilized to measure tyre inner liner strain in the longitudinal direction. Tyre driving condition can be recognized from different tyre strain pattern data which is collected from the piezoelectric transducers. Tyre load, slip angle, and rolling speed can be identified from the tyre strain pattern throughout the tyre contact patch area strain pattern. A novel circumferential four-chamber tyre concept was designed, manufactured and tested in this study. These four-chamber prototypes can have independent inflation pressure in each chamber to provide wide range of tyre behavior which is suitable for different road surfaces and driving conditions. The multi-chamber tyre has a range of modes to accommodate urban and motorway driving for rolling resistance and grip control. A multi-chamber tyre finite element model using ABAQUS software was developed from a conventional tyre model to design and validate these tyre prototypes. These multi-chamber tyre prototypes were manufactured with the assistance of Fusion Innovations Ltd and tested in the University of Birmingham tyre test rig. Finally, a shape memory alloy valve design was investigated to control the air flow in the multi-chamber tyre.
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Lee, Sang Hyup. "A strategic vision of AVCS maglev and its socioeconomic implications." Diss., This resource online, 1994. http://scholar.lib.vt.edu/theses/available/etd-10052007-143432/.
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Hitchings, Mark R., and n/a. "Distance and Tracking Control for Autonomous Vehicles." Griffith University. School of Microelectronic Engineering, 1999. http://www4.gu.edu.au:8080/adt-root/public/adt-QGU20050902.084155.
Full textAbstract:
The author's concept of the distance and tracking control problem for autonomous vehicles relates to the cooperative behaviour of two successive vehicles travelling in the same environment. This behaviour requires one vehicle, designated the leader to move autonomously around it's environment with other vehicles, designated followers maintaining a coincident travel path and desired longitudinal distance with respect to the leader. Distance and tracking control is beneficial in numerous applications including guiding autonomous vehicles in Intelligent Transport Systems (ITS) which increases traffic safety and the capacity of pre-existing road infrastructure. Service robotics may also benefit from the cost savings and flexibility offered by distance and tracking control which enables a number of robots to cooperate together in order to achieve a task beyond the capabilities ofjust one robot. Using a distance and tracking control scheme an intelligent leader robot may guide a number of less intelligent (and therefore less costly and less complex) followers to a work-site to perform a task. The author's approach to the distance and tracking control problem consisted of two separate solutions - an initial solution used as a starting point and learning experience and a second, more robust, fuzzy control-based solution. This thesis briefly describes the initial solution, but places a greater emphasis on the second solution. The reason for this is that the fuzzy control-based solution offers significant improvement on the initial solution and was developed based on conclusions drawn from the initial solution. Most implementations of distance and tracking control, sometimes referred to as Intelligent Cruise Control (ICC) or platooning, are limited to longitudinal distance control only. The leader tracking control is performed either implicitly by a separate lane-following control system or by human drivers. The fuzzy control-based solution offered in this thesis performs both distance and tracking control of an autonomous follower vehicle with respect to a leader vehicle in front of it. It represents a simple and cost effective solution to the requirements of autonomous vehicles operating in ITS schemes - particularly close formation platooning. The follower tracks a laser signal emitted by the leader and monitors the distance to the follower at the same time using ultrasonic ranging techniques. The follower uses the data obtained from these measuring techniques as inputs to a fuzzy controller algorithm to adjust its distance and alignment with respect to the leader. Other systems employed on road vehicles utilise video-based leader tracking, or a range of lane-following methods such as magnetometer or video-based methods. Typically these methods are disadvantaged by substantial unit and/or infrastructure costs associated with their deployment. The limitations associated with the solutions presented in this thesis arise in curved trajectories at larger longitudinal distance separations between vehicles. The effects of these limitations on road vehicles has yet to be fully quantified, however it is thought that these effects would not disadvantage its use in close formation platooning. The fuzzy control-based distance and tracking control solution features two inputs, which are the distance and alignment of the follower with respect to the leader. The fuzzy controller asserts two outputs, which are left and right wheel velocities to control the speed and trajectory of a differential drive vehicle. Each of the input and output fuzzy membership functions has seven terms based around lambda, Z-type and S-type functions. The fuzzy rule base consists of 49 rules and the fuzzy inference stage is based on the MAX/MIN method. A Centre of Maximum (CoM) def'uzzification method is used to provide the two crisp valued outputs to the vehicle motion control. The methods chosen for the fuzzy control of distance and tracking for autonomous vehicles were selected based on a compromise between their computational complexity and performance characteristics. This compromise was necessary in order to implement the chosen controller structure on pre-existing hardware test beds based on an 8-bit microcontrollers with limited memory and processing resources. Overall the fuzzy control-based solution presented in this thesis effectively solves the distance and tracking control problem. The solution was applied to differential drive hardware test-beds and was tested to verify performance. The solution was thoroughly tested in both the simulation environment and on hardware test-beds. Several issues are identified in this thesis regarding the application of the solution to other platforms and road vehicle use. The solution will be shown to be directly portable to service robotics applications and, with minor modifications, applicable to road vehicle close-formation platooning.
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Hitchings, Mark. "Distance and Tracking Control for Autonomous Vehicles." Thesis, Griffith University, 1999. http://hdl.handle.net/10072/366396.
Full textAbstract:
The author's concept of the distance and tracking control problem for autonomous vehicles relates to the cooperative behaviour of two successive vehicles travelling in the same environment. This behaviour requires one vehicle, designated the leader to move autonomously around it's environment with other vehicles, designated followers maintaining a coincident travel path and desired longitudinal distance with respect to the leader. Distance and tracking control is beneficial in numerous applications including guiding autonomous vehicles in Intelligent Transport Systems (ITS) which increases traffic safety and the capacity of pre-existing road infrastructure. Service robotics may also benefit from the cost savings and flexibility offered by distance and tracking control which enables a number of robots to cooperate together in order to achieve a task beyond the capabilities ofjust one robot. Using a distance and tracking control scheme an intelligent leader robot may guide a number of less intelligent (and therefore less costly and less complex) followers to a work-site to perform a task. The author's approach to the distance and tracking control problem consisted of two separate solutions - an initial solution used as a starting point and learning experience and a second, more robust, fuzzy control-based solution. This thesis briefly describes the initial solution, but places a greater emphasis on the second solution. The reason for this is that the fuzzy control-based solution offers significant improvement on the initial solution and was developed based on conclusions drawn from the initial solution. Most implementations of distance and tracking control, sometimes referred to as Intelligent Cruise Control (ICC) or platooning, are limited to longitudinal distance control only. The leader tracking control is performed either implicitly by a separate lane-following control system or by human drivers. The fuzzy control-based solution offered in this thesis performs both distance and tracking control of an autonomous follower vehicle with respect to a leader vehicle in front of it. It represents a simple and cost effective solution to the requirements of autonomous vehicles operating in ITS schemes - particularly close formation platooning. The follower tracks a laser signal emitted by the leader and monitors the distance to the follower at the same time using ultrasonic ranging techniques. The follower uses the data obtained from these measuring techniques as inputs to a fuzzy controller algorithm to adjust its distance and alignment with respect to the leader. Other systems employed on road vehicles utilise video-based leader tracking, or a range of lane-following methods such as magnetometer or video-based methods. Typically these methods are disadvantaged by substantial unit and/or infrastructure costs associated with their deployment. The limitations associated with the solutions presented in this thesis arise in curved trajectories at larger longitudinal distance separations between vehicles. The effects of these limitations on road vehicles has yet to be fully quantified, however it is thought that these effects would not disadvantage its use in close formation platooning. The fuzzy control-based distance and tracking control solution features two inputs, which are the distance and alignment of the follower with respect to the leader. The fuzzy controller asserts two outputs, which are left and right wheel velocities to control the speed and trajectory of a differential drive vehicle. Each of the input and output fuzzy membership functions has seven terms based around lambda, Z-type and S-type functions. The fuzzy rule base consists of 49 rules and the fuzzy inference stage is based on the MAX/MIN method. A Centre of Maximum (CoM) def'uzzification method is used to provide the two crisp valued outputs to the vehicle motion control. The methods chosen for the fuzzy control of distance and tracking for autonomous vehicles were selected based on a compromise between their computational complexity and performance characteristics. This compromise was necessary in order to implement the chosen controller structure on pre-existing hardware test beds based on an 8-bit microcontrollers with limited memory and processing resources. Overall the fuzzy control-based solution presented in this thesis effectively solves the distance and tracking control problem. The solution was applied to differential drive hardware test-beds and was tested to verify performance. The solution was thoroughly tested in both the simulation environment and on hardware test-beds. Several issues are identified in this thesis regarding the application of the solution to other platforms and road vehicle use. The solution will be shown to be directly portable to service robotics applications and, with minor modifications, applicable to road vehicle close-formation platooning.Thesis (Masters)
Master of Philosophy (MPhil)
School of Microelectronic Engineering
Science, Environment, Engineering and Technology
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Velenis, Efstathios. "Analysis and Control of High-Speed Wheeled Vehicles." Diss., Georgia Institute of Technology, 2006. http://hdl.handle.net/1853/10476.
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In this work we reproduce driving techniques to mimic expert race drivers and obtain the open-loop control signals that may be used by auto-pilot agents driving autonomous ground wheeled vehicles.
Race drivers operate their vehicles at the limits of the acceleration envelope. An accurate characterization of the acceleration capacity of the vehicle is required. Understanding and reproduction of such complex maneuvers also require a physics-based mathematical description of the vehicle dynamics. While most of the modeling issues of ground-vehicles/automobiles are already well established in the literature, lack of understanding of the physics associated with friction generation results in ad-hoc approaches to tire friction modeling. In this work we revisit this aspect of the overall vehicle modeling and develop a tire friction model that provides physical interpretation of the tire forces. The new model is free of those singularities at low vehicle speed and wheel angular rate that are inherent in the widely used empirical static models. In addition, the dynamic nature of the tire model proposed herein allows the study of dynamic effects such as transients and hysteresis.
The trajectory-planning problem for an autonomous ground wheeled vehicle is formulated in an optimal control framework aiming to minimize the time of travel and maximize the use of the available acceleration capacity. The first approach to solve the optimal control problem is using numerical techniques. Numerical optimization allows incorporation of a vehicle model of high fidelity and generates realistic solutions. Such an optimization scheme provides an ideal platform to study the limit operation of the vehicle, which would not be possible via straightforward simulation. In this work we emphasize the importance of online applicability of the proposed methodologies. This underlines the need for optimal solutions that require little computational cost and are able to incorporate real, unpredictable environments. A semi-analytic methodology is developed to generate the optimal velocity profile for minimum time travel along a prescribed path. The semi-analytic nature ensures minimal computational cost while a receding horizon implementation allows application of the methodology in uncertain environments. Extensions to increase fidelity of the vehicle model are finally provided.
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Tuul, Viktor, and John Dahlberg. "Intelligent Traffic Intersection Management Using Motion Planning for Autonomous Vehicles." Thesis, KTH, Skolan för teknikvetenskap (SCI), 2017. http://urn.kb.se/resolve?urn=urn:nbn:se:kth:diva-214714.
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With the increasing advances in the field of autonomousvehicles it is alluring to ask if a possible vehicularparadigm shift is in the near future. Maximizing road capacitywith Intelligent Traffic Intersections that communicate withautonomous vehicles could become a reality, where the needfor traffic lights and stop signs is excluded. In this paper, anAutonomous Intersection Management system is introduced thatutilizes trajectory-based prioritization and motion planning techniquesto manage traffic in an orthogonal single lane four-wayintersection. The developed system reduces the need for vehiclesto slow down or even stop before intersections, contrariwise, itlets all vehicles enter the intersection at the highest allowed speed.The proposed solution is shown to increase the capacity of intersectionscompared with contemporary intersections managedwith traffic lights.
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Al-Hasan, Sami A. "Intelligent approaches to mission planning and control for autonomous vehicles." Diss., Georgia Institute of Technology, 1999. http://hdl.handle.net/1853/15414.
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Wei, Chuliang. "A CAN network based intelligent monitoring system for automotive vehicles." Thesis, University of Liverpool, 2006. http://ethos.bl.uk/OrderDetails.do?uin=uk.bl.ethos.428194.
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Road vehicles are the major pollution sources which seriously harm human health and the environment worldwide. A series of directives for limiting vehicle pollution have been introduced pillticularly in the EU and USA. Presently many investigations are being carried out in order to find out more effectively ways to reduce the vehicle pollution hy precise measurement of the vehicle pollution. Tllis thesis reports the details of the design illId construction of a CAN network based intelligent monitoring system for the distributed monitoring of vellicle pollution. The essential theory to SUppOlt the development of the CAN networks is given in this thesis. The system descrihed in this thesis monitors the vehicle engine vibration, temperature of the vehicle exhaust emissions, and the vehicle exhaust gases. The CAN networks have been developed for each suh-system so that the system can be incorporated into automotive vehicles. The system has been tested on a diesel engine and showed that the vihration and the temperature were accurately measured. The tests of the priI11ill'y set-ups of the gas monitoring suh-system showed that the exhaust gases could he detected, and the pollution levels of each exhaust gases could he precisely measured. This project has heen undertaken as pillt of an EC Framework Programme 6 STREP project (OPTO-EMI-SENSE) and the results are being reviewed by Centro Ricerche Fiat for installation on a new diesel engine car. Chuliang
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Zinoune, Clément. "Autonomous integrity monitoring of navigation maps on board intelligent vehicles." Thesis, Compiègne, 2014. http://www.theses.fr/2014COMP1972/document.
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Les véhicules dits intelligents actuellement développés par la plupart des constructeurs automobiles, ainsi que les véhicules autonomes nécessitent des informations sur le contexte dans lequel ils évoluent. Certaines de ces informations (par exemple la courbure de la route, la forme des intersections, les limitations de vitesses) sont fournies en temps réel par le système de navigation qui exploite les données de cartes routières numériques. Des défauts résultant de l’évolution du réseau routier ou d’imprécisions lors de la collecte de données peuvent être contenus dans ces cartes numériques et entraîner le dysfonctionnement des systèmes d’aide à la conduite. Les recherches menées dans cette thèse visent à rendre le véhicule capable d’évaluer, de manière autonome et en temps réel, l’intégrité des informations fournies par son système de navigation. Les véhicules de série sont désormais équipés d’un grand nombre de capteurs qui transmettent leurs mesures sur le réseau central interne du véhicule. Ces données sont donc facilement accessibles mais de faible précision. Le défi de cette thèse réside donc dans l’évaluation de l’intégrité des informations cartographiques malgré un faible degré de redondance et l’absence de données fiables. On s’adresse à deux types de défauts cartographiques : les défauts structurels et les défauts géométriques. Les défauts structurels concernent les connections entre les routes (intersections). Un cas particulier de défaut structurel est traité : la détection de ronds-points qui n’apparaissent pas dans la carte numérique. Ce défaut est essentiel car il est fréquent (surtout en Europe) et perturbe le fonctionnement des aides à la conduite. Les ronds-points sont détectés à partir de la forme typique de la trajectoire du véhicule lorsqu’il les traverse, puis sont mémorisés pour avertir les aides à la conduite aux prochains passages du véhicule sur la zone. Les imprécisions de représentation du tracé des routes dans la carte numérique sont quant à elles désignées comme défauts géométriques. Un formalisme mathématique est développé pour détecter ces défauts en comparant l’estimation de la position du véhicule d’après la carte à une autre estimation indépendante de la carte. Cette seconde estimation pouvant elle aussi être affectée par un défaut, les anciens trajetsdu véhicule sur la même zone sont utilisés. Un test statistique est finalement utilisé pour améliorer la méthode de détection de défauts géométriques dans des conditions de mesures bruitées. Toutes les méthodes développées dans le cadre de cette thèse sont évaluées à l’aide de données réellesSeveral Intelligent Vehicles capabilities from Advanced Driving Assistance Systems (ADAS) to Autonomous Driving functions depend on a priori information provided by navigation maps. Whilst these were intended for driver guidance as they store road network information, today they are even used in applications that control vehicle motion. In general, the vehicle position is projected onto the map to relate with links in the stored road network. However, maps might contain faults, leading to navigation and situation understanding errors. Therefore, the integrity of the map-matched estimates must be monitored to avoid failures that can lead to hazardous situations. The main focus of this research is the real-time autonomous evaluation of faults in navigation maps used in intelligent vehicles. Current passenger vehicles are equipped with proprioceptive sensors that allow estimating accurately the vehicle state over short periods of time rather than long trajectories. They include receiver for Global Navigation Satellite System (GNSS) and are also increasingly equipped with exteroceptive sensors like radar or smart camera systems. The challenge resides on evaluating the integrity of the navigation maps using vehicle on board sensors. Two types of map faults are considered: Structural Faults, addressing connectivity (e.g., intersections). Geometric Faults, addressing geographic location and road geometry (i.e. shape). Initially, a particular structural navigation map fault is addressed: the detection of roundabouts absent in the navigation map. This structural fault is problematic for ADAS and Autonomous Driving. The roundabouts are detected by classifying the shape of the vehicle trajectory. This is stored for use in ADAS and Autonomous Driving functions on future vehicle trips on the same area. Next, the geometry of the map is addressed. The main difficulties to do the autonomous integrity monitoring are the lack of reliable information and the low level of redundancy. This thesis introduces a mathematical framework based on the use of repeated vehicle trips to assess the integrity of map information. A sequential test is then developed to make it robust to noisy sensor data. The mathematical framework is demonstrated theoretically including the derivation of definitions and associated properties. Experiments using data acquired in real traffic conditions illustrate the performance of the proposed approaches
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Kurdej, Marek. "Exploitation of map data for the perception of intelligent vehicles." Thesis, Compiègne, 2015. http://www.theses.fr/2015COMP2174/document.
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La plupart des logiciels contrôlant les véhicules intelligents traite de la compréhension de la scène. De nombreuses méthodes existent actuellement pour percevoir les obstacles de façon automatique. La majorité d’entre elles emploie ainsi les capteurs extéroceptifs comme des caméras ou des lidars. Cette thèse porte sur les domaines de la robotique et de la fusion d’information et s’intéresse aux systèmes d’information géographique. Nous étudions ainsi l’utilité d’ajouter des cartes numériques, qui cartographient le milieu urbain dans lequel évolue le véhicule, en tant que capteur virtuel améliorant les résultats de perception. Les cartes contiennent en effet une quantité phénoménale d’information sur l’environnement : sa géométrie, sa topologie ainsi que d’autres informations contextuelles. Dans nos travaux, nous avons extrait la géométrie des routes et des modèles de bâtiments afin de déduire le contexte et les caractéristiques de chaque objet détecté. Notre méthode se base sur une extension de grilles d’occupations : les grilles de perception crédibilistes. Elle permet de modéliser explicitement les incertitudes liées aux données de cartes et de capteurs. Elle présente également l’avantage de représenter de façon uniforme les données provenant de différentes sources : lidar, caméra ou cartes. Les cartes sont traitées de la même façon que les capteurs physiques. Cette démarche permet d’ajouter les informations géographiques sans pour autant leur donner trop d’importance, ce qui est essentiel en présence d’erreurs. Dans notre approche, le résultat de la fusion d’information contenu dans une grille de perception est utilisé pour prédire l’état de l’environnement à l’instant suivant. Le fait d’estimer les caractéristiques des éléments dynamiques ne satisfait donc plus l’hypothèse du monde statique. Par conséquent, il est nécessaire d’ajuster le niveau de certitude attribué à ces informations. Nous y parvenons en appliquant l’affaiblissement temporel. Étant donné que les méthodes existantes n’étaient pas adaptées à cette application, nous proposons une famille d’opérateurs d’affaiblissement prenant en compte le type d’information traitée. Les algorithmes étudiés ont été validés par des tests sur des données réelles. Nous avons donc développé des prototypes en Matlab et des logiciels en C++ basés sur la plate-forme Pacpus. Grâce à eux nous présentons les résultats des expériences effectués en conditions réellesThis thesis is situated in the domains of robotics and data fusion, and concerns geographic information systems. We study the utility of adding digital maps, which model the urban environment in which the vehicle evolves, as a virtual sensor improving the perception results. Indeed, the maps contain a phenomenal quantity of information about the environment : its geometry, topology and additional contextual information. In this work, we extract road surface geometry and building models in order to deduce the context and the characteristics of each detected object. Our method is based on an extension of occupancy grids : the evidential perception grids. It permits to model explicitly the uncertainty related to the map and sensor data. By this means, the approach presents also the advantage of representing homogeneously the data originating from various sources : lidar, camera or maps. The maps are handled on equal terms with the physical sensors. This approach allows us to add geographic information without imputing unduly importance to it, which is essential in presence of errors. In our approach, the information fusion result, stored in a perception grid, is used to predict the stateof environment on the next instant. The fact of estimating the characteristics of dynamic elements does not satisfy the hypothesis of static world. Therefore, it is necessary to adjust the level of certainty attributed to these pieces of information. We do so by applying the temporal discounting. Due to the fact that existing methods are not well suited for this application, we propose a family of discoun toperators that take into account the type of handled information. The studied algorithms have been validated through tests on real data. We have thus developed the prototypes in Matlab and the C++ software based on Pacpus framework. Thanks to them, we present the results of experiments performed in real conditions
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Dahlberg, John Henry, and Viktor Tuul. "Intelligent Traffic Intersection Management Using Motion Planning for Autonomous Vehicles." Thesis, KTH, Skolan för teknikvetenskap (SCI), 2017. http://urn.kb.se/resolve?urn=urn:nbn:se:kth:diva-210895.
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With the increasing advances in the field of autonomousvehicles it is alluring to ask if a possible vehicularparadigm shift is in the near future. Maximizing road capacitywith Intelligent Traffic Intersections that communicate withautonomous vehicles could become a reality, where the needfor traffic lights and stop signs is excluded. In this paper, anAutonomous Intersection Management system is introduced thatutilizes trajectory-based prioritization and motion planning techniquesto manage traffic in an orthogonal single lane four-wayintersection. The developed system reduces the need for vehiclesto slow down or even stop before intersections, contrariwise, itlets all vehicles enter the intersection at the highest allowed speed.The proposed solution is shown to increase the capacity of intersectionscompared with contemporary intersections managedwith traffic lights.
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Chu, Kim-chiu. "Development of intelligent battery charger and controller for electric vehicle /." [Hong Kong : University of Hong Kong], 1989. http://sunzi.lib.hku.hk/hkuto/record.jsp?B12599074.
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Unsal, Cem. "Intelligent Navigation of Autonomous Vehicles in an Automated Highway System: Learning Methods and Interacting Vehicles Approach." Diss., Virginia Tech, 1997. http://hdl.handle.net/10919/30595.
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One of today's most serious social, economical and environmental problems is traffic congestion. In addition to the financial cost of the problem, the number of traffic related injuries and casualties is very high. A recently considered approach to increase safety while reducing congestion and improving driving conditions is Automated Highway Systems (AHS).
The AHS will evolve from the present highway system to an intelligent vehicle/highway system that will incorporate communication, vehicle control and traffic management techniques to provide safe, fast and more efficient surface transportation. A key factor in AHS deployment is intelligent vehicle control. While the technology to safely maneuver the vehicles exists, the problem of making intelligent decisions to improve a single vehicle's travel time and safety while optimizing the overall traffic flow is still a stumbling block.
We propose an artificial intelligence technique called stochastic learning automata to design an intelligent vehicle path controller. Using the information obtained by on-board sensors and local communication modules, two automata are capable of learning the best possible (lateral and longitudinal) actions to avoid collisions. This learning method is capable of adapting to the automata environment resulting from unmodeled physical environment. Simulations for simultaneous lateral and longitudinal control of an autonomous vehicle provide encouraging results. Although the learning approach taken is capable of providing a safe decision, optimization of the overall traffic flow is also possible by studying the interaction of the vehicles.
The design of the adaptive vehicle path planner based on local information is then carried onto the interaction of multiple intelligent vehicles. By analyzing the situations consisting of conflicting desired vehicle paths, we extend our design by additional decision structures. The analysis of the situations and the design of the additional structures are made possible by the study of the interacting reward-penalty mechanisms in individual vehicles. The definition of the physical environment of a vehicle as a series of discrete state transitions associated with a "stationary automata environment" is the key to this analysis and to the design of the intelligent vehicle path controller.
This work was supported in part by the Center for Transportation Research and Virginia DOT under Smart Road project, by General Motors ITS Fellowship program, and by Naval Research Laboratory under grant no. N000114-93-1-G022.Ph. D.
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朱劍超 and Kim-chiu Chu. "Development of intelligent battery charger and controller for electricvehicle." Thesis, The University of Hong Kong (Pokfulam, Hong Kong), 1989. http://hub.hku.hk/bib/B31209178.
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Sridhar, Srivatsan. "Cooperative Perception in Autonomous Ground Vehicles using a Mobile Robot Testbed." Thesis, Virginia Tech, 2017. http://hdl.handle.net/10919/88742.
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With connected and autonomous vehicles, no optimal standard or framework currently exists, outlining the right level of information sharing for cooperative autonomous driving. Cooperative Perception is proposed among vehicles, where every vehicle is transformed into a moving sensor platform that is capable of sharing information collected using its on-board sensors. This helps extend the line of sight and field of view of autonomous vehicles, which otherwise suffer from blind spots and occlusions. This increase in situational awareness promotes safe driving over a short range and improves traffic flow efficiency over a long range.
This thesis proposes a methodology for cooperative perception for autonomous vehicles over a short range. The problem of cooperative perception is broken down into sub-tasks of cooperative relative localization and map merging. Cooperative relative localization is achieved using visual and inertial sensors, where a computer-vision based camera relative pose estimation technique, augmented with position information, is used to provide a pose-fix that is subsequently updated by dead reckoning using an inertial sensor. Prior to map merging, a technique for object localization using a monocular camera is proposed that is based on the Inverse Perspective Mapping technique. A mobile multi-robot testbed was developed to emulate autonomous vehicles and the proposed method was implemented on the testbed to detect pedestrians and also to respond to the perceived hazard. Potential traffic scenarios where cooperative perception could prove crucial were tested and the results are presented in this thesis.MS
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Wilson, Malcolm Baxter. "Recognition of the immediate driving environment." Thesis, Cranfield University, 2002. http://ethos.bl.uk/OrderDetails.do?uin=uk.bl.ethos.250632.
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Brown, Ryan Charles. "IRIS: Intelligent Roadway Image Segmentation." Thesis, Virginia Tech, 2014. http://hdl.handle.net/10919/49105.
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The problem of roadway navigation and obstacle avoidance for unmanned ground vehicles has typically needed very expensive sensing to operate properly. To reduce the cost of sensing, it is proposed that an algorithm be developed that uses a single visual camera to image the roadway, determine where the lane of travel is in the image, and segment that lane. The algorithm would need to be as accurate as current lane finding algorithms as well as faster than a standard k- means segmentation across the entire image.
This algorithm, named IRIS, was developed and tested on several sets of roadway images. The algorithm was tested for its accuracy and speed, and was found to be better than 86% accurate across all data sets for an optimal choice of algorithm parameters. IRIS was also found to be faster than a k-means segmentation across the entire image. IRIS was found to be adequate for fulfilling the design goals for the algorithm. IRIS is a feasible system for lane identification and segmentation, but it is not currently a viable system. More work to increase the speed of the algorithm and the accuracy of lane detection and to extend the inherent lane model to more complex road types is needed. IRIS represents a significant step forward in the single camera roadway perception field.Master of Science
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Sandoval, Marcelo. "Electric vehicle-intelligent energy management system for frequency regulation application using a distributed, prosumer-based grid control architecture." Thesis, Georgia Institute of Technology, 2013. http://hdl.handle.net/1853/47708.
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The world faces the unprecedented challenge of the need change to a new energy era. The introduction of distributed renewable energy and storage together with transportation electrification and deployment of electric and hybrid vehicles, allows traditional consumers to not only consume, but also to produce, or store energy.
The active participation of these so called "prosumers", and their interactions may have a significant impact on the operations of the emerging smart grid. However, how these capabilities should be integrated with the overall system operation is unclear.
Intelligent energy management systems give users the insight they need to make informed decisions about energy consumption. Properly implemented, intelligent energy management systems can help cut energy use, spending, and emissions.
This thesis aims to develop a consumer point of view, user-friendly, intelligent energy management system that enables vehicle drivers to plan their trips, manage their battery pack and under specific circumstances, inject electricity from their plug-in vehicles to power the grid, contributing to frequency regulation.
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Kaygisiz, Huseyin Burak. "Intelligent Methods For Dynamic Analysis And Navigation Of Autonomous Land Vehicles." Phd thesis, METU, 2004. http://etd.lib.metu.edu.tr/upload/2/12605112/index.pdf.
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Autonomous land vehicles (ALVs) have received considerable attention after their introduction into military and commercial applications. ALVs still stand as a challenging research topic. One of the main problems arising in ALV operations is the navigation accuracy while the other is the dynamic effects of road irregularities which may prevent the vehicle and its cargo to function properly. In this thesis, we propose intelligent solutions to these two basic problems of ALV. First, an intelligent method is proposed to enhance the performance of a coupled global positioning/inertial navigation system (GPS/INS) for land navigation applications during the GPS signal loss. Our method is based on using an artificial neural network (ANN) to intelligently aid the GPS/INS coupled navigation system in the absence of GPS signals. The proposed enhanced GPS/INS is used in the dynamic environment of a tour of an autonomous van and we provide the results here. GPS/INS+ANN system performance is thus demonstrated with the land trials. Secondly, our work focuses on the identification and enlargement of the stability region of the ALV. In this thesis, the domain of attraction of the ALV is found to be patched by chaotic and regular regions with chaotic boundaries which are extracted using novel technique of cell mapping equipped with measures of fractal dimension and rough sets. All image cells in the cellular state space, with their individual fractal dimension are classified as being members of lower approximation (surely stable), upper approximation (possibly stable) or boundary region using rough set theory. The obtained rough set with fractal dimension as its attribute is used to model the uncertainty of the regular regions. This uncertainty is then smoothed by a reinforcement learning algorithm in order to enlarge regular regions that are used for chassis control, critical in ALV in preventing vibration damages that can harm the payload. Hence, we will make ALV work in the largest safe area in dynamical sense and prevent the vehicle and its cargo.
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Ali, Khattab M. "An intelligent intrusion detection system for external communications in autonomous vehicles." Thesis, University of Essex, 2017. http://repository.essex.ac.uk/20747/.
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Advancements in computing, electronics and mechanical systems have resulted in the creation of a new class of vehicles called autonomous vehicles. These vehicles function using sensory input with an on-board computation system. Self-driving vehicles use an ad hoc vehicular network called VANET. The network has ad hoc infrastructure with mobile vehicles that communicate through open wireless channels. This thesis studies the design and implementation of a novel intelligent intrusion detection system which secures the external communication of self-driving vehicles. This thesis makes the following four contributions: It proposes a hybrid intrusion detection system to protect the external communication in self-driving vehicles from potential attacks. This has been achieved using fuzzification and artificial intelligence. The second contribution is the incorporation of the Integrated Circuit Metrics (ICMetrics) for improved security and privacy. By using the ICMetrics, specific device features have been used to create a unique identity for vehicles. Our work is based on using the bias in on board sensory systems to create ICMetrics for self-driving vehicles. The incorporation of fuzzy petri net in autonomous vehicles is the third contribution of the thesis. Simulation results show that the scheme can successfully detect denial-of-service attacks. The design of a clustering based hierarchical detection system has also been presented to detect worm hole and Sybil attacks. The final contribution of this research is an integrated intrusion detection system which detects various attacks by using a central database in BusNet. The proposed schemes have been simulated using the data extracted from trace files. Simulation results have been compared and studied for high levels of detection capability and performance. Analysis shows that the proposed schemes provide high detection rate with a low rate of false alarm. The system can detect various attacks in an optimised way owing to a reduction in the number of features, fuzzification.