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Journal articles on the topic 'Autonomous infrastructure'

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Published: 4 June 2021
Last updated: 1 February 2022

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1

Milan, Stefania. "Autonomous infrastructure for a suckless internet." XRDS: Crossroads, The ACM Magazine for Students 24, no. 4 (July 11, 2018): 20–23. http://dx.doi.org/10.1145/3220877.

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2

Shami, Khaldoon, Damien Magoni, and Pascal Lorenz. "Autonomous, scalable, and resilient overlay infrastructure." Journal of Communications and Networks 8, no. 4 (December 2006): 378–90. http://dx.doi.org/10.1109/jcn.2006.6182786.

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3

Noorvand, Hossein, Guru Karnati, and B. Shane Underwood. "Autonomous Vehicles." Transportation Research Record: Journal of the Transportation Research Board 2640, no. 1 (January 2017): 21–28. http://dx.doi.org/10.3141/2640-03.

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With ongoing technological improvements and research in the field of autonomous vehicles, it is becoming evident that the technology has the potential to substantially affect the transportation sector. Although the potential benefits with respect to productivity increases, cost decreases, and safety are evident, the potential for these vehicles to negatively or positively affect the transportation infrastructure is unclear. In this study, the influence of truck loadings positioning on the long-term performance of transportation infrastructure was estimated by carrying out performance simulations of pavement structures. Scenarios considering both full and partial use by autonomous trucks were considered. In all cases, performance was estimated with respect to rutting, fatigue cracking, and overall pavement smoothness, and the results were compiled in terms of reduced pavement thickness. It was found that if controlled appropriately, autonomous trucks could be highly beneficial for the pavement infrastructure design, and they would be most effective when they represented more than 50% of the total truck traffic. It was also found that in the absence of appropriate control, specifically by repeatedly positioning trucks in the same location, the amount of damage could be highly detrimental, and noticeable influences may occur at autonomous truck volumes as low as 10%.
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Kozaczka, Eugeniusz, and Grażyna Grelowska. "Autonomous Platform to Protect Maritime Infrastructure Facilities." Polish Maritime Research 26, no. 4 (December 1, 2019): 101–8. http://dx.doi.org/10.2478/pomr-2019-0071.

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Abstract Problems regarding the security of maritime infrastructure, especially harbours and offshore infrastructure, are currently a very hot topic. Due to these problems, there are some research projects in which the main goal is to decrease the gap and improve the methods of observation in the chosen area, for both in-air and underwater areas. The main goal of the paper is to show a new complex system for improving the security of the maritime infrastructure by means of many methods of observation – such as thermovision, optical devices, and radar systems – generally by means of an electromagnetic wave as a carrier of information in the air and acoustical methods in water. The system can be applied to the protection of maritime infrastructure as well as the coastal zone.
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McNaughton, Matthew, Christopher R. Baker, Tugrul Galatali, Bryan Salesky, Christopher Urmson, and Jason Ziglar. "Software Infrastructure for an Autonomous Ground Vehicle." Journal of Aerospace Computing, Information, and Communication 5, no. 12 (December 2008): 491–505. http://dx.doi.org/10.2514/1.39487.

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Levin, Michael W., Eugene Wong, Benjamin Nault-Maurer, and Alireza Khani. "Parking infrastructure design for repositioning autonomous vehicles." Transportation Research Part C: Emerging Technologies 120 (November 2020): 102838. http://dx.doi.org/10.1016/j.trc.2020.102838.

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7

Wray, Robert E., Sean A. Lisse, and Jonathan T. Beard. "Ontology infrastructure for execution-oriented autonomous agents." Robotics and Autonomous Systems 49, no. 1-2 (November 2004): 113–22. http://dx.doi.org/10.1016/j.robot.2004.07.019.

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8

Yadan, LUO. "FROM TRANSPORTATION INFRASTRUCTURE TO GREEN INFRASTRUCTURE — ADAPTABLE FUTURE ROADS IN AUTONOMOUS URBANISM." Landscape Architecture Frontiers 7, no. 2 (2019): 92. http://dx.doi.org/10.15302/j-laf-20190209.

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9

Dong, Kai, Wenjia Wu, Haibo Ye, Ming Yang, Zhen Ling, and Wei Yu. "Canoe: An Autonomous Infrastructure-Free Indoor Navigation System." Sensors 17, no. 5 (April 30, 2017): 996. http://dx.doi.org/10.3390/s17050996.

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Truong, Quang Thinh, Ha Quang Thinh Ngo, Thanh Phuong Nguyen, Hung Nguyen, and Won-Ho Kim. "A Novel Infrastructure Design of Industrial Autonomous System." INTERNATIONAL JOURNAL of FUZZY LOGIC and INTELLIGENT SYSTEMS 19, no. 2 (June 30, 2019): 103–11. http://dx.doi.org/10.5391/ijfis.2019.19.2.103.

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11

Neveu, D., V. S. Bilodeau, M. Alger, B. Moffat, J.-F. Hamel, and J. de Lafontaine. "Simulation Infrastructure for Autonomous Vision-Based Navigation Technologies." IFAC Proceedings Volumes 43, no. 15 (2010): 279–86. http://dx.doi.org/10.3182/20100906-5-jp-2022.00048.

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12

Quack, Tobias, Michael Bösinger, Frank-Josef Heßeler, and Dirk Abel. "Infrastructure-based digital maps for connected autonomous vehicles." at - Automatisierungstechnik 66, no. 2 (February 23, 2018): 183–91. http://dx.doi.org/10.1515/auto-2017-0100.

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Abstract One major key to autonomous driving is reliable knowledge about the vehicle's surroundings. In complex situations like urban intersections, the vehicle's on-board sensors are often unable to detect and classify all features of the environment. Therefore, high-precision digital maps are widely used to provide the vehicle with additional information. In this article, we introduce a system which makes use of a mobile edge computing architecture (MEC) for computing digital maps on infrastructure-based, distributed computers. In cooperation with the mobile network operator Vodafone an LTE test field is implemented at the Aldenhoven Testing Center (ATC). The proving ground thus combines an urban crossing with the MEC capabilities of the LTE test field so that the developed methods can be tested in a realistic scenario. In the near future the LTE test field will be equipped with the new 5G mobile standard allowing for fast and reliable exchange of map and sensor data between vehicles and infrastructure.
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13

Mettler, Bérénice, Navid Dadkhah, Zhaodan Kong, and Jonathan Andersh. "Research Infrastructure for Interactive Human- and Autonomous Guidance." Journal of Intelligent & Robotic Systems 70, no. 1-4 (September 22, 2012): 437–59. http://dx.doi.org/10.1007/s10846-012-9774-6.

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14

Gouda, Maged, Ishaat Chowdhury, Jonas Weiß, Alexander Epp, and Karim El-Basyouny. "Automated assessment of infrastructure preparedness for autonomous vehicles." Automation in Construction 129 (September 2021): 103820. http://dx.doi.org/10.1016/j.autcon.2021.103820.

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15

Wu, Qiong, Siyang Xia, Pingyi Fan, Qiang Fan, and Zhengquan Li. "Velocity-Adaptive V2I Fair-Access Scheme Based on IEEE 802.11 DCF for Platooning Vehicles." Sensors 18, no. 12 (November 30, 2018): 4198. http://dx.doi.org/10.3390/s18124198.

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Platooning strategy is an important component of autonomous driving technology. Autonomous vehicles in platoons are often equipped with a variety of on-board sensors to detect the surrounding environment. The abundant data collected by autonomous vehicles in platoons can be transmitted to the infrastructure through vehicle-to-infrastructure (V2I) communications using the IEEE 802.11 distributed coordination function (DCF) mechanism and then uploaded to the cloud platform through the Internet. The cloud platform extracts useful information and then sends it back to the autonomous vehicles respectively. In this way, autonomous vehicles in platoons can detect emergency conditions and make a decision in time. The characteristics of platoons would cause a fair-access problem in the V2I communications, i.e., vehicles in the platoons moving on different lanes with different velocities would have different resident time within the infrastructure’s coverage and thus successfully send different amounts of data to the infrastructure. In this case, the vehicles with different velocities will receive different amounts of useful information from the cloud. As a result, vehicles with a higher velocity are more likely to suffer from a traffic accident as compared to the vehicles with a lower velocity. Hence, this paper considers the fair-access problem and proposes a fair-access scheme to ensure that vehicles with different velocities successfully transmit the same amount of data by adaptively adjusting the minimum contention window of each vehicle according to its velocity. Moreover, the normalized throughput of the proposed scheme is derived. The validity of the fair-access scheme is demonstrated by simulation.
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16

Protopapadakis, E., C. Stentoumis, N. Doulamis, A. Doulamis, K. Loupos, K. Makantasis, G. Kopsiaftis, and A. Amditis. "AUTONOMOUS ROBOTIC INSPECTION IN TUNNELS." ISPRS Annals of Photogrammetry, Remote Sensing and Spatial Information Sciences III-5 (June 6, 2016): 167–74. http://dx.doi.org/10.5194/isprsannals-iii-5-167-2016.

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In this paper, an automatic robotic inspector for tunnel assessment is presented. The proposed platform is able to autonomously navigate within the civil infrastructures, grab stereo images and process/analyse them, in order to identify defect types. At first, there is the crack detection via deep learning approaches. Then, a detailed 3D model of the cracked area is created, utilizing photogrammetric methods. Finally, a laser profiling of the tunnel’s lining, for a narrow region close to detected crack is performed; allowing for the deduction of potential deformations. The robotic platform consists of an autonomous mobile vehicle; a crane arm, guided by the computer vision-based crack detector, carrying ultrasound sensors, the stereo cameras and the laser scanner. Visual inspection is based on convolutional neural networks, which support the creation of high-level discriminative features for complex non-linear pattern classification. Then, real-time 3D information is accurately calculated and the crack position and orientation is passed to the robotic platform. The entire system has been evaluated in railway and road tunnels, i.e. in Egnatia Highway and London underground infrastructure.
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17

Protopapadakis, E., C. Stentoumis, N. Doulamis, A. Doulamis, K. Loupos, K. Makantasis, G. Kopsiaftis, and A. Amditis. "AUTONOMOUS ROBOTIC INSPECTION IN TUNNELS." ISPRS Annals of Photogrammetry, Remote Sensing and Spatial Information Sciences III-5 (June 6, 2016): 167–74. http://dx.doi.org/10.5194/isprs-annals-iii-5-167-2016.

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In this paper, an automatic robotic inspector for tunnel assessment is presented. The proposed platform is able to autonomously navigate within the civil infrastructures, grab stereo images and process/analyse them, in order to identify defect types. At first, there is the crack detection via deep learning approaches. Then, a detailed 3D model of the cracked area is created, utilizing photogrammetric methods. Finally, a laser profiling of the tunnel’s lining, for a narrow region close to detected crack is performed; allowing for the deduction of potential deformations. The robotic platform consists of an autonomous mobile vehicle; a crane arm, guided by the computer vision-based crack detector, carrying ultrasound sensors, the stereo cameras and the laser scanner. Visual inspection is based on convolutional neural networks, which support the creation of high-level discriminative features for complex non-linear pattern classification. Then, real-time 3D information is accurately calculated and the crack position and orientation is passed to the robotic platform. The entire system has been evaluated in railway and road tunnels, i.e. in Egnatia Highway and London underground infrastructure.
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18

Kenning, Marie-Madeleine. "Creating an infrastructure for autonomous learning: The resource catalogue." System 24, no. 2 (June 1996): 223–31. http://dx.doi.org/10.1016/0346-251x(96)00007-3.

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19

Sowade, S., D. Rippel, and B. Scholz-Reiter. "Modeling concept for the infrastructure of autonomous logistic processes." CIRP Journal of Manufacturing Science and Technology 5, no. 4 (January 2012): 254–66. http://dx.doi.org/10.1016/j.cirpj.2012.09.011.

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20

Zhi-gang, Wang, Lu Zheng-ding, Li Rui-xuan, Wu Wei, and Wang Xiao-gang. "TrustedRBAC—A distributed authorization infrastructure span multiple autonomous domains." Wuhan University Journal of Natural Sciences 9, no. 5 (September 2004): 694–98. http://dx.doi.org/10.1007/bf02831665.

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21

Yeum, C. M., J. Choi, and S. J. Dyke. "Autonomous image localization for visual inspection of civil infrastructure." Smart Materials and Structures 26, no. 3 (February 20, 2017): 035051. http://dx.doi.org/10.1088/1361-665x/aa510e.

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22

Sun, Yu, Yin Cui, and Huixia Huang. "An Empirical Analysis of the Coupling Coordination among Decomposed Effects of Urban Infrastructure Environment Benefit: Case Study of Four Chinese Autonomous Municipalities." Mathematical Problems in Engineering 2016 (2016): 1–11. http://dx.doi.org/10.1155/2016/8472703.

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The environment benefit of urban public infrastructure is the positive influence on the natural ecological environment generated by the use of urban infrastructure. This paper decomposes urban infrastructure environment benefit into three effects which include treating waste effect, purifying air effect, and regulating climate effect for the first time and introduces a comprehensive approach to evaluate the coupling coordination among three effects taking four Chinese autonomous municipalities as an example. These four cities have large-scale urban infrastructures but their environment problems are more serious. The basic function of urban infrastructures, especially environment protection, has not been fully played in these cities. Whether the different decomposed effects of urban infrastructure environment benefit have been developed in harmony or not is unclear. We analyzed the coordinated development among three effects by constructing a coupling coordination degree model and studied the impacts of three effects on coupling coordination degree using the panel data regression model. The result showed that the coupling coordination degrees among three effects of urban infrastructure environment benefit of four cities were all at the level of moderately unbalanced development and the impacts of three effects on coupling coordination degree among them in four cities were fairly close.
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23

Mushtaq, Anum, Irfan ul Haq, Wajih un Nabi, Asifullah Khan, and Omair Shafiq. "Traffic Flow Management of Autonomous Vehicles Using Platooning and Collision Avoidance Strategies." Electronics 10, no. 10 (May 20, 2021): 1221. http://dx.doi.org/10.3390/electronics10101221.

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Connected Autonomous Vehicles (AVs) promise innovative solutions for traffic flow management, especially for congestion mitigation. Vehicle-to-Vehicle (V2V) communication depends on wireless technology where vehicles can communicate with each other about obstacles and make cooperative strategies to avoid these obstacles. Vehicle-to-Infrastructure (V2I) also helps vehicles to make use of infrastructural components to navigate through different paths. This paper proposes an approach based on swarm intelligence for the formation and evolution of platoons to maintain traffic flow during congestion and collision avoidance practices using V2V and V2I communications. In this paper, we present a two level approach to improve traffic flow of AVs. At the first level, we reduce the congestion by forming platoons and study how platooning helps vehicles deal with congestion or obstacles in uncertain situations. We performed experiments based on different challenging scenarios during the platoon’s formation and evolution. At the second level, we incorporate a collision avoidance mechanism using V2V and V2I infrastructures. We used SUMO, Omnet++ with veins for simulations. The results show significant improvement in performance in maintaining traffic flow.
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Kozaczka, Nina, and Stanisław Gaca. "An attempt to evaluate the influence of automated vehicles on traffic flow and design of road infrastructure." Transportation Overview - Przeglad Komunikacyjny 2019, no. 9 (September 1, 2019): 29–38. http://dx.doi.org/10.35117/a_eng_19_09_04.

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The article evaluates the impact of autonomous vehicles on road infrastructure de- sign, road traffic conditions and safety based on a review of existing literature. Levels of driv- ing automation and equipment of self-driving vehicles were presented. Attention was drawn to the benefits of developing communication systems between vehicle and the environment. The possible negative impact of autonomous vehicles on mixed traffic capacity was noted. The potential needs to adapt the road infrastructure to the traffic flow of automated vehicles were also presented. Separation of the lane, dedicated to self-driving vehicles, with a high share of these vehicles was presented as an element that improves the flow of traffic and safe- ty. Keywords: Autonomous vehicles; Road infrastructure; Self-driving cars
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Bhavsar, Parth, Plaban Das, Matthew Paugh, Kakan Dey, and Mashrur Chowdhury. "Risk Analysis of Autonomous Vehicles in Mixed Traffic Streams." Transportation Research Record: Journal of the Transportation Research Board 2625, no. 1 (January 2017): 51–61. http://dx.doi.org/10.3141/2625-06.

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The introduction of autonomous vehicles in the surface transportation system could improve traffic safety and reduce traffic congestion and negative environmental effects. Although the continuous evolution in computing, sensing, and communication technologies can improve the performance of autonomous vehicles, the new combination of autonomous automotive and electronic communication technologies will present new challenges, such as interaction with other nonautonomous vehicles, which must be addressed before implementation. The objective of this study was to identify the risks associated with the failure of an autonomous vehicle in mixed traffic streams. To identify the risks, the autonomous vehicle system was first disassembled into vehicular components and transportation infrastructure components, and then a fault tree model was developed for each system. The failure probabilities of each component were estimated by reviewing the published literature and publicly available data sources. This analysis resulted in a failure probability of about 14% resulting from a sequential failure of the autonomous vehicular components alone in the vehicle’s lifetime, particularly the components responsible for automation. After the failure probability of autonomous vehicle components was combined with the failure probability of transportation infrastructure components, an overall failure probability related to vehicular or infrastructure components was found: 158 per 1 million mi of travel. The most critical combination of events that could lead to failure of autonomous vehicles, known as minimal cut-sets, was also identified. Finally, the results of fault tree analysis were compared with real-world data available from the California Department of Motor Vehicles autonomous vehicle testing records.
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Kremlev, I. A., and A. V. Tyryshkin. "Infrastructure Facilities for Unmanned Vehicles." World of Transport and Transportation 17, no. 2 (September 13, 2019): 64–71. http://dx.doi.org/10.30932/1992-3252-2019-17-2-64-71.

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The article considers the technological aspects of creating infrastructure facilities for organization of movement of unmanned vehicles on the roads. The features of operation of cars with an autonomous control system inRussiaand other regions of the world are analyzed. Basing on the study, the authors offer a fundamentally new approach for solving a problem of recognizing objects along the route of unmanned vehicles.The study suggests installation of stationary points that are interconnected in a single network and exchange data with the cloud storage. The effectiveness of existing and alternative systems is evaluated.
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Serrano. "Deep Reinforcement Learning Algorithms in Intelligent Infrastructure." Infrastructures 4, no. 3 (August 16, 2019): 52. http://dx.doi.org/10.3390/infrastructures4030052.

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Intelligent infrastructure, including smart cities and intelligent buildings, must learn and adapt to the variable needs and requirements of users, owners and operators in order to be future proof and to provide a return on investment based on Operational Expenditure (OPEX) and Capital Expenditure (CAPEX). To address this challenge, this article presents a biological algorithm based on neural networks and deep reinforcement learning that enables infrastructure to be intelligent by making predictions about its different variables. In addition, the proposed method makes decisions based on real time data. Intelligent infrastructure must be able to proactively monitor, protect and repair itself: this includes independent components and assets working the same way any autonomous biological organisms would. Neurons of artificial neural networks are associated with a prediction or decision layer based on a deep reinforcement learning algorithm that takes into consideration all of its previous learning. The proposed method was validated against an intelligent infrastructure dataset with outstanding results: the intelligent infrastructure was able to learn, predict and adapt to its variables, and components could make relevant decisions autonomously, emulating a living biological organism in which data flow exhaustively.
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Heino, Ossi, and Ari-Veikko Anttiroiko. "Inverse infrastructures: self-organization in the water services." Water Policy 17, no. 2 (September 30, 2014): 299–315. http://dx.doi.org/10.2166/wp.2014.095.

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In urban communities, infrastructures that support living are indispensable. There is increased interest in alternative ways of providing such support systems, including semi-autonomous infrastructures resulting from the self-organization of local actors. In this study, we analyze the emergence and management of such infrastructures in light of the theory of complex adaptive systems, within which they are called ‘inverse infrastructures’. Empirical evidence is drawn from the case of water cooperatives in the town of Ikaalinen, Finland. Our analysis shows that, with favorable preconditions in place, inverse infrastructures may contribute significantly to local infrastructure services and so also to the functioning of society.
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Aguzzi, Jacopo, Jan Albiez, Sascha Flögel, Olav Rune Godø, Endre Grimsbø, Simone Marini, Olaf Pfannkuche, et al. "A Flexible Autonomous Robotic Observatory Infrastructure for Bentho-Pelagic Monitoring." Sensors 20, no. 6 (March 13, 2020): 1614. http://dx.doi.org/10.3390/s20061614.

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This paper presents the technological developments and the policy contexts for the project “Autonomous Robotic Sea-Floor Infrastructure for Bentho-Pelagic Monitoring” (ARIM). The development is based on the national experience with robotic component technologies that are combined and merged into a new product for autonomous and integrated ecological deep-sea monitoring. Traditional monitoring is often vessel-based and thus resource demanding. It is economically unviable to fulfill the current policy for ecosystem monitoring with traditional approaches. Thus, this project developed platforms for bentho-pelagic monitoring using an arrangement of crawler and stationary platforms at the Lofoten-Vesterålen (LoVe) observatory network (Norway). Visual and acoustic imaging along with standard oceanographic sensors have been combined to support advanced and continuous spatial-temporal monitoring near cold water coral mounds. Just as important is the automatic processing techniques under development that have been implemented to allow species (or categories of species) quantification (i.e., tracking and classification). At the same time, real-time outboard processed three-dimensional (3D) laser scanning has been implemented to increase mission autonomy capability, delivering quantifiable information on habitat features (i.e., for seascape approaches). The first version of platform autonomy has already been tested under controlled conditions with a tethered crawler exploring the vicinity of a cabled stationary instrumented garage. Our vision is that elimination of the tether in combination with inductive battery recharge trough fuel cell technology will facilitate self-sustained long-term autonomous operations over large areas, serving not only the needs of science, but also sub-sea industries like subsea oil and gas, and mining.
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Baiden, G., Y. Bissiri, S. Luoma, and G. Henrich. "Mapping utility infrastructure via underground GPS positioning with autonomous telerobotics." Tunnelling and Underground Space Technology 39 (January 2014): 6–14. http://dx.doi.org/10.1016/j.tust.2013.03.007.

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31

Li, Yuying, and Qipeng Liu. "Intersection management for autonomous vehicles with vehicle-to-infrastructure communication." PLOS ONE 15, no. 7 (July 2, 2020): e0235644. http://dx.doi.org/10.1371/journal.pone.0235644.

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32

Yang, Jianjun, Tinggui Chen, Bryson Payne, Ping Guo, Yanping Zhang, and Juan Guo. "Generating routes for autonomous driving in vehicle-to-infrastructure communications." Digital Communications and Networks 6, no. 4 (November 2020): 444–51. http://dx.doi.org/10.1016/j.dcan.2020.04.005.

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33

Förster, Jan, Winfred Kuipers, Christian Lenz, Steffen Ziesche, and Franz Bechtold. "An autonomous flame ionization detector for emission monitoring." Journal of Sensors and Sensor Systems 8, no. 1 (January 24, 2019): 67–73. http://dx.doi.org/10.5194/jsss-8-67-2019.

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Abstract. Reliable and very sensitive detection of hydrocarbons can be achieved with a flame ionization detector (FID). Due to the required complex gas infrastructure for the operation of an FID, these devices have not been implemented as true field devices yet. Miniaturization by using ceramic multilayer technology leads to a strong reduction of gas consumption and allows autonomous operation of the FID with gas supply by electrolysis and without external gas infrastructure. Therefore, this research enables the use of the FID in the field. Characterization of this miniaturized FID reveals a performance comparable to conventional FIDs.
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Othman, Kareem. "Impact of Autonomous Vehicles on the Physical Infrastructure: Changes and Challenges." Designs 5, no. 3 (July 8, 2021): 40. http://dx.doi.org/10.3390/designs5030040.

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Over the last few years, autonomous vehicles (AVs) have witnessed tremendous worldwide interest. Although AVs have been extensively studied in the literature regarding their benefits, implications, and public acceptance, research on the physical infrastructure requirements for autonomous vehicles is still in the infancy stage. For the road infrastructure, AVs can be very promising; however, AVs might introduce new risks and challenges. This paper investigates the impact of AVs on the physical infrastructure with the objective of revealing the infrastructure changes and challenges in the era of AVs. In AVs, the human factor, which is the major factor that influences the geometric design, will not be a concern anymore so the geometric design requirements can be relaxed. On the other hand, the decrease in the wheel wander, because of the lane-keeping system, and the increase in the lane capacity, because of the elimination of the human factor, will bring an accelerated rutting potential and will quickly deteriorate the pavement condition. Additionally, the existing structural design methods for bridges are not safe to support autonomous truck platoons. For parking lots, AVs have the potential to significantly increase the capacity of parking lots using the blocking strategy. However, the implementation of this parking strategy faces multiple issues such as the inconsistent marking system. Finally, AVs will need new infrastructure facilities such as safe harbor areas.
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Larsson, Anthony, Carl Savage, Mats Brommels, and Pauline Mattsson. "Structuring a research infrastructure: A study of the rise and fall of a large-scale distributed biobank facility." Social Science Information 57, no. 2 (March 10, 2018): 196–222. http://dx.doi.org/10.1177/0539018418761848.

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This study analyses the perceived key interests, importance, influences and participation of different actors in harmonizing the processes and mechanisms of a distributed research infrastructure. It investigates the EU-funded initiative, BioBanking and Molecular Resource Infrastructure in Sweden (BBMRI.se), which seeks to harmonize the biobanking standards. The study interviews multiple actors involved throughout the development process. Their responses are analysed via a framework based on the IIED Stakeholder Power Analysis Tool. The BBMRI.se formation was facilitated by two parallel processes, with domestic and European/foreign origin, with leading scientists becoming ‘National Champions’. The respondents joined the organization under the premise that it would be a collaborative endeavour, but they were disappointed to learn the deliberative elements were more prevalent. In conclusion, the resulting autonomous structure caused disarray, while also fuelling interpersonal differences, ultimately leading to the closure of the infrastructure. Hence, it is necessary to clearly identify potential collaborative and deliberative elements already at the outset while also securing wider forms of communication between the participating actors, when establishing distributed research infrastructures. Moreover, while prior literature suggests that research infrastructures counteracts fragmentation, these results illustrate that this is not the case for this distributed research infrastructure.
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Hjalmarsson-Jordanius, Anders, Mikael Edvardsson, Martin Romell, Johan Isacson, Carl-Johan Aldén, and Niklas Sundin. "Autonomous Transport: Transforming Logistics through Driverless Intelligent Transportation." Transportation Research Record: Journal of the Transportation Research Board 2672, no. 7 (September 17, 2018): 24–33. http://dx.doi.org/10.1177/0361198118796968.

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How can autonomous technology be used beyond end-customer autonomous driving features? This position paper addresses this problem by exploring a novel autonomous transport solution applied in the automotive logistics domain. We propose that factory-complete cars can be transformed to become their own autonomous guided vehicles and thus transport themselves when being moved from the factory for shipment. Cars equipped with such a system are driverless and use an onboard autonomous transport solution combined with the advanced driver assistance systems pre-installed in the car for end-customer use. The solution uses factory-equipped sensors as well as the connectivity infrastructure installed in the car. This means that the solution does not require any extra components to enable the car to transport itself autonomously to complete a transport mission in the logistics chain. The solution also includes an intelligent off-board traffic control system that defines the transport mission and manages the interaction between vehicles during systems operation. A prototype of the system has been developed which was tested successfully in live trials at the Volvo Car Group plant in Gothenburg Sweden in 2017. In the paper, autonomous transport is positioned in between autonomous guided vehicles and autonomous driving technology. A review of the literature on autonomous vehicle technology offers contextual background to this positioning. The paper also presents the solution and displays lessons learned from the live trials. Finally, other use areas are introduced for driverless autonomous transport beyond the automotive logistics domain that is the focus of this paper.
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Fischer, Colin, Monika Sester, and Steffen Schön. "Spatio-Temporal Research Data Infrastructure in the Context of Autonomous Driving." ISPRS International Journal of Geo-Information 9, no. 11 (October 25, 2020): 626. http://dx.doi.org/10.3390/ijgi9110626.

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In this paper, we present an implementation of a research data management system that features structured data storage for spatio-temporal experimental data (environmental perception and navigation in the framework of autonomous driving), including metadata management and interfaces for visualization and parallel processing. The demands of the research environment, the design of the system, the organization of the data storage, and computational hardware as well as structures and processes related to data collection, preparation, annotation, and storage are described in detail. We provide examples for the handling of datasets, explaining the required data preparation steps for data storage as well as benefits when using the data in the context of scientific tasks.
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38

Kong, Peng-Yong. "Computation and Sensor Offloading for Cloud-Based Infrastructure-Assisted Autonomous Vehicles." IEEE Systems Journal 14, no. 3 (September 2020): 3360–70. http://dx.doi.org/10.1109/jsyst.2019.2959703.

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39

Soldatos, John, Ippokratis Pandis, Kostas Stamatis, Lazaros Polymenakos, and James L. Crowley. "Agent based middleware infrastructure for autonomous context-aware ubiquitous computing services." Computer Communications 30, no. 3 (February 2007): 577–91. http://dx.doi.org/10.1016/j.comcom.2005.11.018.

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40

Li, Yang-Yang, and Elvino S. Sousa. "Cognitive Femtocell: A Cost-Effective Approach Towards 4G Autonomous Infrastructure Networks." Wireless Personal Communications 64, no. 1 (February 7, 2012): 65–78. http://dx.doi.org/10.1007/s11277-012-0517-6.

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41

Shoushtari, Hossein, Thomas Willemsen, and Harald Sternberg. "Many Ways Lead to the Goal—Possibilities of Autonomous and Infrastructure-Based Indoor Positioning." Electronics 10, no. 4 (February 5, 2021): 397. http://dx.doi.org/10.3390/electronics10040397.

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There are many ways to navigate in Global Navigation Satellite System-(GNSS) shaded areas. Reliable indoor pedestrian navigation has been a central aim of technology researchers in recent years; however, there still exist open challenges requiring re-examination and evaluation. In this paper, a novel dataset is used to evaluate common approaches for autonomous and infrastructure-based positioning methods. The autonomous variant is the most cost-effective realization; however, realizations using the real test data demonstrate that the use of only autonomous solutions cannot always provide a robust solution. Therefore, correction through the use of infrastructure-based position estimation based on smartphone technology is discussed. This approach invokes the minimum cost when using existing infrastructure, whereby Pedestrian Dead Reckoning (PDR) forms the basis of the autonomous position estimation. Realizations with Particle Filters (PF) and a topological approach are presented and discussed. Floor plans and routing graphs are used, in this case, to support PDR positioning. The results show that the positioning model loses stability after a given period of time. Fifth Generation (5G) mobile networks can enable this feature, as well as a massive number of use-cases, which would benefit from user position data. Therefore, a fusion concept of PDR and 5G is presented, the benefit of which is demonstrated using the simulated data. Subsequently, the first implementation of PDR with 5G positioning using PF is carried out.
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42

Maksakova, Darya, and Sergei Popov. "Modelling gas supply systems with a high role of autonomous consumers (the case of Mongolia)." E3S Web of Conferences 209 (2020): 05010. http://dx.doi.org/10.1051/e3sconf/202020905010.

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The paper presents a tool to optimize gas infrastructure systems and analyses some aspects of modelling related to autonomous gas consumers. A model of national gas infrastructure creation in Mongolia is proposed. The model is linked with the model of the regional Northeast Asian gas market and the financial models of gas infrastructure facilities. The model determines the optimal design of the national gas infrastructure system, i.e. the number of the facilities, their capacities, locations and the transport modes for connecting the consumption centres. The role of autonomous consumers is considered by introducing the demand for liquefied natural gas separately from the demand for pipeline gas. The scope of the model application is demonstrated by an illustrative example. The results show the rational natural gas import and distribution patterns. The need for expanding the energy cooperation between Mongolia and the other Northeast Asian countries to create gas industry in Mongolia is highlighted.
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43

DOVIER, AGOSTINO, ANDREA FORMISANO, and ENRICO PONTELLI. "Autonomous agents coordination: Action languages meet CLP() and Linda." Theory and Practice of Logic Programming 13, no. 2 (September 24, 2012): 149–73. http://dx.doi.org/10.1017/s1471068411000615.

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AbstractThe paper presents a knowledge representation formalism, in the form of a high-levelAction Description Language (ADL)for multi-agent systems, where autonomous agents reason and act in a shared environment. Agents are autonomously pursuing individual goals, but are capable of interacting through a shared knowledge repository. In their interactions through shared portions of the world, the agents deal with problems of synchronization and concurrency; the action language allows the description of strategies to ensure a consistent global execution of the agents’ autonomously derived plans. A distributed planning problem is formalized by providing the declarative specifications of the portion of the problem pertaining to a single agent. Each of these specifications is executable by a stand-alone CLP-based planner. The coordination among agents exploits a Linda infrastructure. The proposal is validated in a prototype implementation developed in SICStus Prolog.
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44

Palevičius, Vytautas, Rasa Ušpalytė-Vitkūnienė, Jonas Damidavičius, and Tomas Karpavičius. "Concepts of Development of Alternative Travel in Autonomous Cars." Sustainability 12, no. 21 (October 24, 2020): 8841. http://dx.doi.org/10.3390/su12218841.

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Autonomous car travel planning is increasingly gaining attention from scientists and professionals, who are addressing the integration of autonomous cars into the general urban transportation system. Autonomous car travel planning depends on the transport system infrastructure, the dynamic data, and their quality. The efficient development of travel depends on the development level of the Intelligent Transport Systems (ITS) and the Cooperative Intelligent Transport Systems (C-ITS). Today, most cities around the world are competing with each other to become the smartest cities possible, using and integrating the most advanced ITS and C-ITS that are available. It is clear that ITS and C-ITS are occupying an increasing share of urban transport infrastructure, so the complex challenges of ITS and C-ITS development will inevitably need to be addressed, in the near future, by integrating them into the overall urban transport system. With this in mind, the authors proposed three autonomous car travel development concepts that should become a conceptual tool in the development of ITS and C-ITS.
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Takmasheva, Irina V. "Development of the state support of small business of Ugra." Yugra State University Bulletin 13, no. 4 (December 15, 2015): 73–75. http://dx.doi.org/10.17816/byusu20150473-75.

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In article the analysis of the infrastructure of support of small business existing in the autonomous area is carried out. It is revealed that in the specified sphere become especially actual directions of development: centralization and creation of associative structures. The model of the mechanism of the state support of small business in Khanty-Mansiysk Autonomous Okrug - Ugra is presented.
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46

Shieh, Peter Iming, Yao Chang Jeng, and Ming Piao Tsai. "Agent Based Infrastructure with RFID Technology for Autonomous Shop Floor Control System." Advanced Materials Research 341-342 (September 2011): 596–600. http://dx.doi.org/10.4028/www.scientific.net/amr.341-342.596.

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Automatic shop floor control system in distributed computing context is one of the most important links in Computer Integrated Manufacturing (or CIM) system. For most agent-based manufacturing control systems, a real-time product data available mechanism is not considered. In this paper, an integrated approach of RFID technology and multi-agent system of shot-floor control is presented. The proposed framework uses RFID for tracking products in the system and also for attaching product manufacturing information. This shift from off-line planning systems to on-line control systems will facilitate implementations of real-time scheduling requirement in autonomous manufacturing environment.
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47

Alves, JoÃo, Lorenzo Brignone, Matthias Schneider, and Joerg Kalwa. "Communication infrastructure for fleets of autonomous marine vehicles: concepts and first results." IFAC Proceedings Volumes 42, no. 18 (2009): 370–75. http://dx.doi.org/10.3182/20090916-3-br-3001.0060.

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48

Zhang, Hongcai, Colin J. R. Sheppard, Timothy E. Lipman, Teng Zeng, and Scott J. Moura. "Charging infrastructure demands of shared-use autonomous electric vehicles in urban areas." Transportation Research Part D: Transport and Environment 78 (January 2020): 102210. http://dx.doi.org/10.1016/j.trd.2019.102210.

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49

Vosooghi, Reza, Jakob Puchinger, Joschka Bischoff, Marija Jankovic, and Anthony Vouillon. "Shared autonomous electric vehicle service performance: Assessing the impact of charging infrastructure." Transportation Research Part D: Transport and Environment 81 (April 2020): 102283. http://dx.doi.org/10.1016/j.trd.2020.102283.

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Liu, Yuyan, Miles Tight, Quanxin Sun, and Ruiyu Kang. "A systematic review: Road infrastructure requirement for Connected and Autonomous Vehicles (CAVs)." Journal of Physics: Conference Series 1187, no. 4 (April 2019): 042073. http://dx.doi.org/10.1088/1742-6596/1187/4/042073.

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