Готові списки джерел за темами / Car driving

Добірка наукової літератури з теми "Car driving"

Автор: Grafiati

Опубліковано: 4 червня 2021

Оновлено: 20 лютого 2023

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Статті в журналах з теми "Car driving"

T., Dr Manikandan. "Self Driving Car." International Journal of Psychosocial Rehabilitation 24, no. 5 (March 31, 2020): 380–88. http://dx.doi.org/10.37200/ijpr/v24i5/pr201704.

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Blevis, Eli. "Selfish-driving car." Interactions 24, no. 2 (February 21, 2017): 88. http://dx.doi.org/10.1145/3047404.

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Dubey, Ashutosh, and Prabakaran N. "SELF-DRIVING CAR SIMULATION." International Research Journal of Computer Science 07, no. 05 (May 25, 2020): 66–69. http://dx.doi.org/10.26562/irjcs.2020.v0705.002.

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Phansekar, Soham. "LIDAR Self Driving Car." International Journal for Research in Applied Science and Engineering Technology 9, no. 10 (October 31, 2021): 1334–37. http://dx.doi.org/10.22214/ijraset.2021.38621.

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Abstract: Increasing population is the major issue of transportation nowadays. People who live and work in the major cities of the world are faced with increasing levels of congestion, delays, total travel time, costs, frustration, accidents and loss of life. The objective of this project is to help prevent traffic accidents and save people’s time by fundamentally changing car use. The system would have sensors to detect the obstacles and to be able to react according to their position. In this project we have developed an automated driving system which drives the car automatically. We have developed a technology for cars that drives it automatically using LIDAR. This car is capable of sensing the surroundings, navigating and fulfilling the human transportation capabilities without any human input. It continuously tracks the surrounding and if any obstacle is detected vehicle senses and moves around and avoids the obstacle. An autonomous car navigation system based on Global Positioning System (GPS) is a new and promising technology, which uses real time geographical data received from several GPS satellites to calculate longitude, latitude, speed and course to help navigate a car. As we know the development of gps is more improved now the accuracy of gps we can see centimetre also so Like for our car to go at specific inputted location we use this gps technology.Lidar is used for sensing the surroundings. Like radar, lidar is an active remote sensing technology but instead of using radio or microwaves it uses electromagnetic waves. Keywords: Congestion, Traffic Accident, LIDAR sensor, Global Positioning System, Electromagnetic waves

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M R, Prajwal. "Self-Driving Autonomous Car." International Journal for Research in Applied Science and Engineering Technology 8, no. 8 (August 31, 2020): 260–63. http://dx.doi.org/10.22214/ijraset.2020.30866.

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Kumar, Dr S. Senthil, Anjali S, and Hashina Parveen S. Aishwarya R. "Automatic Car Window Opener for safe Driving." International Journal of Trend in Scientific Research and Development Volume-2, Issue-2 (February 28, 2018): 1253–56. http://dx.doi.org/10.31142/ijtsrd9606.

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Kerr, Sophie-May, Natascha Klocker, and Gordon Waitt. "Diverse Driving Emotions." Transfers 8, no. 2 (June 1, 2018): 23–43. http://dx.doi.org/10.3167/trans.2018.080203.

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In the industrialized West, cars are considered an essential part of everyday life. Their dominance is underpinned by the challenges of managing complex, geographically stretched daily routines. Drivers’ emotional and embodied relationships with automobiles also help to explain why car cultures are difficult to disrupt. This article foregrounds ethnic diversity to complicate notions of a “love affair” with the car. We report on the mobilities of fourteen Chinese migrants living in Sydney, Australia—many of whom described embodied dispositions against the car, influenced by their life histories. Their emotional responses to cars and driving, shaped by transport norms and infrastructures in their places of origin, ranged from pragmatism and ambivalence to fear and hostility. The lived experiences of these migrants show that multiple cultures of mobility coexist, even in ostensibly car-dependent societies. Migrants’ life histories and contemporary practices provide an opportunity to reflect on fissures in the logic of automobility.

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Zhao, Jianfeng, Bodong Liang, and Qiuxia Chen. "The key technology toward the self-driving car." International Journal of Intelligent Unmanned Systems 6, no. 1 (January 2, 2018): 2–20. http://dx.doi.org/10.1108/ijius-08-2017-0008.

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Purpose The successful and commercial use of self-driving/driverless/unmanned/automated car will make human life easier. The paper aims to discuss this issue. Design/methodology/approach This paper reviews the key technology of a self-driving car. In this paper, the four key technologies in self-driving car, namely, car navigation system, path planning, environment perception and car control, are addressed and surveyed. The main research institutions and groups in different countries are summarized. Finally, the debates of self-driving car are discussed and the development trend of self-driving car is predicted. Findings This paper analyzes the key technology of self-driving car and illuminates the state-of-art of the self-driving car. Originality/value The main research contents and key technology have been introduced. The research progress as well as the research institution has been summarized.

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Hoc, Jean-Michel. "Car-driving assistance for safety." Le travail humain 69, no. 2 (2006): 97. http://dx.doi.org/10.3917/th.692.0097.

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Rane, Vedant, Hrithik Poojari, Prasan Sharma, Soham Phansekar, and Prof Prajakta Pawar. "LiDAR Based Self-Driving Car." International Journal for Research in Applied Science and Engineering Technology 10, no. 4 (April 30, 2022): 261–70. http://dx.doi.org/10.22214/ijraset.2022.41213.

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Abstract: LiDAR, typically used as an acronym for “’light detection and ranging’”, is essentially a sonar that uses pulsed laser waves to map the distance to surrounding objects. It is used by a large number of autonomous vehicles to navigate environments in real time. Its advantages include impressively accurate depth perception, which allows LiDAR to know the distance to an object to within a few centimetres, up to 60 metres away. It’s also highly suitable for 3D mapping, which means returning vehicles can then navigate the environment predictably —a significant benefit for most self-driving technologies. One of the key strengths of LiDAR is the number of areas that show potential for improvement. These include solid-state sensors, which could reduce its cost tenfold, sensor range increases of up to 200m, and 4-dimensional LiDAR, which senses the velocity of an object as well as its position in 3-D space. However, despite these exciting advances, LiDAR is still hindered by a key factor; its significant cost. LiDAR is not the only self-driving detection technology, with cameras as the major rival, championed by Tesla as the best way forward. Elon Musk has described LiDAR as “a fool’s errand” and “unnecessary”. The argument runs that humans drive based only on ambient visible light, so robots should equally be able to. A camera is significantly smaller and cheaper than LiDAR (although more of them are needed), and has the advantage of seeing in better resolution and in colour, meaning it can read traffic lights and signs. However, cameras have a wide host of characteristics that make them tricky to use in common driving conditions. Whereas LiDAR uses near infra-red light, cameras use visible light, and are thus more susceptible to issues when faced with rain, fog, or even some textures. In addition, LiDARs do not depend on ambient light, generating their own light pulses, whereas cameras are more sensitive to sudden light changes, direct sunlight and even raindrops. Keywords: Congestion, Traffic Accident, LIDAR sensor, Global Positioning System, Electromagnetic waves

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Дисертації з теми "Car driving"

EKESUND, JACOB. "Self-driving car." Thesis, KTH, Maskinkonstruktion (Inst.), 2016. http://urn.kb.se/resolve?urn=urn:nbn:se:kth:diva-191188.

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Imagine to be able to catch a little more sleep on your way to work or school, drive home after a night at the bar or watch a movie on a long road trip. All these things have never been possible before without someone else driving the car, until recent years. Autonomous cars or self-driving cars is being introduced to society more and more and will be the next big step in the progression of personal cars. There are a number of factors that decides how fast this new technology will be adopted. Safety, reliability, ethics and cost to name a few. This project will focus on the cost aspect of self-driving cars by examine if ultrasonic sensors can be used to develop a cheap self-driving car and thereby reach a broad customer base. To determine this a small scale prototype car was built and tested in a highway cruising situation and the results showed that the prototype was able to drive itself.
Tänk dig att kunna sova några extra minuter på väg till jobbet eller skolan, köra hem efter en natt i baren eller titta på film under en lång bilresa. Dessa saker har tidigare bara varit möjligt genom att ha en annan person som kör bilen, tills nu. Självstyrande bilar håller på att introduceras till samhället mer och mer och kommer vara det nästa stora steg som bilindustrin kommer ta. Det finns flera faktorer som bestämmer hur snabbt den här teknologin kommer adopteras. Säkerhet, pålitlighet, etik och kostnad för att nämna några. Det här projektet kommer att fokusera på kostnadsaspekten gällande självstyrande bilar, genom att undersöka om ultraljudssensorer kan användas vid utvecklandet av en självstyrande bil och på så sätt kunna hålla nere kostnaderna och nå en bredare kundbas. För att fastställa detta byggdes en småskalig prototypbil och testades i en simulation av motorvägskörning. Resultatet av testerna visar att bilen kunde köra sig själv med endast information från ultraljudssenorerna.

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Дядечко, Алла Миколаївна, Алла Николаевна Дядечко, Alla Mykolaivna Diadechko, and E. I. Ponomarenko. "Google's self-driving car." Thesis, Видавництво СумДУ, 2011. http://essuir.sumdu.edu.ua/handle/123456789/13466.

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Yanamanamanda, Srinivasa Rao. "Study of car-leading behavior in passing maneuvers on freeways /." free to MU campus, to others for purchase, 2003. http://wwwlib.umi.com/cr/mo/fullcit?p1418078.

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Appiah, Joseph. "Modelling and simulation of car following driving behaviour." Thesis, Edinburgh Napier University, 2018. http://researchrepository.napier.ac.uk/Output/1253614.

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Driver behaviour has become an important aspect of transport research and over the years a considerable number of car following models have been developed. However, many of these models do not accurately simulate actual driving behaviour due to a lack of suitable qualitative and quantitative data. Moreover, the inclusion of socioeconomic variables in the existing models to ascertain the effect on car following behaviour is lacking. This research underlines the need to further investigate driving behaviour and car following models and to develop techniques to provide a better understanding of driver-vehicle interactions during car following. It investigates data collection techniques and develop better techniques to enhance and improve the collection of microscopic driver behaviour and traffic flow data. This study developed a novel data collection technique which involved instrumenting a private vehicle with front and rear advanced radar sensors, both forward and rear facing video-audio recorders connected to GPS based time series speed and distance measurement devices, an in-vehicle computer logging vehicle speed and a CAN monitoring interface user program to provide real time monitoring and display of data. This system has been utilised to collect a more enhanced and reliable microscopic driver behaviour data in three consecutive vehicles movements which represents an improvement from previously used systems. Three different versions of the GHR car following model were produced for: car following car, truck following car and car following truck. Further analysis of the GHR model showed that in the case of car following car, car drivers responses to the lead car are more obviously stronger than in the case of truck following a car. A distance-based car following model and distance-based two-leader car following model that predict the safe following distance of following vehicles were developed to provide a better understanding of driver behaviour. An extension of these models to include gender, corridor (road) type and vehicle occupancy showed evidence of statistical significance of these variables on driver behaviour. A bus following model that predicts the “following distance” also has been calibrated to describe the interactions between a bus and a car within urban-rural driving conditions. In addition, data analysis showed that drivers were inconsistent with their driving behaviour and that there was variability in driving behaviour across the drivers observed in keeping a safe or desired following distance. This study provides a platform for a number of future research agendas including data collection techniques for collection of driver behaviour data; evaluation of different ITS technologies; impact assessment of ACC on driver safety and improvement of traffic microscopic simulation tools in order to strengthen their ability to simulate realistic transport problems for efficient and effective transportation systems.

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Backman, Martin. "Driving skill : the role of car control behavior /." Turku : Turun yliopisto, 2001. http://catalogue.bnf.fr/ark:/12148/cb402215287.

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Kawaguchi, Nobuo, Shigeki Matsubara, Kazuya Takeda, and Fumitada Itakura. "CIAIR In-Car Speech Corpus : Influence of Driving Status." IEICE, 2005. http://hdl.handle.net/2237/7815.

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Konnanov, P. "Microprocessor evaluation of drug effects on car driving skills." Thesis, University of Salford, 1985. http://ethos.bl.uk/OrderDetails.do?uin=uk.bl.ethos.356178.

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Sakamoto, Ryota. "Is driving a car a risk for Legionnaires' disease?" Kyoto University, 2009. http://hdl.handle.net/2433/126450.

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Rosenfield, Adam (Adam Isaac). "Driving change : how workplace benefits can nudge solo car commuters toward sustainable modes." Thesis, Massachusetts Institute of Technology, 2018. http://hdl.handle.net/1721.1/117826.

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Thesis: M.C.P., Massachusetts Institute of Technology, Department of Urban Studies and Planning, 2018.
Thesis: S.M. in Transportation, Massachusetts Institute of Technology, Department of Civil and Environmental Engineering, 2018.
This electronic version was submitted by the student author. The certified thesis is available in the Institute Archives and Special Collections.
Cataloged student-submitted from PDF version of thesis.
Includes bibliographical references (pages 223-229).
This thesis investigates the role that employer benefits can play in encouraging commuters to use sustainable modes of transportation, motivated by the increasing cost of parking provision at urban workplaces and the broader potential for travel demand management strategies to mitigate traffic congestion and pollution. In this research, case studies are conducted at two urban employers in Greater Boston. At the Massachusetts Institute of Technology (MIT) and at Partners HealthCare, employee transportation benefits were recently enhanced to encourage alternatives to driving. MIT, concerned about an upcoming reduction in parking supply, announced in 2016 that it would provide its more than ten thousand staff with a fully-subsidized local transit pass. In an agreement with the transit agency, MIT only pays for transit trips taken, thereby avoiding the expense of monthly passes for non-riders while providing universality of coverage. For drivers, MIT eliminated annual parking permits in favor of daily, pay-as-you-park pricing to encourage multi-modality. The net result was an eight percent reduction in parking demand in the first year, at a net cost to MIT of about $200 per employee. Transit agency revenue increased as ridership among MIT employees rose approximately ten percent. Partners HealthCare was motivated to reduce its employee parking demand in the midst of consolidating fourteen administrative worksites to a new facility in Somerville, MA, and faced cityƯimposed parking restrictions. Like MIT, it introduced daily parking pricing, but tied the rates to employee income as an equity measure. Unlike MIT, it did not offer a universal transit pass, but increased monthly pass subsidies. With the new facility located along the MBTA Orange Line, there was a marked increase in transit ridership among employees who used to work in the suburbs, and today parking demand is well below anticipated levels. The thesis supplements these case studies with a randomized controlled experiment on two thouƯsand MIT car commuters, investigating how behavioral 'nudges' can further encourage reductions in driving. While no statistically significant reductions in parking were observed during the experiment, the combination of token monetary rewards and informational nudges appeared most effective at shifting travel behavior. This research illustrates the potential for travel demand management strategies to influence commuter mode choice, but reinforces the importance of carefully considering implementation deƯtails such as cost salience and user experience. Long-term success appears dependent on building a constituency of support for such strategies among employer, commuter and government stakeholders.
by Adam Rosenfield.
M.C.P.
S.M. in Transportation

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Roine, Matti. "Accident risks of car drivers in wintertime traffic /." Espoo [Finland] : Technical Research Centre of Finland, 1999. http://www.vtt.fi/inf/pdf/publications/1999/P401.pdf.

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Книги з теми "Car driving"

Car craft. Sydney: The Author, 1988.

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Frère, Paul. Sports car and competition driving. 2nd ed. Sparkford, Nr. Yeovil: P. Stephens, 1993.

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Sports car and competition driving. Cambridge, Mass: Robert Bentley Publishers, 1992.

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Arif, Mohammad. Theory test for car driving. Bury: Mohammad Arif, 2000.

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Harding, Davis Richard. The scarlet car. Toronto: McLeod & Allen, 1994.

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Andy, Wilman, ed. Richard Hammond's car confidential. London: Weidenfeld & Nicolson, 2006.

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Your first car: Owning, driving, maintaining. Shepperton: I. Allan, 1993.

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McCarthy, Laura Flynn. Your quick & easy car care and safe driving handbook. Garden City, N.Y: Nelson Doubleday, 1988.

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Your quick & easy car care and safe driving handbook. New York: Doubleday, 1990.

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Lawrence, Nathan, ed. Lawrence Nathan's car driving in 2 weeks. 2nd ed. Kingswood: Elliot Right Way Books, 1989.

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Частини книг з теми "Car driving"

Kumar, Ankit, Mayukh Mukherjee, and Preetam Mukhopadhyay. "Self Driving Car." In Computational Intelligence in Pattern Recognition, 685–91. Singapore: Springer Singapore, 2019. http://dx.doi.org/10.1007/978-981-13-9042-5_59.

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Sauda, Valerie C. "Driving in My Car." In Case Studies in Gerontological Nursing for the Advanced Practice Nurse, 343–49. West Sussex, UK: John Wiley & Sons, Inc., 2013. http://dx.doi.org/10.1002/9781118785607.ch38.

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Alves, João Pedro, N. M. Fonseca Ferreira, António Valente, Salviano Soares, and Vítor Filipe. "Autonomous Driving Car Competition." In Robotics in Education, 356–63. Cham: Springer International Publishing, 2019. http://dx.doi.org/10.1007/978-3-030-26945-6_32.

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Fraedrich, Eva, and Barbara Lenz. "Taking a Drive, Hitching a Ride: Autonomous Driving and Car Usage." In Autonomous Driving, 665–85. Berlin, Heidelberg: Springer Berlin Heidelberg, 2016. http://dx.doi.org/10.1007/978-3-662-48847-8_31.

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van Dijk, Ingmar, and Ruud Wijnands. "Test Driving the Wrong Car." In Lecture Notes in Computer Science, 250–52. Berlin, Heidelberg: Springer Berlin Heidelberg, 2007. http://dx.doi.org/10.1007/978-3-540-73101-6_47.

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Hubele, Norma Faris. "The Self-Driving Car Debate." In Backseat Driver, 143–58. Boca Raton: Chapman and Hall/CRC, 2022. http://dx.doi.org/10.1201/9781003035343-11.

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Cveticanin, Livija, and Ivona Ninkov. "Sensors in Self-Driving Car." In Machine and Industrial Design in Mechanical Engineering, 595–604. Cham: Springer International Publishing, 2022. http://dx.doi.org/10.1007/978-3-030-88465-9_60.

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Santanen, Eino. "Should a Self-driving Car." In The Ethos of Digital Environments, 22–23. Title: The ethos of digital environments : technology, literary theory and philosophy / edited by Susanna Lindberg and Hanna-Riikka Roinen. Description: New York : Routledge, 2021. | Series: Perspectives on the non-human in literature and culture: Routledge, 2021. http://dx.doi.org/10.4324/9781003123996-2.

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Gupta, Sahil, Divya Upadhyay, and Ashwani Kumar Dubey. "Self-Driving Car Using Artificial Intelligence." In Lecture Notes in Mechanical Engineering, 521–33. Singapore: Springer Singapore, 2019. http://dx.doi.org/10.1007/978-981-13-6577-5_49.

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Kumar, Rishabh, Tarun Sharma, Renu Chaudhary, and Vibhor Singh. "Self-Driving Car Using Machine Learning." In Emerging Technologies in Data Mining and Information Security, 709–19. Singapore: Springer Nature Singapore, 2022. http://dx.doi.org/10.1007/978-981-19-4193-1_69.

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Тези доповідей конференцій з теми "Car driving"

Brodsky, Warren, and Matan Ziv. "Car-aoke: Vocal Performances Indicate Distraction Effects of In-Car Music." In Driving Assessment Conference. Iowa City, Iowa: University of Iowa, 2015. http://dx.doi.org/10.17077/drivingassessment.1545.

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Sauer, Craig W., George J. Andersen, and Asad Saidpour. "Car Following by Optical Parameters." In Driving Assessment Conference. Iowa City, Iowa: University of Iowa, 2005. http://dx.doi.org/10.17077/drivingassessment.1113.

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Tateyama, Yoshisuke, Yukihiro Mori, Keiichi Yamamoto, Tetsuro Ogi, Hidekazu Nishimura, Noriyasu Kitamura, and Harumi Yashiro. "Car Driving Behaviour Observation Using an Immersive Car Driving Simulator." In 2010 International Conference on P2P, Parallel, Grid, Cloud and Internet Computing (3PGCIC). IEEE, 2010. http://dx.doi.org/10.1109/3pgcic.2010.68.

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Boer, Erwin R., Nicholas J. Ward, Michael P. Manser, Tomohiro Yamamura, and Nobuyuki Kuge. "Driver Performance Assessment with a Car Following Model." In Driving Assessment Conference. Iowa City, Iowa: University of Iowa, 2005. http://dx.doi.org/10.17077/drivingassessment.1195.

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Boer, Erwin R., Stéphane Caro, and Viola Cavallo. "A Cybernetic Perspective on Car Following in Fog." In Driving Assessment Conference. Iowa City, Iowa: University of Iowa, 2007. http://dx.doi.org/10.17077/drivingassessment.1275.

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Merat, Natasha, and A. Hamish Jamson. "How Do Drivers Behave in a Highly Automated Car?" In Driving Assessment Conference. Iowa City, Iowa: University of Iowa, 2009. http://dx.doi.org/10.17077/drivingassessment.1365.

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Cavallo, Viola, and Maria Pinto. "Evaluation of Motorcycle Conspicuity in a Car DRL Environment." In Driving Assessment Conference. Iowa City, Iowa: University of Iowa, 2011. http://dx.doi.org/10.17077/drivingassessment.1410.

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Lester, Benjamin D., Sarah D. Hacker, Shaun P. Vecera, and Matthew Rizzo. "Serialization of Behavior During Car Following in Older Drivers." In Driving Assessment Conference. Iowa City, Iowa: University of Iowa, 2015. http://dx.doi.org/10.17077/drivingassessment.1555.

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Noel, Elliott, Blair Nonnecke, and Lana Trick. "Evaluating First-time and Infrequent Use of In-Car Navigation Devices." In Driving Assessment Conference. Iowa City, Iowa: University of Iowa, 2005. http://dx.doi.org/10.17077/drivingassessment.1187.

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Caro, Stéphane, Viola Cavallo, Christian Marendaz, Erwin Boer, and Fabrice Vienne. "The Influence of Fog on Motion Discrimination Thresholds in Car Following." In Driving Assessment Conference. Iowa City, Iowa: University of Iowa, 2007. http://dx.doi.org/10.17077/drivingassessment.1274.

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Takada, Hajime, Tomohiro Yokota, and Yoshifusa Matuura. Influence of Auditory Information in Driving a Car. Warrendale, PA: SAE International, May 2005. http://dx.doi.org/10.4271/2005-08-0245.

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Miyasaka, Tsutomu, Masaaki Taniguchi, and Hiroshi Sambuichi. Potentiality of Effects by Fuel Conservation Driving in Automatic Transmission Car. Warrendale, PA: SAE International, May 2005. http://dx.doi.org/10.4271/2005-08-0382.

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Takada, Hajime, Yoshifusa Matsuura, and Hajime Nada. Study on the Influence of Visual Information Processing in Driving a Car. Warrendale, PA: SAE International, May 2005. http://dx.doi.org/10.4271/2005-08-0246.

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Guo, Xiaolei, Dayu Wan, Dongfang Liu, Christos Mousas, and Yingjie Chen. A Virtual Reality Framework to Measure Psychological and Physiological Responses of the Self-Driving Car Passengers. Purdue University, 2022. http://dx.doi.org/10.5703/1288284317567.

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Utsugi, Akio, and Motoyuki Akamatsu. Analysis of Car-Following Behavior Using Dynamic Probabilistic Models~Identification of Driving Mode Transition Using Dynamic Bayesian Networks. Warrendale, PA: SAE International, May 2005. http://dx.doi.org/10.4271/2005-08-0241.

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Kumar, Anil R., and Hannah Bowman. Understanding the Safety and Usability of Personal Vehicles for Non-Driving Individuals with Disabilities and their Families/Care Providers. Mineta Transportation Institute, October 2022. http://dx.doi.org/10.31979/mti.2022.2110.

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The connections between shared personal vehicles of individuals with disabilities (IWDs) and their household family members play an important role in the mobility, overall health, and well-being of all involved actors, yet this topic remains mostly overlooked within publicly available research. Families that include a non-driving IWD are more likely to be low-income, and often struggle with the costs of operating a family car but, due to insufficient public transportation options, they own vehicles despite their prohibitive cost. This exploratory study utilized the Systems Engineering Initiative for Patient Safety (SEIPS) model, a framework focused on assessing the interplaying sociotechnical factors that contribute toward work-systems to gain a holistic understanding of the factors that influence household vehicles, safety, and a sense of well-being for non-driving IWDs and their household family members. A combined effort of surveys, interviews, qualitative coding, and statistical analysis (including one-way ANOVA) revealed a series of influential factors, including: (1) slow bureaucratic processes for vehicle funding; (2) error-prone modifications including lift and tie-downs; (3) miscommunications between IWDs and family members; and (4) residential area development and subsequent social support. Findings highlight the need for improved access to government funding, more reliable modification equipment, and interior vehicle designs that consider better social integration for IWDs.

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., Susilawati. Will self-driving cars solve traffic congestion? Edited by Sara Phillips. Monash University, February 2023. http://dx.doi.org/10.54377/1f93-023d.

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Ostrovsky, Michael, and Michael Schwarz. Carpooling and the Economics of Self-Driving Cars. Cambridge, MA: National Bureau of Economic Research, February 2018. http://dx.doi.org/10.3386/w24349.

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Mudryj, Igor, and Igor Ivaneіko. The Use of Small Drilling Equipment in the Arrangement of Pile Foundations in Compressed Conditions. Intellectual Archive, September 2022. http://dx.doi.org/10.32370/ia_2022_09_11.

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The procedure for finding technological parameters for the installation of pile foundations with small-sized drilling rigs, when developing design and technological documentation in compressed construction conditions, is considered. Methodological approaches to the choice of technologies for the construction of pile foundations are shown, depending on the dimensions of the small-sized drilling machines used, the required area for their placement, storage areas, and auxiliary equipment. in compressed conditions of construction. The existing normative documents do not set out separate requirements for the development of projects for the execution of works in compressed construction conditions, these norms do not provide for the definition of rational erection schemes for the selected set of mechanization in the dimensions of a specific construction site, which is characterized by various restrictions and obstacles. The proposed requirements for the use of mechanization methods in the conditions of compacted buildings during the installation of pile foundations based on a preliminary analysis of the parameters of the construction site: engineering and geological condition of the site; internal brevity of the designed structure; external brevity of the construction site; dimensions of the driving car; sites for the location of additional equipment, warehouses, unloading areas. Taking into account practical experience in the development of work projects and the analysis of current regulatory documents, made it possible to establish the main requirements for the use of small-sized drilling rigs in densely built-up conditions.

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Reichmuth, David, Jessica Dunn, and Don Anair. Driving Cleaner: Electric Cars and Pickups Beat Gasoline on Lifetime Global Warming Emissions. Union of Concerned Scientists, July 2022. http://dx.doi.org/10.47923/2022.14657.

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Passenger cars and trucks are one of the largest sources of global warming emissions in the US. Electric vehicles (EVs) have the potential to dramatically reduce these emissions, especially when charged by low-carbon renewable electricity. New UCS analysis finds that over its lifetime—from manufacturing to operation to disposal—the average new battery electric vehicle produces more than 50 percent less global warming pollution than a comparable gasoline or diesel vehicle. Based on the most recently available data on power plant emissions and EV sales, driving the average EV in the US produces global warming emissions equal to a gasoline vehicle that gets 91 miles per gallon. To speed climate benefits and to encourage more drivers to choose electric vehicles, the report recommends policy changes and investments to bring even more renewable energy onto the grid, develop robust battery recycling programs to help reduce manufacturing impacts, and make EVs more accessible and affordable.

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