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Littérature scientifique sur le sujet « Car driving »

Auteur : Grafiati

Publié le 4 juin 2021

Mis à jour le 9 juin 2025

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Articles de revues sur le sujet "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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Tamil Selvan B and Srirangarajalu N. "Self-Driving Car." international journal of engineering technology and management sciences 7, no. 4 (2023): 275–80. http://dx.doi.org/10.46647/ijetms.2023.v07i04.038.

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An autonomous vehicle is a self-driving vehicle that uses various sensors, cameras, and advanced algorithms to sense the surrounding environment, make decisions and navigate without human intervention. These vehicles are rapidly evolving and have the potential to revolutionize the transportation industry by offering increased safety, efficiency, and accessibility. Autonomous vehicles are expected to bring significant benefits such as reduced traffic congestion, lower emissions, and increased mobility for people who cannot drive. However, there are still many challenges that need to be addressed, such as regulatory and ethical issues, cybersecurity concerns and infrastructure requirements. Despite these challenges, autonomous vehicles are poised to become an integral part of our future transportation system, changing the way we move and interact with our environment.

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Patinge, Sushant. "Self-Driving Car." International Journal for Research in Applied Science and Engineering Technology 11, no. 5 (May 31, 2023): 240–44. http://dx.doi.org/10.22214/ijraset.2023.50830.

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Abstract: The evolution of Internet of Things has served up the catalyst in the field of technology. Automobile manufacturers such as Ford, Audi, Hyundai, Tesla, and other companies are investing billions of dollars in autonomous vehicle driving research. According to the new information, in the next 30 years, this fast-developing industry will be worth $ 7 trillion. This will create a shift on the way cities are planned, as less parking spots will be needed, and secondly, in a developed city with most of its vehicles being connected and autonomous, traffic optimization will be able to be strongly applied by coordinating movement of the vehicles..

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Asati, Sudiksha. "Self Driving Car." International Journal of Science, Engineering and Technology 12, no. 2 (February 14, 2024): 1–6. http://dx.doi.org/10.61463/ijset.vol.12.issue2.138.

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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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Thakur, Deepak Kumar. "Automatic Self Driving Car." INTERANTIONAL JOURNAL OF SCIENTIFIC RESEARCH IN ENGINEERING AND MANAGEMENT 08, no. 04 (April 15, 2024): 1–5. http://dx.doi.org/10.55041/ijsrem30742.

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This project deals with building an autonomous car that can travel safely and intelligently avoiding the risk of human errors. This raspberry Pi based project can detect the obstacles & traffic light. It can compare the data processed with the data provided to it and is able to take an intelligent decision whether to stop or continue its present path. Important components involved in this project are - the hardware platform which includes raspberry pi board, all the hardware like pi camera and the ultrasonic sensor for improved efficiency & the camera used along with an ultrasonic sensor to provide necessary data from the world for real time processing and application. Second being the cloud platform which will be basically used to train our raspberry pi board for real time applications. Cloud helps us to test as well as train better tracking and decision models & helps in providing the offline computing and storage capabilities for vehicle. Basically, it will be used to train the processor to differentiate between positive (green signal) and negative (red signal) images using various thousands of such signal images as an example. The third and most important part includes the algorithms for perception, control, localization, and recognition. Key Words: Raspberry pi, L293D Driver, Machine Learning, Open CV, Ultrasonic Sensor & Pi Camera.

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Ashutosh, Dubey, and N. Prabakaran. "SELF-DRIVING CAR SIMULATION." International Research Journal of Computer Science VII, no. V (May 20, 2020): 66–69. https://doi.org/10.26562/irjcs.2020.v0705.002.

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For the recent years, there has been a flood of enthusiasm for self-driving vehicles. This is because of forward leaps in the field of deep learning where deep neural networks are trained to perform tasks that usually require human intervention.CNN apply models to distinguish examples and highlights in pictures, making them helpful in the field of Computer Vision. Instances of these are object detection, image classification, image captioning, etc. In this project, we have prepared a CNN utilizing pictures captured by a simulated vehicle so as to drive the vehicle self-sufficiently. The CNN learns unique and distinct features from the images and generates steering predictions permitting the vehicle to drive without a human. For testing purposes and preparing the dataset the Unity based simulator provided by Udacity was used.

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Thèses sur le sujet "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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Livres sur le sujet "Car driving"

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

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

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1945-, Collett Peter, ed. Driving passion: The psychology of the car. London: Cape, 1986.

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Peter, Collett, ed. Driving passion: The psychology of the car. Boston: Faber and Faber, 1987.

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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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Middlesex University. Library of Historic Advertising., ed. Driving it home: 100 years of car advertising. [London, England?]: Middlesex University Press, 2008.

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Daniels, Jeff. Driving force: The evolution of the car engine. Sparkford, Yeovil, Somerset: Haynes Pub., 2002.

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Lee-Gosselin, Martin. International study of car-use. [Montréal]: The Centre, 1990.

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Chapitres de livres sur le sujet "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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Frömmig, Lars. "Driving Dynamics Basics." In Basic Course in Race Car Technology, 89–140. Wiesbaden: Springer Fachmedien Wiesbaden, 2023. http://dx.doi.org/10.1007/978-3-658-38470-8_5.

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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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Aiswarya, P., and N. V. Ravindhar. "Advanced Self-Driving Car Simulation." In MULTIDISCIPLINARY APPROACHES FOR SUSTAINABLE DEVELOPMENT, 176–82. London: CRC Press, 2024. http://dx.doi.org/10.1201/9781003543633-28.

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Li, Bo. "Artificial Intelligence in Car Driving." In Proceedings of the 2023 3rd International Conference on Social Development and Media Communication (SDMC 2023), 238–44. Paris: Atlantis Press SARL, 2023. http://dx.doi.org/10.2991/978-2-38476-178-4_29.

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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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Actes de conférences sur le sujet "Car driving"

Hindoriya, Dhairya, and Pratvina Talele. "Self-Driving Car." In 2024 First International Conference on Technological Innovations and Advance Computing (TIACOMP), 314–19. IEEE, 2024. http://dx.doi.org/10.1109/tiacomp64125.2024.00059.

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Chethana, Savarala, Sreevathsa Sree Charan, Vemula Srihitha, D. Radha, and Amudha J. "Autonomous Car Driving: Advanced Maneuvers Training." In 2024 IEEE North Karnataka Subsection Flagship International Conference (NKCon), 1–6. IEEE, 2024. https://doi.org/10.1109/nkcon62728.2024.10774948.

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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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Rapports d'organisations sur le sujet "Car driving"

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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Bansal, Prateek, and Rubal Dua. How Responsive Are New Car Buyers in India and China to Factors Driving Fuel Consumption? King Abdullah Petroleum Studies and Research Center, March 2023. http://dx.doi.org/10.30573/ks--2022-dp20.

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China and India, the world’s two most populous developing economies, are also among the largest automotive markets and carbon emitters. To reduce carbon emissions from the passenger car sector, both countries have considered various policy levers that affect fuel price, car prices and fuel economy. This study estimates the responsiveness of new car buyers in China and India to such policy levers and drivers, including income.

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Meda, Pranav, Aubrey Victoria Contreras, Wei-Hsiang Lo, Gaojian Huang, and Yue Luo. Insights for the Future of Car Rental and Ridesharing: Driving Behavior Across Different Levels of Automation. Mineta Transportation Institute, February 2025. https://doi.org/10.31979/mti.2024.2427.

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Autonomous vehicles are reshaping the car rental and ridesharing industries, potentially leading to a unified model of on-demand transportation suitable for both uncommon (e.g., business trips) and daily commuting. An exploratory study of human behavior towards autonomous vehicles can uncover the challenges and opportunities inherent in different levels of vehicle automation. This study aims to (a) identify behavioral differences in drivers operating vehicles at various levels of automation and (b) explore how these behaviors vary with different assistance feature styles, specifically between risky and conservative modes. Human-subject experiments were conducted among twelve participants (aged 21 to 29, including four women) to complete simulated driving trials under different levels of automation (Levels 0, 3, and 5), assistance features (risky and conservative modes), and driving activities (lane keeping and lane changing). Measures of driving performance, body posture, and eye movement were recorded during each trial. The data implied that: (1) driving performance: drivers exhibited stable speed and steering control at Levels 0 and 5, while speed decreased and steering variability increased obviously at Level 3; (2) driving posture: a tense posture was noted at Level 0, with potential posture preparation needed for takeover actions at Level 3; (3) eye movement: active scanning and continuous control were maintained at Level 0, with notable shifts in attention at Levels 3 and 5. Further research could focus on conducting on-road tests, using equipment designed for on-road tests and broadening the demographic range of participants.

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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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Yaseen, Lama, Nourah Al-Hosain, Ibrahem Shatnawi, and Abdelrahman Muhsen. Impact of Urban Traffic on Fuel Consumption Leveraging IoT Data: Case Study of Riyadh City. King Abdullah Petroleum Studies and Research Center, December 2024. https://doi.org/10.30573/ks--2024-dp72.

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This study explores the rising trend of traffic congestion in Riyadh and its impact on fuel consumption for passenger cars amid the challenges of rapid urbanization and increasing vehicle use. By utilizing real-time floating car data (FCD) collected by vehicles equipped with Global Positioning System (GPS) technology and communication systems, this study illustrates the potential of the Internet of Things (IoT) and smart city technologies in developing intelligent transportation systems and improving urban mobility management. A spatial analysis of the traffic flow dynamics in Riyadh, focusing on selected primary highways, reveals that driving on high-capacity roads tends to increase fuel consumption. We conducted an analysis at a mesoscopic level, representing traffic congestion in Riyadh on high-capacity roads. It shows that traffic congestion leads to up to a 29% increase in fuel consumption, primarily due to frequent stop-and-go driving behavior, reducing overall fuel efficiency. This study enhances our understanding of urban traffic patterns, providing policymakers with data-driven insights to help them create more sustainable road planning strategies to address the specific needs and challenges of urban mobility in cities.

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Shen, Kevin, Dave Cooke, Emmanuell De Barros, Mike Christensen, Kim Mitchell, and Dorothy Wiley. Freedom to Move: Investing in Transportation Choices for a Clean, Prosperous, and Just Future. Union of Concerned Scientists, October 2024. http://dx.doi.org/10.47923/2024.15594.

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More transportation options such as transit, walking and biking are good for the environment, economy, and social equity. • A system with improved transportation options and reduced driving could save up to $175 billion in energy infrastructure and $125 billion in public health costs through 2050, presenting a more effective climate solution than the current car-dependent model. • The auto and oil industries have a vested interest in car dependence, currently receiving more than 75% of public and private transportation spending and have lobbied for decades to prioritize cars over a more complete and affordable set of transportation options. • Science-based policies that prioritize more transportation choices align with community-based solutions where local advocates have long fought for a transportation system that prioritizes people over industry interests.

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Bäumler, Maximilian, Günther Prokop, Matthias Lehmann, and Linda Dziuba-Kaiser. Use Information You Have Never Observed Together: Data Fusion as a Major Step Towards Realistic Test Scenarios. TU Dresden, 2020. http://dx.doi.org/10.26128/2024.3.

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Scenario-based testing is a major pillar in the development and effectiveness assessment of automated driving systems. Thereby, test scenarios address different information layers and situations (normal driving, critical situations and accidents) by using different databases. However, the systematic combination of accident and / or normal driving databases into new synthetic databases can help to obtain scenarios that are as realistic as possible. This paper shows how statistical matching (SM) can be applied to fuse different categorical accident and traffic observation databases. Hereby, the fusion is demonstrated in two use cases, each featuring several fusion methods. In use case 1, a synthetic database was generated out of two accident data samples, whereby 78.7% of the original values could be estimated correctly by a random forest classifier. The same fusion using distance-hot-deck reproduced only 67% of the original values, but better preserved the marginal distributions. A real-world application is illustrated in use case 2, where accident data was fused with over 23,000 car trajectories at one intersection in Germany. We could show that SM is applicable to fuse categorical traffic databases. In future research, the combination of hotdeck- methods and machine learning classifiers needs to be further investigated.

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