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Relevant bibliographies by topics / Truck driving

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Academic literature on the topic 'Truck driving'

Author: Grafiati

Published: 4 June 2021

Last updated: 28 July 2024

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Journal articles on the topic "Truck driving"

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Lee, Yongki, Taewon Ahn, Chanhwa Lee, Sangjun Kim, and Kihong Park. "A Novel Path Planning Algorithm for Truck Platooning Using V2V Communication." Sensors 20, no. 24 (December 8, 2020): 7022. http://dx.doi.org/10.3390/s20247022.

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In truck platooning, the leading vehicle is driven manually, and the following vehicles run by autonomous driving, with the short inter-vehicle distance between trucks. To successfully perform platooning in various situations, each truck must maintain dynamic stability, and furthermore, the whole system must maintain string stability. Due to the short front-view range, however, the following vehicles’ path planning capabilities become significantly impaired. In addition, in platooning with articulated cargo trucks, the off-tracking phenomenon occurring on a curved road makes it hard for the fo

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Sollins, Brandon, Dar-Wei Chen, Lauren Reinerman-Jones, and Ron Tarr. "Truck Driving Distractions." Proceedings of the Human Factors and Ergonomics Society Annual Meeting 58, no. 1 (September 2014): 2171–75. http://dx.doi.org/10.1177/1541931214581456.

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Geng, Zhaoshi, Xiaofeng Ji, Rui Cao, Mengyuan Lu, and Wenwen Qin. "A Conflict Measures-Based Extreme Value Theory Approach to Predicting Truck Collisions and Identifying High-Risk Scenes on Two-Lane Rural Highways." Sustainability 14, no. 18 (September 7, 2022): 11212. http://dx.doi.org/10.3390/su141811212.

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Collision risk identification and prediction is an effective means to prevent truck accidents. However, most existing studies focus only on highways, not on two-lane rural highways. To predict truck collision probabilities and identify high-risk scenes on two-lane rural highways, this study first calculated time to collision and post-encroachment time using high-precision trajectory data and combined them with extreme value theory to predict the truck collision probability. Subsequently, a traffic feature parameter system was constructed with the driving behavior risk parameter. Furthermore, m

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Zuo, Yong Gang, Zhu Xin Li, Jun Chen, Jing Yang, and Zhen Zhang. "Study on Driving Safety of Refueling Truck." Advanced Materials Research 774-776 (September 2013): 433–37. http://dx.doi.org/10.4028/www.scientific.net/amr.774-776.433.

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In this paper,lateral slip, longitudinal slip, lateral overturning and longitudinal overturning of refueling truck are studied according to petroleum sloshing of refueling truck tank driving on road,the rules that driving safety of refueling truck changes with related parameters variation when Refueling Truck is been driven on ramp is analyzed, the unsafe factors causing driving instability is found.

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Abdelkareem, Mohamed AA, Mina MS Kaldas, Mohamed Kamal Ahmed Ali, and Lin Xu. "Analysis of the energy harvesting potential–based suspension for truck semi-trailer." Proceedings of the Institution of Mechanical Engineers, Part D: Journal of Automobile Engineering 233, no. 11 (November 17, 2018): 2955–69. http://dx.doi.org/10.1177/0954407018812276.

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As the articulated trucks are mainly used for long distance transportations, the design of the suspension system became a major concern and a research hotspot not only for ride comfort and driving safety but also for energy consumption. Therefore, the objective of this study is to conduct a comprehensive parametrical–based conflict analysis between the ride comfort and road holding together with the potential power of the shock absorbers. The simulation analysis is performed using a 23 degree-of-freedom full truck semi-trailer mathematical model with random road surface model. The bounce and c

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Wang, Qun, Ruixin Zhang, Yangting Wang, and Shuaikang Lv. "Machine Learning-Based Driving Style Identification of Truck Drivers in Open-Pit Mines." Electronics 9, no. 1 (December 24, 2019): 19. http://dx.doi.org/10.3390/electronics9010019.

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The significance in constructing a driving style identification model for open-pit mine truck drivers is to reduce diesel consumption and improve training. First, we developed a driving behavior and mining truck condition monitoring system for an open-pit mine. Under heavy-load and no-load conditions of a mining truck, based on the same experimental truck and haulage road, the data of driving behavior and truck status of different drivers were collected. The driving style characteristic parameters of mining trucks under heavy-load and no-load conditions were constructed through Pearson correla

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Grinin, Valeriy, Evgeniy Shkarupelov, Aleksandr Muravev, Aleksandr Kartashov, Sergey Nazarenko, and Aleksandr Klimov. "Method for applying vehicle driving cycles to assess the durability of electromechanical transmissions of trucks." E3S Web of Conferences 402 (2023): 10008. http://dx.doi.org/10.1051/e3sconf/202340210008.

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The article discusses the relevance of the problem of calculating the elements of electromechanical transmissions of trucks for strength and durability. The main methods used for the formation of load conditions on electromechanical transmissions of trucks are given. The driving cycles of trucks used to obtain loads when calculating the durability of the elements of electromechanical transmissions are given. The universal driving cycles of vehicles obtained on the basis of the collection of statistical data on the movement of trucks are considered. A simulation model of the movement of a truck

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Kudo, Takahiko, and Michael H. Belzer. "Safe rates and unpaid labour: Non-driving pay and truck driver work hours." Economic and Labour Relations Review 30, no. 4 (October 15, 2019): 532–48. http://dx.doi.org/10.1177/1035304619880406.

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In the trucking industry, truck drivers’ duties include not only driving trucks but also non-driving labor. However, non-driving work is not necessarily paid. This article analyses how the payment for non-driving duties (non-driving pay) affects truck drivers’ work hours. Using the National Institute for Occupational Safety and Health Long-Haul Truck Driver survey, the study finds that remunerating drivers for non-driving duties decreases drivers’ work hours. Drivers who are paid for their non-driving labor may reach their target earnings in fewer work hours, leading them to refrain from worki

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Xin, Tian, Jinliang Xu, Chao Gao, and Zhenhua Sun. "Research on the speed thresholds of trucks in a sharp turn based on dynamic rollover risk levels." PLOS ONE 16, no. 8 (August 20, 2021): e0256301. http://dx.doi.org/10.1371/journal.pone.0256301.

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Truck rollover is a problem that seriously endangers the safety of human life. Under special conditions, when the driver takes a sharp turn, the truck is most prone to rollover. Speed seriously affects the driving stability of the truck in a sharp turn, but the calculation of the safe speed is not accurate enough at present. The aim of this paper is to develop a more accurate safe speed calculation method to avoid the truck rollover in a sharp turn. Firstly, the calculation formula of the rollover threshold was derived based on a theoretical model, then, the simulation tests were carried out.

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Akay, Abdullah E. "Determination of the Safest Route for Logging Trucks Based on Road Types and Conditions." Environmental Sciences Proceedings 3, no. 1 (November 13, 2020): 5. http://dx.doi.org/10.3390/iecf2020-08068.

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Hauling of wood-based forest products is a complex problem that requires evaluation of many alternative routes. Forest transportation has been generally done by using logging trucks with high carrying capacity. Logging truck driving is one of the dangerous occupations in forestry, particularly in Turkey, where forest lands are mostly located in mountainous regions with steep slopes. The safety risk of truck driving mainly depends on the road standards and conditions. The majority of the forest roads in Turkey have low standards that limit the maneuverability of logging trucks. In such conditio

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Dissertations / Theses on the topic "Truck driving"

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Mabbott, Nicholas. "Monitoring device for early warning signs of operator fatigue in open cut mines /." Access via Murdoch University Digital Theses Project, 2006. http://wwwlib.murdoch.edu.au/adt/browse/view/adt-MU20060809.93310.

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Douglas, Matthew Aaron Swartz Stephen M. "Commercial motor vehicle driver safety an application of ethics theory /." [Denton, Tex.] : University of North Texas, 2009. http://digital.library.unt.edu/permalink/meta-dc-11048.

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Desai, Anup. "Obstructive sleep apnoea and driver performance prevalence, correlates, and implications for driver fatigue /." Connect to full text, 2002. http://hdl.handle.net/2123/589.

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Thesis (Ph. D.)--University of Sydney, 2003.<br>Includes tables and questionnaires. Title from title screen (viewed Apr. 29, 2008). Submitted in fulfilment of the requirements for the degree of Doctor of Philosophy to the Faculty of Medicine. Degree awarded 2003; thesis submitted 2002. Includes bibliography. Also available in print form.

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Mohamedshah, Yusuf M. "Correlation of truck accidents with highway geometry /." This resource online, 1991. http://scholar.lib.vt.edu/theses/available/etd-10222009-125005/.

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Isaksson, Palmqvist Mia. "Model Predictive Control for Autonomous Driving of a Truck." Thesis, KTH, Skolan för elektro- och systemteknik (EES), 2016. http://urn.kb.se/resolve?urn=urn:nbn:se:kth:diva-187668.

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Platooning and cooperative driving can decrease the emissions of greenhouse gases and increase the traﬃc capacity of the roads. The Grand Cooperative Driving Challenge, GCDC, is a competition that will be held in May 2016 focusing on cooperative driving. A cornerstone in the cooperative driving is autonomous driving. The main objective of this thesis is to design a Model Predictive Control for a truck so it autonomously can perform the following tasks: follow a straight road, make a lane change and make a turn. Constraints are added to the vehicle states and the control signals. Additionally,

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Mancino, Francesco. "An embedded model predictive controller for optimal truck driving." Thesis, KTH, Reglerteknik, 2017. http://urn.kb.se/resolve?urn=urn:nbn:se:kth:diva-205649.

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An embedded model predictive controller for velocity control of trucks is developed and tested. By using a simple model of a heavy duty vehicle and knowledge about the slope of the road ahead, the fuel consumption while traveling near a set speed is diminished by almost 1% on an example road compared to a rule based speed control system. The problem is formulated as a look-ahead optimization problem were fuel consumption and total trip time have to be minimized. To find the optimal solution dynamic programming is used, and the whole code is designed to run on a Scania gearbox ECU in parallel w

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Chan, Sau Yin. "Cross-border truck driving : negotiating work control and gendering work identity /." View Abstract or Full-Text, 2003. http://library.ust.hk/cgi/db/thesis.pl?SOSC%202003%20CHAN.

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Thesis (M. Phil.)--Hong Kong University of Science and Technology, 2003.<br>Includes bibliographical references (leaves 95-104). Also available in electronic version. Access restricted to campus users.

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Tanner, Deborah Brae. "No margin for error driving the east shore of Flathead Lake /." Diss., [Missoula, Mont.] : The University of Montana, 2009. http://etd.lib.umt.edu/theses/available/etd-12302009-133705.

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Mohamedshah, Yusaf M. "Correlation of truck accidents with highway geometry." Thesis, Virginia Tech, 1991. http://hdl.handle.net/10919/45243.

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Growth trends in vehicle transportation for the year 1989 showed that truck travel has increased from 400 billion vehicle miles of travel to 600 billion vehicle miles from 1980 to 1989, a staggering 50% increase. If this trend continues, then truck travel will reach 800 billion vehicle miles by the end of the year 2000. This increase in truck travel poses operational and safety problems for both passenger vehicles and trucks. To improve the existing highway facilities for trucks as well as to determine the design standards for new truck facilities, an understanding of the relationship between

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Anibas, Judith. "A study of virtual simulation in a truck driver training program." Online version, 2008. http://www.uwstout.edu/lib/thesis/2008/2008anibasj.pdf.

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Thesis PlanB (M.S.)--University of Wisconsin--Stout, 2008.<br>ONLINE VERSION INCOMPLETE: APPENDIX A, P. 51; APPENDIX B, P. 52; APPENDIX I, P. 59; & APPENDIS J, P. 6 MISSING. Includes bibliographical references.

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Books on the topic "Truck driving"

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Royston, Angela. Truck trouble. New York, N.Y: DK Pub., 1998.

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Tudor, Ted N. Professional truck driving manual. Casper, WY: IAP, 1993.

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Clement, Nathan. Drive. Asheville, N.C: Front Street, 2008.

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Clement, Nathan. Drive. Asheville, N.C: Front Street, 2008.

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Truck trouble. London: Dorling Kindersley, 1998.

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California. Heavy Commercial Vehicle and Driver Safety Task Force. Status report on truck and truck driver safety: A report to the Governor and the Legislature, January 1991. [Sacramento, CA ] (PO Box 942898 Sacramento, 94298-0001): California Highway Patrol, 1991.

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Radlauer, Ed. Truck tech talk. Chicago: Childrens Press, 1986.

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Radlauer, Ed. Truck tech talk. Chicago: Childrens Press, 1986.

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Savage, Jeff. Truck and tractor pulling. Minneapolis: Capstone Press, 1996.

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Safety, Canada Road. Driving behaviour and characteristics of heavy duty truck operators in Canada. Ottawa: Transport Canada,Safety, 1987.

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Book chapters on the topic "Truck driving"

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Turri, Valerio, Jonas Mårtensson, and Karl H. Johansson. "Automated Truck Driving." In Encyclopedia of Systems and Control, 106–15. Cham: Springer International Publishing, 2021. http://dx.doi.org/10.1007/978-3-030-44184-5_100117.

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Turri, Valerio, Jonas Mårtensson, and Karl H. Johansson. "Automated Truck Driving." In Encyclopedia of Systems and Control, 1–10. London: Springer London, 2021. http://dx.doi.org/10.1007/978-1-4471-5102-9_100117-1.

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de Bruin, Gerrit Jan, Cor J. Veenman, H. Jaap van den Herik, and Frank W. Takes. "Understanding Dynamics of Truck Co-Driving Networks." In Complex Networks and Their Applications VIII, 140–51. Cham: Springer International Publishing, 2019. http://dx.doi.org/10.1007/978-3-030-36683-4_12.

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de Bruijn, Freek, and Jacques Terken. "Truck Drivers as Stakeholders in Cooperative Driving." In Lecture Notes in Computer Science, 290–98. Cham: Springer International Publishing, 2014. http://dx.doi.org/10.1007/978-3-319-14112-1_23.

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Kuhn, Andreas, José Carmona, and Elvira Thonhofer. "Research Design and Evaluation Strategies for Automated Driving." In Energy-Efficient and Semi-automated Truck Platooning, 41–54. Cham: Springer International Publishing, 2022. http://dx.doi.org/10.1007/978-3-030-88682-0_4.

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AbstractAutomated driving, in general, and platooning, in particular, represent a highly active field of research. The idea to automate traffic is closely related to high expectations in both individual and public transport. However, the complexity of automated driving requires methods beyond the traditional development approaches. This chapter describes a state-of-the-art methodology to organise and systematically address a comprehensive set of research questions in the context of truck platooning. Following best practices, an evaluation design is presented, which ensures the alignment of research efforts with the actual research agenda, that is, to answer the right questions. Specifically, the benefits of automated driving and their conflicting relationships are explored and the entities that affect automated driving performance and their interactions are presented. Finally, a solution concept that adequately addresses the complexity and the stochastic nature of the problem is presented. The solution concept consists of several key methods such as scenario-based design and stochastic simulation, data mining and complexity and robustness management.

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Kuhn, Andreas, José Carmona, and Elvira Thonhofer. "Research Design and Evaluation Strategies for Automated Driving." In Energy-Efficient and Semi-automated Truck Platooning, 41–54. Cham: Springer International Publishing, 2022. http://dx.doi.org/10.1007/978-3-030-88682-0_4.

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AbstractAutomated driving, in general, and platooning, in particular, represent a highly active field of research. The idea to automate traffic is closely related to high expectations in both individual and public transport. However, the complexity of automated driving requires methods beyond the traditional development approaches. This chapter describes a state-of-the-art methodology to organise and systematically address a comprehensive set of research questions in the context of truck platooning. Following best practices, an evaluation design is presented, which ensures the alignment of research efforts with the actual research agenda, that is, to answer the right questions. Specifically, the benefits of automated driving and their conflicting relationships are explored and the entities that affect automated driving performance and their interactions are presented. Finally, a solution concept that adequately addresses the complexity and the stochastic nature of the problem is presented. The solution concept consists of several key methods such as scenario-based design and stochastic simulation, data mining and complexity and robustness management.

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Bruin, Gerrit Jan de, Cor J. Veenman, H. Jaap van den Herik, and Frank W. Takes. "Understanding Behavioral Patterns in Truck Co-driving Networks." In Studies in Computational Intelligence, 223–35. Cham: Springer International Publishing, 2018. http://dx.doi.org/10.1007/978-3-030-05414-4_18.

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Du, Junmin, Hui Lu, Weiyu Sun, Xin Zhang, Huimin Hu, and Yang Liu. "Investigation on Driving Habits of Chinese Truck Driver." In Communications in Computer and Information Science, 526–31. Cham: Springer International Publishing, 2017. http://dx.doi.org/10.1007/978-3-319-58750-9_73.

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Gan, Jiahua, Meng Zhang, and Yun Xiao. "Multidimensional Data Analysis Based on LOGIT Model." In Proceeding of 2021 International Conference on Wireless Communications, Networking and Applications, 303–15. Singapore: Springer Nature Singapore, 2022. http://dx.doi.org/10.1007/978-981-19-2456-9_32.

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AbstractLogit Model is an important method for empirical analysis of multi-source data. In order to explore the traffic safety mechanism, The Paper taked traffic behavior data as an example, researched personal characteristics of truck drivers, Analyzed the influence of the driver’s personal traits on traffic violations. Based on the binary logistics regression model, the analysis model of traffic violations was established. The results show that personality, driver’s license level, daily driving time, transportation route, vehicle ownership, and occupational disease are important factors that affect drivers’ violations. Further data analysis shows that truck drivers with bile personalities, driving for more than 12 h per day, no fixed transportation routes, and vehicles with loans have the highest probability of violations. The data analysis conclusion provides data basis for truck driver management and improving truck traffic safety.

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Fassih, Mohamed, Anne-Sophie Capelle-Laizé, Philippe Carré, and Pierre-Yves Boisbunon. "Reinforcement Learning for Truck Eco-Driving: A Serious Game as Driving Assistance System." In Advanced Concepts for Intelligent Vision Systems, 299–310. Cham: Springer Nature Switzerland, 2023. http://dx.doi.org/10.1007/978-3-031-45382-3_25.

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Conference papers on the topic "Truck driving"

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Parkes, Andrew M. "Truck Driver Training Using Simulation in England." In Driving Assessment Conference. Iowa City, Iowa: University of Iowa, 2005. http://dx.doi.org/10.17077/drivingassessment.1094.

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Richardson, Natalie Tara, Michael Sinning, Michael Fries, Sonja Stockert, and Markus Lienkamp. "Highly automated truck driving." In AutomotiveUI '15: The 7th International Conference on Automotive User Interfaces and Interactive Vehicular Applications. New York, NY, USA: ACM, 2015. http://dx.doi.org/10.1145/2809730.2809733.

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Hoskins, A., M. El-Gindy, R. Vance, N. Hiller, and C. Goodhart. "Truck Driving Simulator Effectiveness." In ASME 2002 International Mechanical Engineering Congress and Exposition. ASMEDC, 2002. http://dx.doi.org/10.1115/imece2002-32964.

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The Pennsylvania Truck Driving Simulator (PTDS) has been implemented in several different studies. The effectiveness of utilizing the simulator for training heavy truck operators was investigated through a study with the Pennsylvania Department of Transportation (PENNDOT). Thirty-four highway equipment operator trainees from PENNDOT completed a series of driving tasks in the simulator. The driving tasks were developed as five independent driving scenarios within the simulator each requiring different skills. Measures of driver performance included ratings from professional driving instructors

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Chen, Chi-Chun, Shang-Lin Tien, Yan-Ting Lin, Chung-Chen Teng, and Meng-Hua Yen. "Truck Driving Assistance System." In 2021 IEEE/ACIS 22nd International Conference on Software Engineering, Artificial Intelligence, Networking and Parallel/Distributed Computing (SNPD). IEEE, 2021. http://dx.doi.org/10.1109/snpd51163.2021.9704970.

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Allen, Talleah, and Ronald Tarr. "Driving Simulators for Commercial Truck Drivers - Humans in the Loop." In Driving Assessment Conference. Iowa City, Iowa: University of Iowa, 2005. http://dx.doi.org/10.17077/drivingassessment.1181.

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Krishnamoorthy, Bharathi, and S. Gopalakrishnan. "Truck Driver's Driving Performance Assessment." In Commercial Vehicle Engineering Congress & Exhibition. 400 Commonwealth Drive, Warrendale, PA, United States: SAE International, 2008. http://dx.doi.org/10.4271/2008-01-2700.

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Knipling, Ronald R. "Car-Truck Crashes in the National Motor Vehicle Crash Causation Survey." In Driving Assessment Conference. Iowa City, Iowa: University of Iowa, 2015. http://dx.doi.org/10.17077/drivingassessment.1588.

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Knipling, Ronald R. "Threats to Scientific Validity in Truck Driver Hours-of-Service Studies." In Driving Assessment Conference. Iowa City, Iowa: University of Iowa, 2017. http://dx.doi.org/10.17077/drivingassessment.1662.

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Maguire, Daniel J. "Value Assessment and Implementation Tradeoffs for Production-Heavy Truck Active Noise Control." In Driving Assessment Conference. Iowa City, Iowa: University of Iowa, 2001. http://dx.doi.org/10.17077/drivingassessment.1075.

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Knipling, Ronald R. "Three Large Truck Crash Categories: What They Tell Us About Crash Causation." In Driving Assessment Conference. Iowa City, Iowa: University of Iowa, 2009. http://dx.doi.org/10.17077/drivingassessment.1299.

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Reports on the topic "Truck driving"

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Iwao, Mayumi, and Motoyuki Akamatsu. Driving Attitude/Workload Consciousness of Truck Drivers. Warrendale, PA: SAE International, May 2005. http://dx.doi.org/10.4271/2005-08-0300.

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Al-Qadi, Imad, Egemen Okte, Aravind Ramakrishnan, Qingwen Zhou, and Watheq Sayeh. Truck Platooning on Flexible Pavements in Illinois. Illinois Center for Transportation, May 2021. http://dx.doi.org/10.36501/0197-9191/21-010.

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Truck platoons have many benefits over traditional truck mobility. Truck platoons have the potential to improve safety and reduce fuel consumption between 5% and 15%, based on platoon configuration. In Illinois, trucks carry more than 50% of freight tonnage and constitute 25% of the traffic on interstates. Therefore, expected fuel savings would be significant for trucks. Deployment of truck platoons within interstate highways may have a direct effect on flexible pavement performance, as the time between consecutive axle loads (i.e., resting time) is expected to decrease significantly. Moreover

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Shaver, Greg, and Miles Droege. Develop and Deploy a Safe Truck Platoon Testing Protocol for the Purdue ARPA-E Project in Indiana. Purdue University, 2021. http://dx.doi.org/10.5703/1288284317314.

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Hilly terrain poses challenges to truck platoons using fixed set speed cruise control. Driving the front truck efficiently on hilly terrain improves both trucks fuel economies and improves gap maintenance between the trucks. An experimentally-validated simulation model was used to show fuel savings for the platoon of 12.3% when the front truck uses long horizon predictive cruise control (LH-PCC), 8.7% when the front truck uses flexible set speed cruise control, and only 1.2% when the front truck uses fixed set speed cruise control. Purdue, Peloton, and Cummins have jointly configured two Peter

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Lascurain, Mary Beth, Oscar Franzese, Gary J. Capps, Adam Siekmann, Neil Thomas, Tim J. LaClair, Alan M. Barker, and Helmut E. Knee. Medium Truck Duty Cycle Data from Real-World Driving Environments: Final Report. Office of Scientific and Technical Information (OSTI), November 2012. http://dx.doi.org/10.2172/1081995.

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Franzese, Oscar, Mary Beth Lascurain, and Gary J. Capps. Medium Truck Duty Cycle Data from Real-World Driving Environments: Project Interim Report. Office of Scientific and Technical Information (OSTI), January 2011. http://dx.doi.org/10.2172/1081683.

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Walker, Jasmine, Yujie Li, Maria A. Chung Li, Sikai Chen, Samuel Labi, Jon D. Fricker, and Kumares C. Sinha. Integrating Transformative Technologies in Indiana’s Transportation Operations. Purdue University, 2024. http://dx.doi.org/10.5703/1288284317651.

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New and emerging transportation technologies, driven by automation, connectivity, and electrification, could potentially help address the transportation sector’s persistent and pervasive problems, including those associated with safety, mobility, and energy use. For this reason, the state of Indiana, uniquely positioned to serve interstate truck traffic, sought ways to identify and incorporate these new technologies on Indiana’s highways. This report addressed the challenges and opportunities regarding the integration of transformative technologies in Indiana’s truck operations, with a particu

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Meidani, Hadi, and Amir Kazemi. Data-Driven Computational Fluid Dynamics Model for Predicting Drag Forces on Truck Platoons. Illinois Center for Transportation, November 2021. http://dx.doi.org/10.36501/0197-9191/21-036.

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Fuel-consumption reduction in the truck industry is significantly beneficial to both energy economy and the environment. Although estimation of drag forces is required to quantify fuel consumption of trucks, computational fluid dynamics (CFD) to meet this need is expensive. Data-driven surrogate models are developed to mitigate this concern and are promising for capturing the dynamics of large systems such as truck platoons. In this work, we aim to develop a surrogate-based fluid dynamics model that can be used to optimize the configuration of trucks in a robust way, considering various uncert

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Liu, Tong, and Hadi Meidani. Artificial Intelligence for Optimal Truck Platooning: Impact on Autonomous Freight Delivery. Illinois Center for Transportation, August 2023. http://dx.doi.org/10.36501/0197-9191/23-017.

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The advancements in autonomous- and connected-vehicle technologies bring drastic changes in freight delivery. Vehicle-to-vehicle and vehicle-to-infrastructure communication has become a reality with the help of autonomous and connected vehicles. One of the most notable changes is the formation of truck platoons. Despite the numerous benefits of truck platooning, such as reduced fuel consumption and increased traffic efficiency, this approach requires a significant amount of computational resources to obtain aerodynamic performance under different scenarios. To overcome this challenge, a data-d

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Research Institute (IFPRI), International Food Policy. On the fast track: Driving down stunting in Vietnam. Washington, DC: International Food Policy Research Institute, 2016. http://dx.doi.org/10.2499/9780896295889_15.

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Muelaner, Jody, ed. Unsettled Issues in Commercial Vehicle Platooning. SAE International, November 2021. http://dx.doi.org/10.4271/epr2021027.

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Platooning has the potential to reduce the energy consumption of commercial vehicles while improving safety; however, both advantages are currently difficult to quantify due to insufficient data and the wide range of variables affecting models. Platooning will significantly reduce the use of energy when compared to trucks driven alone, or at a safe distance for a driver without any automated assistance. Platooning will also reduce stopping distances—multiple states in the US have passed laws authorizing truck platoons to operate at shorter gaps than are authorized for normal, human-driven truc

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