Relevant bibliographies by topics / Driver behavior

Contents

  1. Journal articles
  2. Dissertations / Theses
  3. Books
  4. Book chapters
  5. Conference papers
  6. Reports

Academic literature on the topic 'Driver behavior'

Author: Grafiati
Published: 4 June 2021
Last updated: 25 July 2025

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Journal articles on the topic "Driver behavior"

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Esad Ergin, Mahmut. "Are Drivers Ready for the Winter? A Driver Behavior Analysis." International Journal of Science and Research (IJSR) 11, no. 1 (2022): 1056–57. http://dx.doi.org/10.21275/sr22118143033.

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Tan, Yun Long, and Hong Fei Jia. "Establishment and Validation of Mainline Driver Type Model at Expressway-Ramp Merging Area." Applied Mechanics and Materials 409-410 (September 2013): 1392–97. http://dx.doi.org/10.4028/www.scientific.net/amm.409-410.1392.

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The driver characteristic is an important factor that affects driver behaviors, however, the existing driver behavior models little consider the influence of driver own characteristic differences on the driver behaviors. As the driver mental and physical behaviors in the process of driving are uncertainty and ambiguity, the mainline vehicles at expressway-ramp merging area are selected as research object, and the fuzzy clustering theory is introduced. In order to describe the mainline drivers characteristics accurately, the mainline vehicle acceleration, the relative speed of the current mainl
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Trindade, Nielson S., Artur H. Kronbauer, Helder G. Aragão, and Jorge Campos. "Driver Rating: a mobile application to evaluate driver behavior." South Florida Journal of Development 2, no. 2 (2021): 1147–60. http://dx.doi.org/10.46932/sfjdv2n2-001.

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The combination of data from sensors embedded in vehicles and smartphones promises to generate great innovations in intelligent transportation systems. This article presents Driver Rating, a mobile application to evaluate the behavior of drivers based on the data gathered from vehicles´ and smartphones´ sensors. The Driver Rating application analyzes five variables (fuel consumption, carbon dioxide emission, speed, longitudinal acceleration, and transverse acceleration) to evaluate driver´s behaviors while driving. To test the Driver Rating application and identify its potentialities, an exper
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Dai, Songyin, Yuan Zhong, Cheng Xu, Hongzhe Liu, Jiazheng Yuan, and Pengfei Wang. "An Intelligent Security Classification Model of Driver’s Driving Behavior Based on V2X in IoT Networks." Security and Communication Networks 2022 (May 11, 2022): 1–12. http://dx.doi.org/10.1155/2022/6793365.

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Traffic accidents occur frequently in Internet of Things (IoT) safety system. Traffic accidents are largely caused by drivers’ unsafe driving behaviors in the process of driving. Aiming at the problem of low safety of real-time warning in driving, this paper proposes a model to detect driver behavior. Firstly, according to the driver target detection for positioning, combined with the Pose Estimation to identify the driver in the process of driving a variety of driving behaviors, at the same time, a rating model is built to score drivers’ driving behaviors. Then, by integrating the driver beha
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Farooq, Danish, Sarbast Moslem, Rana Faisal Tufail, et al. "Analyzing the Importance of Driver Behavior Criteria Related to Road Safety for Different Driving Cultures." International Journal of Environmental Research and Public Health 17, no. 6 (2020): 1893. http://dx.doi.org/10.3390/ijerph17061893.

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Driver behavior has been considered as the most critical and uncertain criteria in the study of traffic safety issues. Driver behavior identification and categorization by using the Fuzzy Analytic Hierarchy Process (FAHP) can overcome the uncertainty of driver behavior by capturing the ambiguity of driver thinking style. The main goal of this paper is to examine the significant driver behavior criteria that influence traffic safety for different traffic cultures such as Hungary, Turkey, Pakistan and China. The study utilized the FAHP framework to compare and quantify the driver behavior criter
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Mattsson, Markus T. "Network models of driver behavior." PeerJ 6 (January 10, 2019): e6119. http://dx.doi.org/10.7717/peerj.6119.

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The way people behave in traffic is not always optimal from the road safety perspective: drivers exceed speed limits, misjudge speeds or distances, tailgate other road users or fail to perceive them. Such behaviors are commonly investigated using self-report-based latent variable models, and conceptualized as reflections of violation- and error-proneness. However, attributing dangerous behavior to stable properties of individuals may not be the optimal way of improving traffic safety, whereas investigating direct relationships between traffic behaviors offers a fruitful way forward. Network mo
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Yang, Shiyan, Jonny Kuo, and Michael G. Lenné. "Analysis of Gaze Behavior to Measure Cognitive Distraction in Real-World Driving." Proceedings of the Human Factors and Ergonomics Society Annual Meeting 62, no. 1 (2018): 1944–48. http://dx.doi.org/10.1177/1541931218621441.

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Cognitive distraction can impair drivers’ situation awareness and control performance in driving. An on-road study was conducted to examine the efficacy in the detection of driver cognitive distraction based on the driver monitoring system developed by Seeing Machines. Participants completed a 25-km test drive on the local public roads whilst engaging in a series of secondary tasks that were designed to trigger different types of cognitive distraction, such as conversation, comprehension, N-back, and route-planning tasks. The findings showed that percent road center (PRC), one of the promising
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Liu, Shida, Xuyun Wang, Honghai Ji, Li Wang, and Zhongsheng Hou. "A Novel Driver Abnormal Behavior Recognition and Analysis Strategy and Its Application in a Practical Vehicle." Symmetry 14, no. 10 (2022): 1956. http://dx.doi.org/10.3390/sym14101956.

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In this work, a novel driver abnormal behavior analysis system based on practical facial landmark detection (PFLD) and you only look once version 5 (YOLOv5) were developed to solve the recognition and analysis of driver abnormal behaviors. First, a library for analyzing the abnormal behavior of vehicle drivers was designed, in which the factors that cause an abnormal behavior of drivers were divided into three categories according to the behavioral characteristics including natural behavioral factors, unnatural behavioral factors, and passive behavioral factors. Then, different neural network
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A. KATTAN, RAAD. "ILLEGAL DRIVER BEHAVIOR AT SIGNALIZED INTERSECTIONS." Journal of The University of Duhok 22, no. 2 (2020): 11–22. http://dx.doi.org/10.26682/sjuod.2019.22.2.2.

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Davoli, Luca, Marco Martalò, Antonio Cilfone, et al. "On Driver Behavior Recognition for Increased Safety: A Roadmap." Safety 6, no. 4 (2020): 55. http://dx.doi.org/10.3390/safety6040055.

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Advanced Driver-Assistance Systems (ADASs) are used for increasing safety in the automotive domain, yet current ADASs notably operate without taking into account drivers’ states, e.g., whether she/he is emotionally apt to drive. In this paper, we first review the state-of-the-art of emotional and cognitive analysis for ADAS: we consider psychological models, the sensors needed for capturing physiological signals, and the typical algorithms used for human emotion classification. Our investigation highlights a lack of advanced Driver Monitoring Systems (DMSs) for ADASs, which could increase driv
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Dissertations / Theses on the topic "Driver behavior"

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Ogle, Jennifer Harper. "Quantitative assessment of driver speeding behavior using instrumented vehicles." Diss., Georgia Institute of Technology, 2005. http://etd.gatech.edu/theses/available/etd-04182005-034536/unrestricted/ogle%5Fjennifer%5Fh%5F200505%5Fphd.pdf.

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Thesis (Ph. D.)--Civil and Environmental Engineering, Georgia Institute of Technology, 2005.<br>Includes bibliographical references (p. 310-316). Also available online via the Georgia Institute of Technology, website (http://etd.gatech.edu/).
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Graves, Mark. "Avoidance Behavior in the Elderly Driver." TopSCHOLAR®, 1996. http://digitalcommons.wku.edu/theses/873.

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Do older drivers modify their driving habits in response to functional impairment? Older drivers who avoid challenging driving situations were compared with non-avoiders, to determine whether functional limitations were related to avoidance and whether avoidance is related to reducing crash risk. Results showed that, on the average, older drivers reported avoiding driving at night, on high traffic roads, on high speed roads, and in rush hour traffic while not avoiding left turns, driving in the rain, and driving alone. Subjects were placed into groups based on their cognitive and visual abilit
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Miyajima, Chiyomi, Yoshihiro Nishiwaki, Koji Ozawa, et al. "Driver Modeling Based on Driving Behavior and Its Evaluation in Driver Identification." IEEE, 2007. http://hdl.handle.net/2237/9623.

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Hamdar, Samer Hani. "Towards modeling driver behavior under extreme conditions." College Park, Md. : University of Maryland, 2004. http://hdl.handle.net/1903/2141.

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Thesis (M.S.)--University of Maryland, College Park, 2004.<br>Includes vita. Includes bibliographical references (p. 118-123). Also available online via the Digital Repository at the University of Maryland (https://drum.umd.edu/dspace/).
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Garcia, Ortiz Michael [Verfasser]. "Prediction of driver behavior / Michael Garcia Ortiz." Bielefeld : Universitätsbibliothek Bielefeld, 2014. http://d-nb.info/1049523555/34.

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Parwathaneni, Rajiv. "Effect of Roadside Vegetation on Driver Behavior." Cleveland State University / OhioLINK, 2016. http://rave.ohiolink.edu/etdc/view?acc_num=csu1481555419869409.

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Amer, Ahmed. "Statistical and Behavioral Modeling of Driver Behavior on Signalized Intersection Approaches." Diss., Virginia Tech, 2010. http://hdl.handle.net/10919/77995.

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The onset of a yellow indication is typically associated with the risk of vehicle crashes resulting from dilemma-zone and red-light-running problems. Such risk of vehicle crashes is greater for high-speed signalized intersection approaches. The research presented in this dissertation develops statistical as well as behavioral frameworks for modeling driver behavior while approaching high-speed signalized intersection approaches at the onset of a yellow indication. The analysis in this dissertation utilizes two sources of data. The main source is a new dataset that was collected as part of this
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Xu, Feng. "Driver behavior and gap acceptance studies at roundabouts." abstract and full text PDF (free order & download UNR users only), 2007. http://0-gateway.proquest.com.innopac.library.unr.edu/openurl?url_ver=Z39.88-2004&rft_val_fmt=info:ofi/fmt:kev:mtx:dissertation&res_dat=xri:pqdiss&rft_dat=xri:pqdiss:1442865.

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Vogel, Katja. "Modeling driver behavior : a control theory based approach /." Linköping : Univ, 2002. http://www.bibl.liu.se/liupubl/disp/disp2002/tek751s.pdf.

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Lwambagaza, Lina. "Modeling Older Driver Behavior on Freeway Merging Ramps." UNF Digital Commons, 2016. http://digitalcommons.unf.edu/etd/646.

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Merging from on-ramps to mainline traffic is one of the most challenging driving maneuvers on freeways. The challenges are further heightened for older drivers, as they are known to have longer perception-reaction times, larger acceptance gaps, and slower acceleration rates. In this research, VISSIM, a microscopic traffic simulation software, was used to evaluate the influence of the aging drivers on the operations of a typical diamond interchange. First, drivers were recorded on video cameras as they negotiated joining the mainline traffic from an on-ramp acceleration lane at two sites along
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Books on the topic "Driver behavior"

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Takeda, Kazuya, Hakan Erdogan, John H. L. Hansen, and Huseyin Abut, eds. In-Vehicle Corpus and Signal Processing for Driver Behavior. Springer US, 2009. http://dx.doi.org/10.1007/978-0-387-79582-9.

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Haroun, Antoine. Observed minimum headways as an index of driver behavior. National Library of Canada = Bibliothèque nationale du Canada, 1999.

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C, Cacciabue Pietro, ed. Modelling driver behaviour in automotive environments: Critical issues in driver interactions with intelligent transport systems. Springer, 2007.

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VanWechel, Tamara. Traffic safety issues in North Dakota: Phase II: Driver knowledge, attitude, behavior and beliefs : focus group: young male drivers. Mountain-Plains Consortium, 2008.

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United States. National Highway Traffic Safety Administration, ed. NATIONAL SURVEY OF SPEEDING AND OTHER UNSAFE DRIVING ACTIONS... VOLUME II:... DRIVER ATTITUDES & BEHAVIOR. s.n., 1999.

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Hallmark, Shauna, Dan McGehee, Karin M. Bauer, et al. Initial Analyses from the SHRP 2 Naturalistic Driving Study: Addressing Driver Performance and Behavior in Traffic Safety. Transportation Research Board, 2013. http://dx.doi.org/10.17226/22621.

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Katz, B., S. Park, J. Du, et al. Feasibility of Using In-Vehicle Video Data to Explore How to Modify Driver Behavior That Causes Nonrecurring Congestion. National Academies Press, 2011. http://dx.doi.org/10.17226/14509.

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National Research Council (U.S.). Transportation Research Board and Second Strategic Highway Research Program (U.S.), eds. Feasibility of using in-vehicle video data to explore how to modify driver behavior that causes nonrecurring congestion. Transportation Research Board, 2011.

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International Conference on Driver Behaviour and Training (1st 2003 Stratford-upon-Avon, England). Driver behaviour and training. Ashgate, 2003.

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International Conference on Driver Behaviour and Training (3rd 2007 Dublin, Ireland). Driver behaviour and training. Ashgate, 2008.

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Book chapters on the topic "Driver behavior"

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Sato, Toshihisa, and Motoyuki Akamatsu. "Driver Behavior driver behavior at Intersections driver behavior at intersections." In Encyclopedia of Sustainability Science and Technology. Springer New York, 2012. http://dx.doi.org/10.1007/978-1-4419-0851-3_786.

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Sato, Toshihisa, and Motoyuki Akamatsu. "Driver Behavior driver behavior at Intersections driver behavior at intersections." In Transportation Technologies for Sustainability. Springer New York, 2013. http://dx.doi.org/10.1007/978-1-4614-5844-9_786.

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Wang, Jianqiang, Lei Zhang, Xiaojia Lu, and Keqiang Li. "Driver driver Characteristics driver characteristics Based on Driver driver Behavior driver behavior." In Encyclopedia of Sustainability Science and Technology. Springer New York, 2012. http://dx.doi.org/10.1007/978-1-4419-0851-3_785.

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Wang, Jianqiang, Lei Zhang, Xiaojia Lu, and Keqiang Li. "Driver driver Characteristics driver characteristics Based on Driver driver Behavior driver behavior." In Transportation Technologies for Sustainability. Springer New York, 2013. http://dx.doi.org/10.1007/978-1-4614-5844-9_785.

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Donges, Edmund. "Driver Behavior Models." In Handbook of Driver Assistance Systems. Springer International Publishing, 2015. http://dx.doi.org/10.1007/978-3-319-12352-3_2.

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Donges, Edmund. "Driver Behavior Models." In Handbook of Driver Assistance Systems. Springer International Publishing, 2015. http://dx.doi.org/10.1007/978-3-319-09840-1_2-1.

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Hamdar, Samer. "Driver Behavior Modeling." In Handbook of Intelligent Vehicles. Springer London, 2012. http://dx.doi.org/10.1007/978-0-85729-085-4_20.

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Sagiroglu, Seref. "Driver Behavior Analytics." In Encyclopedia of Big Data. Springer International Publishing, 2022. http://dx.doi.org/10.1007/978-3-319-32010-6_524.

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SAGIROGLU, Seref. "Driver Behavior Analytics." In Encyclopedia of Big Data. Springer International Publishing, 2018. http://dx.doi.org/10.1007/978-3-319-32001-4_524-1.

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Witt, Manuela, Lei Wang, Felix Fahrenkrog, Klaus Kompaß, and Günther Prokop. "Cognitive Driver Behavior Modeling: Influence of Personality and Driver Characteristics on Driver Behavior." In Advances in Intelligent Systems and Computing. Springer International Publishing, 2018. http://dx.doi.org/10.1007/978-3-319-93885-1_69.

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Conference papers on the topic "Driver behavior"

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Jawad, Abdul, and Jim Whitehead. "Accident Scenario Generation using Driver Behavior Model." In 2024 IEEE 27th International Conference on Intelligent Transportation Systems (ITSC). IEEE, 2024. https://doi.org/10.1109/itsc58415.2024.10920274.

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Ali, Fatma Mohammed Al, Mouza Jamal Alnuaimi, Sara Ali Alawadhi, and Saeed Abdallah. "Abnormal Driver Behavior Detection Using Deep Learning." In 2024 7th International Conference on Signal Processing and Information Security (ICSPIS). IEEE, 2024. https://doi.org/10.1109/icspis63676.2024.10812606.

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Kumar, S. Senthil, KR N. Vignesh, and V. Bhuvanesh. "Enhancing Road Safety: Analyzing Driver Behavior Through AI-Driven Monitoring Techniques." In 2024 9th International Conference on Communication and Electronics Systems (ICCES). IEEE, 2024. https://doi.org/10.1109/icces63552.2024.10859672.

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Ozkan, Mehmet Fatih, and Yao Ma. "Inverse Reinforcement Learning Based Driver Behavior Analysis and Fuel Economy Assessment." In ASME 2020 Dynamic Systems and Control Conference. American Society of Mechanical Engineers, 2020. http://dx.doi.org/10.1115/dscc2020-3122.

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Abstract Human drivers have different driver behaviors when operating vehicles. These driving behaviors, including the driver’s preferred speed and rate of acceleration, impose a major impact on vehicle fuel consumption consequently. In this study, we proposed a feature-based driver behavior learning model from demonstrated driving data utilizing the Inverse Reinforcement Learning (IRL) approach to analyze various driver behaviors and their impacts on vehicle fuel consumption. The proposed approach models the individual driving style as cost function which is a linear combination of the featur
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Xiaokai He, Jiajun Hu, Jialiang Lu, Min-You Wu, and Benoit Guerin. "Driver lane changing behavior." In 2011 International Conference on Computer Science and Network Technology (ICCSNT). IEEE, 2011. http://dx.doi.org/10.1109/iccsnt.2011.6182452.

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Diederichsa, Frederik, and Gloria Pöhlerb. "Driving Maneuver Prediction Based on Driver Behavior Observation." In Applied Human Factors and Ergonomics Conference. AHFE International, 2021. http://dx.doi.org/10.54941/ahfe100705.

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With respect to an increasing amount of driver assistance systems and automated driving functions, a higher chance of unappreciated action and intervention of these systems can be registered, which in turn lowers the acceptance by drivers and passengers. A reduction of unnecessary warnings and interventions can be achieved by making them adaptive to driver’s intentions and maneuvers planning. In order to learn which driver behavior indicates certain maneuver intentions, a rater-based method using video recordings is proposed in this paper. Three driving maneuvers, namely turning, changing lane
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Wang, Peng, Zhishuai Yin, Linzhen Nie, and Xukai Zhai. "A Sparse Spatiotemporal Transformer for Detecting Driver Distracted Behaviors." In WCX SAE World Congress Experience. SAE International, 2023. http://dx.doi.org/10.4271/2023-01-0835.

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&lt;div class="section abstract"&gt;&lt;div class="htmlview paragraph"&gt;At present, the development of autonomous driving technology is still immature, and there is still a long way until fully driverless vehicles. Therefore, the state of the driver is still an important factor affecting traffic safety, and it is of great significance to detect the driver’s distracted behavior. In the task of driver distracted behavior detection, some characteristics of driver behavior in the cockpit can be further utilized to improve the detection performance. Compared with general human behaviors, driving
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Waters, Anthony, and Vincent Paglioni. "Advanced Driver Assistance Systems and Emotion-based Driver Behavior." In 16th International Conference on Applied Human Factors and Ergonomics (AHFE 2025). AHFE International, 2025. https://doi.org/10.54941/ahfe1006514.

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Automobiles have evolved over the decades to include an increasing number of automated systems that assist the driver or even take over control of the vehicle. As a result, the human demands from vehicles have changed so that cognitive power can be used to participate in other activities while driving. These advanced drivers assistance systems (ADAS) include features such as lane keeping assistance, adaptive cruise control, blind spot monitoring and emergency crash avoidance. Each of these systems works to improve vehicle safety, principally by decreasing the required human intervention in som
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Shi, Lei, Youlin Zhang, Jicun Xu, Xuelian Zheng, Xiao Tong Cui, and Qingju Wang. "Driving Behavior Analysis Based on Weighted Cost Function." In 14th International Conference on Applied Human Factors and Ergonomics (AHFE 2023). AHFE International, 2023. http://dx.doi.org/10.54941/ahfe1003423.

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Drivers are the main participants of road traffic, and analyzing their driving behavior characteristics is beneficial to road safety and personalized vehicle insurance formulation. Most existing studies describe driving behavior characteristics by constructing various driving behavior features of driving data. However, drivers' pursuit of different property is the main reason to form different driving behavior characteristics. Therefore, this paper proposes a novel framework, which uses the weight of cost function to represent driving behavior characteristics. Firstly, for every driver’s basic
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Wang, Jinzhen, Yiming Cheng, and Liangyao Yu. "Racing Driver Modeling Based on Driving Behavior." In ASME 2021 International Design Engineering Technical Conferences and Computers and Information in Engineering Conference. American Society of Mechanical Engineers, 2021. http://dx.doi.org/10.1115/detc2021-71113.

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Abstract The driver model is an important link in the research of shared autonomy control. In order to simulate the driver’s handling characteristics in the complex human-vehicle-road closed-loop system, the driver model is required to accomplish the driving operation under specific working conditions. In this paper, a lateral-longitudinal combined racing driver model is designed. The lateral control model adopts the preview model with far and near viewpoints and the dynamic velocity controller is added into the longitudinal control model to obtain the expected speed of the target trajectory.
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Reports on the topic "Driver behavior"

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Ghanipoor Machiani, Sahar, Aryan Sohrabi, and Arash Jahangiri. Impact of Regular and Narrow AV-Exclusive Lanes on Manual Driver Behavior. Mineta Transportation Institute, 2020. http://dx.doi.org/10.31979/mti.2020.1922.

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This study attempts to answer the question of how a narrow (9-ft) lane dedicated to Automated Vehicles (AVs) would affect the behavior of drivers in the adjacent lane to the right. To this end, a custom driving simulator environment was designed mimicking the Interstate 15 smart corridor in San Diego. A group of participants was assigned to drive next to the simulated 9-ft narrow lane while a control group was assigned to drive next to a regular 12-ft AV lane. Driver behavior was analyzed by measuring the mean lane position, mean speed, and mental effort (self-reported/subjective measure). In
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Soma, Hitoshi, Horotake Matsue, Takayuki Watanabe, Yasuhiko Takae, and Nariaki Etori. Drivers' Trust in Low-Speed ACC Systems (Higher Trust and Driver Behavior). SAE International, 2005. http://dx.doi.org/10.4271/2005-08-0474.

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Goddard, Tara, Kimberly Kahn, and Arlie Adkins. Racial Bias in Driver Yielding Behavior at Crosswalks. Portland State University Library, 2014. http://dx.doi.org/10.15760/trec.130.

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Wakita, Toshihiro, Koji Ozawa, Chiyomi Miyajima, et al. Study on Driver Identification Method Using Driving Behavior Signals. SAE International, 2005. http://dx.doi.org/10.4271/2005-08-0569.

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Pulugurtha, Srinivas S., and Raghuveer Gouribhatla. Drivers’ Response to Scenarios when Driving Connected and Automated Vehicles Compared to Vehicles with and without Driver Assist Technology. Mineta Transportation Institute, 2022. http://dx.doi.org/10.31979/mti.2022.1944.

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Traffic related crashes cause more than 38,000 fatalities every year in the United States. They are the leading cause of death among drivers up to 54 years in age and incur $871 million in losses each year. Driver errors contribute to about 94% of these crashes. In response, automotive companies have been developing vehicles with advanced driver assistance systems (ADAS) that aid in various driving tasks. These features are aimed at enhancing safety by either warning drivers of a potential hazard or picking up certain driving maneuvers like maintaining the lane. These features are already part
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Kulhandjian, Hovannes. Detecting Driver Drowsiness with Multi-Sensor Data Fusion Combined with Machine Learning. Mineta Transportation Institute, 2021. http://dx.doi.org/10.31979/mti.2021.2015.

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In this research work, we develop a drowsy driver detection system through the application of visual and radar sensors combined with machine learning. The system concept was derived from the desire to achieve a high level of driver safety through the prevention of potentially fatal accidents involving drowsy drivers. According to the National Highway Traffic Safety Administration, drowsy driving resulted in 50,000 injuries across 91,000 police-reported accidents, and a death toll of nearly 800 in 2017. The objective of this research work is to provide a working prototype of Advanced Driver Ass
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Tavakoli, Arash, Vahid Balali, and Arsalan Heydarian. How do Environmental Factors Affect Drivers’ Gaze and Head Movements? Mineta Transportation Institute, 2021. http://dx.doi.org/10.31979/mti.2021.2044.

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Studies have shown that environmental factors affect driving behaviors. For instance, weather conditions and the presence of a passenger have been shown to significantly affect the speed of the driver. As one of the important measures of driving behavior is the gaze and head movements of the driver, such metrics can be potentially used towards understanding the effects of environmental factors on the driver’s behavior in real-time. In this study, using a naturalistic study platform, videos have been collected from six participants for more than four weeks of a fully naturalistic driving scenar
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Jacobsen, Mark. Fuel Economy and Safety: The Influences of Vehicle Class and Driver Behavior. National Bureau of Economic Research, 2012. http://dx.doi.org/10.3386/w18012.

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Siebke, Christian, Maximilian Bäumler, Madlen Ringhand, Marcus Mai, Mohamed Nadar Ramadan, and Günther Prokop. Report on layout of the traffic simulation and trial design of the evaluation. Technische Universität Dresden, 2021. http://dx.doi.org/10.26128/2021.244.

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Within the AutoDrive project, openPASS is used to develop a cognitive stochastic traffic flow simulation for urban intersections and highway scenarios, which are described in deliverable D1.14. The deliverable D2.16 includes the customizations of the framework openPASS that are required to provide a basis for the development and implementation of the driver behavior model and the evaluated safety function. The trial design for the evaluation of the safety functions is described. Furthermore, the design of the driver behavior study is introduced to parameterize and validate the underlying drive
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Francfort, Jim. Characterize Plug-In Electric Vehicle Driver Away-From-Home Parking Behavior in The EV Project. Office of Scientific and Technical Information (OSTI), 2015. http://dx.doi.org/10.2172/1483605.

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