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: 1 April 2023

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Journal articles on the topic "Driver behavior"

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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 (May 17, 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 experiment was carried out on an urban environment, showing promising results regarding the classification of drivers’ behavior.
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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 mainline vehicle to the all mainline vehicles and the lag gap of the mainline vehicle are selected to cluster by the fuzzy clustering method, and the driver type distribution model is built by K-S test method. Then, the driver type distribution data as a key parameter is incorporated into the expressway merging model, in order to represent the effect of driver characteristic on drive behavior. Finally, the microscopic traffic simulation system MTSS is taken as the simulation plat to build simulation model and validate the built mainline driver type model, the output results from the simulation system are compared with the field data, the satisfactory results indicate that the built driver type model can be used to describe the impact of driver type on driving behavior.
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A. KATTAN, RAAD. "ILLEGAL DRIVER BEHAVIOR AT SIGNALIZED INTERSECTIONS." Journal of The University of Duhok 22, no. 2 (March 9, 2020): 11–22. http://dx.doi.org/10.26682/sjuod.2019.22.2.2.

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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 models of driver behavior and background factors influencing behavior were constructed using a large UK sample of novice drivers. The models show how individual violations, such as speeding, are related to and may contribute to individual errors such as tailgating and braking to avoid an accident. In addition, a network model of the background factors and driver behaviors was constructed. Finally, a model predicting crashes based on prior behavior was built and tested in separate datasets. This contribution helps to bridge a gap between experimental/theoretical studies and self-report-based studies in traffic research: the former have recognized the importance of focusing on relationships between individual driver behaviors, while network analysis offers a way to do so for self-report studies.
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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 behavior model and evaluation rules, the system can give timely and active warning when the driver makes unsafe behavior in the process of driving. Finally, in the V2X scenario, feedback and presentation are given to users in the form of points. The experimental results show that, in the scenario of Internet of vehicles, the driving behavior rating model can well analyze and evaluate drivers’ driving behaviors, so that drivers can more accurately understand their abnormal driving behaviors and driving scores, which plays a significant role in IoT safety management.
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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 (September 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 gaze metrics, increased significantly with cognitive distraction when compared to baseline, but failed to distinguish between different forms of cognitive distraction Moreover, PRC’s sensitivity to cognitive distraction was found to be affected by the chosen radius of road center area. These findings of driver cognitive distraction measurement provide data-driven suggestions for the development of real-time driver monitoring systems in the wild.
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Farooq, Danish, Sarbast Moslem, Rana Faisal Tufail, Omid Ghorbanzadeh, Szabolcs Duleba, Ahsen Maqsoom, and Thomas Blaschke. "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 (March 14, 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 criteria designed on a three-level hierarchical structure. The FAHP procedure computed the weight factors and ranked the significant driver behavior criteria based on pairwise comparisons (PCs) of driver’s responses on the Driver Behavior Questionnaire (DBQ). The study results observed “violations” as the most significant driver behavior criteria for level 1 by all nominated regions except Hungary. While for level 2, “aggressive violations” is observed as the most significant driver behavior criteria by all regions except Turkey. Moreover, for level 3, Hungary and Turkey drivers evaluated the “drive with alcohol use” as the most significant driver behavior criteria. While Pakistan and China drivers evaluated the “fail to yield pedestrian” as the most significant driver behavior criteria. Finally, Kendall’s agreement test was performed to measure the agreement degree between observed groups for each level in a hierarchical structure. The methodology applied can be easily transferable to other study areas and our results in this study can be helpful for the drivers of each region to focus on highlighted significant driver behavior criteria to reduce fatal and seriously injured traffic accidents.
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Davoli, Luca, Marco Martalò, Antonio Cilfone, Laura Belli, Gianluigi Ferrari, Roberta Presta, Roberto Montanari, et al. "On Driver Behavior Recognition for Increased Safety: A Roadmap." Safety 6, no. 4 (December 12, 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 driving quality and security for both drivers and passengers. We then provide our view on a novel perception architecture for driver monitoring, built around the concept of Driver Complex State (DCS). DCS relies on multiple non-obtrusive sensors and Artificial Intelligence (AI) for uncovering the driver state and uses it to implement innovative Human–Machine Interface (HMI) functionalities. This concept will be implemented and validated in the recently EU-funded NextPerception project, which is briefly introduced.
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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 (September 20, 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 models were established through the representation of the actual scene of the three behaviors. Specifically, the abnormal driver behavior caused by natural behavioral factors was identified by a PFLD neural network model based on facial key point detection, and the abnormal driver behavior caused by unnatural behavioral factors and passive behavioral factors were identified by a YOLOv5 neural network model based on target detection. In addition, in a test of the driver abnormal behavior analysis system in an actual vehicle, the precision rate was greater than 95%, which meets the requirements of practical application.
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James, Rachel M., and Britton E. Hammit. "Identifying Contributory Factors to Heterogeneity in Driving Behavior: Clustering and Classification Approach." Transportation Research Record: Journal of the Transportation Research Board 2673, no. 10 (May 18, 2019): 343–53. http://dx.doi.org/10.1177/0361198119849404.

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Previous research efforts using aerially collected trajectory-level data have confirmed the existence of inter-driver heterogeneity, where different car-following model (CFM) specifications and calibrated parameter sets are required to adequately capture drivers’ driving behavior. This research hypothesizes that there also exist clusters of drivers whose behavior is sufficiently similar to be considered a homogeneous group. To test this hypothesis, this study applies a 664-trip sample of trajectory-level data from the SHRP2 Naturalistic Driving Study to calibrate the Gipps, Intelligent Driver Model, and Wiedemann 99 CFMs. Using the calibrated parameter coefficients, this research provides evidence of the existence of homogeneous groups of driving behavior using the expectation maximization clustering algorithm. Four classification algorithms are then applied to classify the trip’s cluster ID according to driver demographics. Driver age, income, and marital status were most commonly identified as important classification attributes, while gender, work status, and living status appear less significant. The classification algorithms, which sought to classify a trip’s behavioral cluster ID by the driver-specific attributes, achieved the highest accuracy rate when predicting the desired velocity car-following parameter clusters. This effort illustrates that some drivers drive sufficiently alike to form a cluster of similar behavior; moreover, it was confirmed that driver-specific attributes can be utilized to classify drivers into these homogeneous driver groups.
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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.
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 abilities. It was found that older drivers with an impaired UFOV and either 0, 1-2, or 3-4 vision problems reported avoiding significantly more than those with unimpaired cognition and vision. The number of at-fault crashes incurred in the 5 years prior to 1990 was positively related to driving avoidance (those who reported avoidance had a history of more crashes than those who did not report avoidance). However, the number of crashes incurred in the 3 years subsequent to 1990 was negatively related to avoidance (those who reported avoidance in 1990 had fewer crashes in future years than those who did not report avoidance). These results imply that older drivers modify their driving in response to crash involvement and/or functional limitations and that this "self-regulation" may reduce future crash risk.
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Miyajima, Chiyomi, Yoshihiro Nishiwaki, Koji Ozawa, Toshihiro Wakita, Katsunobu Itou, Kazuya Takeda, and Fumitada Itakura. "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.
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 research effort during the summer of 2008. This experiment includes two instructed speeds; 72.4 km/h (45 mph) with 1727 approaching trials (687 running and 1040 stopping), and 88.5 km/h (55 mph) with 1727 approaching trials (625 running and 1102 stopping). The complementary source is an existing dataset that was collected earlier in the spring of 2005 on the Virginia Smart Road facility. This dataset includes a total of 1186 yellow approaching trials (441 running and 745 stopping). The adopted analysis approach comprises four major parts that fulfill the objectives of this dissertation. The first part is concerned with the characterization of different driver behavioral attributes, including driver yellow/red light running behavior, driver stop-run decisions, driver perception-reaction times (PRT), and driver deceleration levels. The characterization of these attributes involves analysis of variance (ANOVA) and frequency distribution analyses, as well as the calibration of statistical models. The second part of the dissertation introduces a novel approach for computing the clearance interval duration that explicitly accounts for the reliability of the design (probability that drivers do not encounter a dilemma zone). Lookup tables are developed to assist practitioners in the design of yellow timings that reflects the stochastic nature of driver PRT and deceleration levels. An extension of the proposed approach is presented that can be integrated with the IntelliDriveSM initiative. Furthermore, the third part of the dissertation develops an agent-based Bayesian statistics approach to capture the stochastic nature of the driver stop-run decision. The Bayesian model parameters are calibrated using the Markov Chain Monte Carlo (MCMC) slice procedure implemented within the MATLAB® software. In addition, two procedures for the Bayesian model application are illustrated; namely Cascaded regression and Cholesky decomposition. Both procedures are demonstrated to produce replications that are consistent with the Bayesian model realizations, and capture the parameter correlations without the need to store the set of parameter realizations. The proposed Bayesian approach is ideal for modeling multi-agent systems in which each agent has its own unique set of parameters. Finally, the fourth part of the dissertation introduces and validates a state-of-the-art behavioral modeling framework that can be used as a tool to simulate driver behavior after the onset of a yellow indication until he/she reaches the intersection stop line. The behavioral model is able to track dilemma zone drivers and update the information available to them every time step until they reach a final decision. It is anticipated that this behavioral model will be implemented in microscopic traffic simulation software to enhance the modeling of driver behavior as they approach signalized intersections.
Ph. D.
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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 I-75 in Southwest Florida. Several measures of effectiveness were collected including speeds, gaps, and location of entry to the mainline lanes. This information was used as either model input or for verification purposes. Two VISSIM models were developed for each site – one for the existing conditions and verification, and another for a sensitivity analysis, varying the percentage of older drivers and Level of Service (from A to E), to determine their influence on ramp operational characteristics. According to the results, there was a significant difference in driving behavior between older, middle-aged, and younger drivers, based on the measures of effectiveness analyzed in this study. Additionally, as the level of service and percentage of older adult motorists increased, longer queues were observed with slower speeds on the acceleration lanes and the right-most travel lane of the mainline traffic.
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Books on the topic "Driver behavior"

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Modeling driver characteristics: Driver behavior in traffic. Washington, D.C.]: U.S. Dept. of Transportation, Federal Highway Administration, 2010.

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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. Boston, MA: 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. Ottawa: 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. London: 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. [Fargo, N.D.]: 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.l: s.n., 1999.

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Design, Data Collection, and Driver Behavior Simulation for the Open- Mode Integrated Transportation System (OMITS). [New York, N.Y.?]: [publisher not identified], 2016.

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Hallmark, Shauna, Dan McGehee, Karin M. Bauer, Jessica M. Hutton, Gary A. Davis, John Hourdos, Indrajit Chatterjee, et al. Initial Analyses from the SHRP 2 Naturalistic Driving Study: Addressing Driver Performance and Behavior in Traffic Safety. Washington, D.C.: Transportation Research Board, 2013. http://dx.doi.org/10.17226/22621.

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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. Washington, D.C: Transportation Research Board, 2011.

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Katz, B., S. Park, J. Du, H. Rakha, G. Golembiewski, F. Guo, Z. Doerzaph, D. Viita, N. Kehoe, and H. Rigdon. Feasibility of Using In-Vehicle Video Data to Explore How to Modify Driver Behavior That Causes Nonrecurring Congestion. Washington, D.C.: National Academies Press, 2011. http://dx.doi.org/10.17226/14509.

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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, 3082–98. New York, NY: 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, 368–84. New York, NY: 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, 3099–108. New York, NY: 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, 385–94. New York, NY: 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, 19–33. Cham: 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, 1–12. Cham: 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, 537–58. London: 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, 407–12. Cham: 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, 1–6. Cham: 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, 751–63. Cham: 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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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 features and their corresponding weights. The proposed IRL framework is used to find the model parameters that fit the observed driving style best. By using the learned driving behavior model, the most likely trajectories are computed and the optimized feature weights are used to analyze different driver behaviors. The different driver behaviors and their impacts on vehicle fuel consumption are then analyzed in real-world driving scenarios. Results show that the proposed IRL framework can successfully learn individual driver behaviors using vehicle trajectory data demonstrated by different real drivers. The learned driver behaviors promise a significant correlation between driving behavior and fuel consumption.
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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 and braking for a pedestrian who intends to cross the road, were chosen for analyzing their predictability due to behavior observation. As a first step, a driving simulator study was conducted in order to collect behavior data of 24 drivers. Subsequently, clearly distinguishable behavior classes for each maneuver were extracted from video data, resulting in five superior behavior categories with 29 behavioral classes. Based on these classes four human observers were trained to detect at the earliest convenience maneuver intentions. Overall in 97 % of all cases the observers could predict the maneuvers. Inter-rater reliabilities showed to be between κ= 0.30 and κ = 1.00.
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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. Finally, the racing driver model proposed in this paper is validated through simulation on track conditions of FSAE. In the given conditions, the result shows the racing driver model outperforms the typical driver model in lateral path tracking and the speed of racing driver model is higher than typical model on straight and corners. Meanwhile, the representation of driving skills is a key step to enhance the adaptive control of vehicles in the future. The control parameters can be adjusted according to the driver’s skill information to make the vehicle control system adapt to the driver’s skill level. This paper introduces the method of driving skill recognition based on wavelet transform and Lipschitz singularity detection theory and the preliminary test results prove the feasibility of using this method to characterize the driver’s operating skill level.
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Liu, Yanfei, and Zhaohui Wu. "Multitasking Driver Cognitive Behavior Modeling." In 2006 3rd International IEEE Conference Intelligent Systems. IEEE, 2006. http://dx.doi.org/10.1109/is.2006.348393.

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Castignani, German, Raphael Frank, and Thomas Engel. "Driver behavior profiling using smartphones." In 2013 16th International IEEE Conference on Intelligent Transportation Systems - (ITSC 2013). IEEE, 2013. http://dx.doi.org/10.1109/itsc.2013.6728289.

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Piotr, Blaszczyk, Wojciech Turek, Aleksander Byrski, and Krzysztof Cetnarowicz. "Towards Credible Driver Behavior Modeling." In 2015 IEEE 18th International Conference on Intelligent Transportation Systems - (ITSC 2015). IEEE, 2015. http://dx.doi.org/10.1109/itsc.2015.254.

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Bar-Gera, Hillel, Edna Schechtman, Tal Ze'evi, and Oren Musicant. "Yellow Signal Driver Crossing Behavior." In 2015 IEEE 18th International Conference on Intelligent Transportation Systems - (ITSC 2015). IEEE, 2015. http://dx.doi.org/10.1109/itsc.2015.440.

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Chen, Hua, Fengkai Zhao, Kai Huang, and Yantao Tian. "Driver Behavior Analysis for Advanced Driver Assistance System." In 2018 IEEE 7th Data Driven Control and Learning Systems Conference (DDCLS). IEEE, 2018. http://dx.doi.org/10.1109/ddcls.2018.8516059.

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Manchala, Vamsi K., Alvaro V. Clara, Susheelkumar C. Subramanian, Sangram Redkar, and Thomas Sugar. "Human Computer Interface Using Electroencephalography for Driver Behavior Classification." In ASME 2019 International Design Engineering Technical Conferences and Computers and Information in Engineering Conference. American Society of Mechanical Engineers, 2019. http://dx.doi.org/10.1115/detc2019-97540.

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Abstract It is important to know and be able to classify the drivers’ behavior as good, bad, keen or aggressive, which would aid in driver assist systems to avoid vehicle crashes. This research attempts to develop, test, and compare the performance of machine learning methods for classifying human driving behavior. It also proposes to correlate driver affective states with the driving behavior. The major contributions of this work are to classify the driver behavior using Electroencephalograph (EEG) while driving simulated vehicle and compare them with the behavior classified using vehicle parameters and affective states. The study involved both classical machine learning techniques such as k-Nearest Neighbor (KNN), Support Vector Machine (SVM), Artificial Neural Network (ANN) and latest “unsupervised” Hybrid Deep Learning techniques, and compared the accuracy of classification across subjects, various driving scenarios and affective states.
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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, October 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 addition to AV lane width, the experimental design took into consideration AV headway, gender, and right lane traffic to investigate possible interaction effects. The results showed no significant differences in the speed and mental effort of drivers while indicating significant differences in lane positioning. Although the overall effect of AV lane width was not significant, there were some significant interaction effects between lane width and other factors (i.e., driver gender and presence of traffic on the next regular lane to the right). Across all the significant interactions, there was no case in which those factors stayed constant while AV lane width changed between the groups, indicating that the significant difference stemmed from the other factors rather than the lane width. However, the trend observed was that drivers driving next to the 12-ft lane had better lane centering compared to the 9ft lane. The analysis also showed that while in general female drivers tended to drive further away from the 9-ft lane and performed worse in terms of lane centering, they performed better than male drivers when right-lane traffic was present. This study contributes to understanding the behavioral impacts of infrastructure adaptation to AVs on non-AV drivers.
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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). Warrendale, PA: SAE International, September 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, April 2014. http://dx.doi.org/10.15760/trec.130.

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Wakita, Toshihiro, Koji Ozawa, Chiyomi Miyajima, Kei Igarashi, Katsunobu Ito, Kazuya Takeda, and Fumitada Itakura. Study on Driver Identification Method Using Driving Behavior Signals. Warrendale, PA: SAE International, September 2005. http://dx.doi.org/10.4271/2005-08-0569.

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Kulhandjian, Hovannes. Detecting Driver Drowsiness with Multi-Sensor Data Fusion Combined with Machine Learning. Mineta Transportation Institute, September 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 Assistance Systems that can be installed in present-day vehicles. By integrating two modes of visual surveillance to examine a biometric expression of drowsiness, a camera and a micro-Doppler radar sensor, our system offers high reliability over 95% in the accuracy of its drowsy driver detection capabilities. The camera is used to monitor the driver’s eyes, mouth and head movement and recognize when a discrepancy occurs in the driver's blinking pattern, yawning incidence, and/or head drop, thereby signaling that the driver may be experiencing fatigue or drowsiness. The micro-Doppler sensor allows the driver's head movement to be captured both during the day and at night. Through data fusion and deep learning, the ability to quickly analyze and classify a driver's behavior under various conditions such as lighting, pose-variation, and facial expression in a real-time monitoring system is achieved.
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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, January 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 of vehicles with Driver Assistance Technology, and they are vital for successful deployment of connected and automated vehicles in the near future. However, drivers' responses to driving vehicles with advanced features have been meagerly explored. This research evaluates driver participants' response to scenarios when driving connected and automated vehicles compared to vehicles with and without Driver Assistance Technology. The research developed rural, urban, and freeway driving scenarios in a driver simulator and tested on participants sixteen years to sixty-five years old. The research team explored two types of advanced features by categorizing them into warnings and automated features. The results show that the advanced features affected driving behavior by making driver participants less aggressive and harmonizing the driving environment. This research also discovered that the type of driving scenario influences the effect of advanced features on driver behavior. Additionally, aggressive driving behavior was observed most in male participants and during nighttime conditions. Rainy conditions and female participants were associated with less aggressive driving behavior. The findings from this research help to assess driver behavior when driving vehicles with advanced features. They can be inputted into microsimulation software to model the effect of vehicles with advanced features on the performance of transportation systems, advancing technology that could eventually save millions of dollars and thousands of lives.
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Tavakoli, Arash, Vahid Balali, and Arsalan Heydarian. How do Environmental Factors Affect Drivers’ Gaze and Head Movements? Mineta Transportation Institute, August 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 scenario. The videos of both the participants’ faces and roads have been cleaned and manually categorized depending on weather, road type, and passenger conditions. Facial videos have been analyzed using OpenFace to retrieve the gaze direction and head movements of the driver. Results, overall, suggest that the gaze direction and head movements of the driver are affected by a combination of environmental factors and individual differences. Specifically, results depict the distracting effect of the passenger on some individuals. In addition, it shows that highways and city streets are the cause for maximum distraction on the driver’s gaze.
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Jacobsen, Mark. Fuel Economy and Safety: The Influences of Vehicle Class and Driver Behavior. Cambridge, MA: National Bureau of Economic Research, April 2012. http://dx.doi.org/10.3386/w18012.

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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), September 2015. http://dx.doi.org/10.2172/1483605.

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Tarko, Andrzej, and Naredla Reddy. Evaluation of Safety Enforcement on Changing Driver Behavior - Runs on Red (1 of 2 Volumes). West Lafayette, IN: Purdue University, 2003. http://dx.doi.org/10.5703/1288284313211.

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