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Journal articles on the topic 'Driver behavior'

Author: Grafiati

Published: 4 June 2021

Last updated: 25 July 2025

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1

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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Espino-Salinas, Carlos H., Huizilopoztli Luna-García, José M. Celaya-Padilla, et al. "Analysis and Processing of Driver Behavior for Emotion Recognition." Inteligencia Artificial 28, no. 76 (2025): 1–19. https://doi.org/10.4114/intartif.vol28iss76pp1-19.

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Road traffic injuries cause considerable economic losses to individuals, families and nations. Knowing the driver’s condition means continuously recognizing whether the driver is physically, emotionally and physiologically fit to drive the vehicle, as well as effectively communicating these situations to the driver. This research aims to collect, analyze and process behavioral signals in drivers through the interaction of the driver with the basic elements of driving to recognize different types of emotions established in the continuous model of emotional characterization proposed by Russell u

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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 (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

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Fruhen, Laura S., Patrick Benetti, Lisette Kanse, and Isabel Rossen. "Why Not Pedal for the Planet? The Role of Perceived Norms for Driver Aggression as a Deterrent to Cycling." International Journal of Environmental Research and Public Health 20, no. 6 (2023): 5163. http://dx.doi.org/10.3390/ijerph20065163.

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Cycling has many benefits for humans and the planet. This research investigates perceived norms and driver behavior toward cyclists as issues that may be useful for addressing reluctance to cycle. It connects perceived norms observed in the road context regarding aggressive driver behavior towards cyclists, and norms observed in workplaces regarding sustainability (perceived green psychological workplace climate) with driver aggressive behavior toward cyclists. Self-reported online survey responses from N = 426 Australian drivers were collected. Perceived norms regarding aggressive driver beha

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Farooq, Danish. "Statistical Evaluation of Risky Driver Behavior Factors that Influence Road Safety based on Drivers Age and Driving Experience in Budapest and Islamabad." European Transport/Trasporti Europei 80, ET.2020 (2020): 1–18. http://dx.doi.org/10.48295/et.2020.80.2.

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Driver behavior is considered as one of the most influential factors on road safety. Most of the drivers on road involve in risky driving attitudes which cause fatal and seriously injured road accidents. This study aims to evaluate and compare the risky driver behavior factors that influence road safety based on drivers age and driving experience for Budapest and Islamabad. To achieve this, the study utilized the well-proved driver behavior questionnaire (DBQ) designed on a three-point scale to analyse statistically the driver behavior responses on perceived road safety issues. The study overa

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Krasnova, Oleksandra, Brett Molesworth, and Ann Williamson. "Understanding the Effect of Feedback on Young Drivers’ Speeding Behavior." Proceedings of the Human Factors and Ergonomics Society Annual Meeting 60, no. 1 (2016): 1986–90. http://dx.doi.org/10.1177/1541931213601452.

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The aim of the present study was to empirically investigate the effect of various types of feedback on young novice drivers’ speed management behavior. One hundred young drivers, randomly allocated to five groups, completed three test drives using a computer-based driving simulator. For four groups, feedback was provided after an 11km drive and focused on speeding behavior, the safety implications of speeding or the financial penalties if caught speeding or all three. The fifth group was a no-feedback control. Driver speed management performance was examined in two 11km drives immediately foll

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Farooq, Danish, and Sarbast Moslem. "Evaluation and Ranking of Driver Behavior Factors Related to Road Safety by Applying Analytic Network Process." Periodica Polytechnica Transportation Engineering 48, no. 2 (2019): 189–95. http://dx.doi.org/10.3311/pptr.13037.

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Human behavior has been considered as a key factor in road safety. Mostly drivers involve in risky behaviors that cause road safety issues. The identification and categorization of risky driver behavior factors is very important to solve road safety issues. This study aims to evaluate and rank the most significant driver behavior factors related to road safety using multi criteria decision making applications. Driver Behavior Questionnaire (DBQ) was designed based on Saaty scale by considering the important risky driver behavior factors related to road safety. Twenty experts of transportation

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Perterer, Nicole, Susanne Stadler, Alexander Meschtscherjakov, and Manfred Tscheligi. "Driving Together Across Vehicle." International Journal of Mobile Human Computer Interaction 11, no. 2 (2019): 58–74. http://dx.doi.org/10.4018/ijmhci.2019040104.

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Most research on vehicle-to-vehicle (V2V) communication is technology-driven, or focused on driver-to-driver interaction. Social communication between drivers and passengers across vehicles, with the same destination, is often neglected. Communication is influenced by context and occupant behavior, and has a significant effect on the collaborative driving scenario. An exploratory in-situ study with seven groups of two driver/co-driver pairs each, located in two separate vehicles, was conducted. On a predefined route, different subtasks had to be solved in a collaborative way. The study reveale

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Khotimah, Khusnul, and Yogi Arisandi. "Analysis Young Driver Behavior in “Z” Generation." Journal of Sosial Science 1, no. 3 (2020): 56–60. http://dx.doi.org/10.46799/jsss.v1i3.20.

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The magnitude of the cause of the accident due to driver error causes the need to do an analysis related to the characteristics of the driver and the factors that most influence the cause of the accident and then the road. At this time the young driver is in generation Z who has an age between 16-21 years. Then an analysis of the characteristics of the causes of traffic accidents, especially in the "Z" generation of drivers by using the driver simulator "Teknosim" and the results of the analysis of observations through crosstab models and chi square test to the influential variable. Obtained t

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Khotimah, Khusnul, and Yogi Arisandi. "Analysis Young Driver Behavior in “Z” Generation." Journal of Social Science 1, no. 3 (2020): 56–60. http://dx.doi.org/10.46799/jss.v1i3.20.

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The magnitude of the cause of the accident due to driver error causes the need to do an analysis related to the characteristics of the driver and the factors that most influence the cause of the accident and then the road. At this time the young driver is in generation Z who has an age between 16-21 years. Then an analysis of the characteristics of the causes of traffic accidents, especially in the "Z" generation of drivers by using the driver simulator "Teknosim" and the results of the analysis of observations through crosstab models and chi square test to the influential variable. Obtained t

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Abosaq, Hamad Ali, Muhammad Ramzan, Faisal Althobiani, et al. "Unusual Driver Behavior Detection in Videos Using Deep Learning Models." Sensors 23, no. 1 (2022): 311. http://dx.doi.org/10.3390/s23010311.

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Anomalous driving behavior detection is becoming more popular since it is vital in ensuring the safety of drivers and passengers in vehicles. Road accidents happen for various reasons, including health, mental stress, and fatigue. It is critical to monitor abnormal driving behaviors in real time to improve driving safety, raise driver awareness of their driving patterns, and minimize future road accidents. Many symptoms appear to show this condition in the driver, such as facial expressions or abnormal actions. The abnormal activity was among the most common causes of road accidents, accountin

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Fikri, Fikri, Rozmi Ismail, and Fatimah Wati Halim. "The Influence of Personality, Driver Stress and Driver Behavior as Mediator on Road Accident among bus Driver in Riau Province Indonesia." Global Journal of Business and Social Science Review (GJBSSR) Volume 4 (2016: Issue-3) 4, no. 3 (2016): 56–62. http://dx.doi.org/10.35609/gjbssr.2016.4.3(8).

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Objective - This study aims to examine the contribution of personality, stress of driver and driver behavior as a mediator on road accident among bus driver in Indonesia. The study adopts a survey method to elicit responses from a sample of 400 bus driver who were selected as a Respondent type. The brief purpose of the paper and illustrate the direction that is taken, whether it is empirical or theoretical testing in analyzing the research subject. Methodology/Technique - The Data collecting using the Big Five Personality questionnaires, Driver Stress Inventory, Driver Behavior questionnaires

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Liu, Jing, Cheng Wang, Zhipeng Liu, Zhongxiang Feng, and N. N. Sze. "Drivers’ Risk Perception and Risky Driving Behavior under Low Illumination Conditions: Modified Driver Behavior Questionnaire (DBQ) and Driver Skill Inventory (DSI)." Journal of Advanced Transportation 2021 (November 19, 2021): 1–13. http://dx.doi.org/10.1155/2021/5568240.

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Most road crashes are caused by human factors. Risky behaviors and lack of driving skills are two human factors that contribute to crashes. Considering the existing evidence, risky driving behaviors and driving skills have been regarded as potential decisive factors explaining and preventing crashes. Nighttime accidents are relatively frequent and serious compared with daytime accidents. Therefore, it is important to focus on driving behaviors and skills to reduce traffic accidents and enhance safe driving in low illumination conditions. In this paper, we examined the relation between drivers’

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Salbi, Adil, Mohamed Amine Gadi, Tarik Bouganssa, Abdelhadi Eloudrhiri Hassani, and Abdelali Lasfar. "Design and implementation of a driving safety assistant system based on driver behavior." IAES International Journal of Artificial Intelligence (IJ-AI) 13, no. 3 (2024): 2603. http://dx.doi.org/10.11591/ijai.v13.i3.pp2603-2613.

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<p>These days, road accidents are one of Morocco's biggest problems. Fatigue, drowsiness, and driver behavior are among the primary causes.This research aims to develop an embedded system by image processing and computer vision to ensure driving safety by monitoring driver behavior and assist drivers to awaken from micro-sleep or fatigue due to long driving hours and various other reasons. Indeed, the driver inattention, drowsiness or driver fatigue can be detected. The suggested method is designed to support drivers if needed, based on the vehicle velocity. Once the driver crosses a cer

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Adil, Salbi, Amine Gadi Mohamed, Bouganssa Tarik, Eloudrhiri Hassani Abdelhadi, and Lasfar Abdelali. "Design and implementation of a driving safety assistant system based on driver behavior." IAES International Journal of Artificial Intelligence (IJ-AI) 13, no. 3 (2024): 2603–13. https://doi.org/10.11591/ijai.v13.i3.pp2603-2613.

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These days, road accidents are one of Morocco's biggest problems. Fatigue, drowsiness, and driver behavior are among the primary causes. This research aims to develop an embedded system by image processing and computer vision to ensure driving safety by monitoring driver behavior and assist drivers to awaken from micro-sleep or fatigue due to long driving hours and various other reasons. Indeed, the driver inattention, drowsiness or driver fatigue can be detected. The suggested method is designed to support drivers if needed, based on the vehiclevelocity. Once the driver crosses a certain spee

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Yarlagadda, Jahnavi, and Digvijay S. Pawar. "Heterogeneity in the Driver Behavior: An Exploratory Study Using Real-Time Driving Data." Journal of Advanced Transportation 2022 (June 18, 2022): 1–17. http://dx.doi.org/10.1155/2022/4509071.

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Driver behavior heterogeneity is a significant aspect to understand the individual behavioral variations and develop driver assistance systems. This study characterizes the heterogeneity in driving behavior using real-time driving performance features. In this context, the study investigates the extent of variations in the individual’s driving styles during routine driving. The driving styles are conceptualized using the vehicle kinematic data, that is, speed and accelerations performed during longitudinal control. The data is collected for 42 professional drivers using instrumented vehicle ov

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Bejar, M., N. Regaieg, D. Gdoura, J. Aloulou, and O. Amami. "Anxious driving behavior among taxi drivers." European Psychiatry 64, S1 (2021): S184—S185. http://dx.doi.org/10.1192/j.eurpsy.2021.488.

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IntroductionThe data suggest that anxious drivers may engage in problem behaviors that expose them and others to an increased risk of negative traffic events.ObjectivesTo study the problematic behavior taxi drivers related to anxiety in three areas exaggerated safety/caution, performance deficits, and hostile/aggressive behaviors and to determine the factors who are associated with them.MethodsThis is a cross-sectional descriptive and analytical study of 58 taxi drivers in the city of Sfax, Tunisia. We used an anonymous questionnaire that included a socio-demographic fact sheet, and a driver b

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Lee, John D. "Driving Safety." Reviews of Human Factors and Ergonomics 1, no. 1 (2005): 172–218. http://dx.doi.org/10.1518/155723405783703037.

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Driving is a common and hazardous activity that is a prominent cause of death worldwide. Driver behavior represents a predominant cause, contributing to over 90% of crashes. In this review, I will focus on how driver behavior influences driving safety by describing the types of crashes and their general causes, the driving process, the perceptual and cognitive characteristics of drivers, and driver types and impairments. Evidence from each of these perspectives suggests that breakdowns of a multilevel control process are the fundamental factors that undermine driving safety. Drivers adapt and

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Turunen, Esko, and Klara Dolos. "Revealing Driver’s Natural Behavior—A GUHA Data Mining Approach." Mathematics 9, no. 15 (2021): 1818. http://dx.doi.org/10.3390/math9151818.

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We investigate the applicability and usefulness of the GUHA data mining method and its computer implementation LISp-Miner for driver characterization based on digital vehicle data on gas pedal position, vehicle speed, and others. Three analytical questions are assessed: (1) Which measured features, also called attributes, distinguish each driver from all other drivers? (2) Comparing one driver separately in pairs with each of the other drivers, which are the most distinguishing attributes? (3) Comparing one driver separately in pairs with each of the other drivers, which attributes values show

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Yue, Lishengsa, Mohamed Abdel-Aty, and Zijin Wang. "Effects of connected and autonomous vehicle merging behavior on mainline human-driven vehicle." Journal of Intelligent and Connected Vehicles 5, no. 1 (2021): 36–45. http://dx.doi.org/10.1108/jicv-08-2021-0013.

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Purpose This study aims to evaluate the influence of connected and autonomous vehicle (CAV) merging algorithms on the driver behavior of human-driven vehicles on the mainline. Design/methodology/approach Previous studies designed their merging algorithms mostly based on either the simulation or the restricted field testing, which lacks consideration of realistic driving behaviors in the merging scenario. This study developed a multi-driver simulator system to embed realistic driving behavior in the validation of merging algorithms. Findings Four types of CAV merging algorithms were evaluated r

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Farooq, Danish, Sarbast Moslem, and Szabolcs Duleba. "Evaluation of Driver Behavior Criteria for Evolution of Sustainable Traffic Safety." Sustainability 11, no. 11 (2019): 3142. http://dx.doi.org/10.3390/su11113142.

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Driver behavior has been considered as the most influential factor in reducing fatal road accidents and the resulting injuries. Thus, it is important to focus on the significance of driver behavior criteria to solve road safety issues for a sustainable traffic system. The recent study aims to enumerate the most significant driver behavior factors which have a critical impact on road safety. The well-proven Analytic Hierarchy Process (AHP) has been applied for 20 examined driver behavior factors in a three-level hierarchical structure. Linguistic judgment data have been collected from three nom

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Chen, Xue Mei, Zhong Hua Wei, Li Gao, and Xi Wang. "The Research on the Driver Steering Behavior under Emergency." Applied Mechanics and Materials 44-47 (December 2010): 1796–801. http://dx.doi.org/10.4028/www.scientific.net/amm.44-47.1796.

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Drivers are used to steering when they meet obstacles in order to protecting the measure and lives. Freecod-data collector, dynamics GPS and the sensors of steering degree was utilized to study the rule of driver steering velocities, the relationships between maximum steering velocities and distance, and as well as maximum steering velocities and driving velocities were eastablished. The results show that drivers steering operation includes five periods and also indicates that driver maximum steering velocities is changing with distance in nagitive and driver maximum steering velocities is cha

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Palac, Daniel, Iiona D. Scully, Rachel K. Jonas, John L. Campbell, Douglas Young, and David M. Cades. "Advanced Driver Assistance Systems (ADAS): Who’s Driving What and What’s Driving Use?" Proceedings of the Human Factors and Ergonomics Society Annual Meeting 65, no. 1 (2021): 1220–24. http://dx.doi.org/10.1177/1071181321651234.

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The emergence of vehicle technologies that promote driver safety and convenience calls for investigation of the prevalence of driver assistance systems as well as of their use rates. A consumer driven understanding as to why certain vehicle technology is used remains largely unexplored. We examined drivers’ experience using 13 different advanced driver assistance systems (ADAS) and several reasons that may explain rates of use through a nationally-distributed survey. Our analysis focused on drivers’ levels of understanding and trust with their vehicle’s ADAS as well as drivers’ perceived ease,

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Zhang, Lin, Xuan Wei huang, and Wei Ming Wu. "The Analysis of Driver's Behavior in Non-Signalized Intersection Based on the Game." Applied Mechanics and Materials 505-506 (January 2014): 1157–62. http://dx.doi.org/10.4028/www.scientific.net/amm.505-506.1157.

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In order to study the driver's decision-making behaviors of the conflict vehicles in non-signalized intersection, according to time refinement, the driver's personality factors and the relative potential factors in the different strategies which affect the driver to make decisions, Based on the dynamic reduplicate game theory, the utility function of the driver's behaviors was built up. As the decision-making behavior by the driver in the process of cross-road, analyzing the different combination of the utility of the driver's decision-making behavior, Nash equilibrium was existed in a single

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Achtemeier, Jacob D., and Nichole L. Morris. "An Assessment of Safety Culture While Navigating Work Zones." Proceedings of the Human Factors and Ergonomics Society Annual Meeting 60, no. 1 (2016): 1499–503. http://dx.doi.org/10.1177/1541931213601344.

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A survey was administered to evaluate a variety of driver opinions, behaviors, and technology use in the context of work zones. Safety culture thematic inquiries, such as willingness to use a cell phone while driving, as well as adherence and trust of conventional work zone signage were included in the driver behavior inventory. Thematic results were examined through a factor analyses, providing insight into the relationship among responses to survey items. Study results contribute to the understanding of driver attitudes towards conventional signage in work zones, driver perceptions of their

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Li, Min, Wuhong Wang, Zhen Liu, Mingjun Qiu, and Dayi Qu. "Driver Behavior and Intention Recognition Based on Wavelet Denoising and Bayesian Theory." Sustainability 14, no. 11 (2022): 6901. http://dx.doi.org/10.3390/su14116901.

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Driver behavior and intention recognition affects traffic safety. Many scholars use the steering wheel angle, distance of the brake pedal, distance of the accelerator pedal, and turn signal as input data to identify driver behaviors and intentions. However, in terms of time, the acquisition of these parameters has a relative delay, which lengthens the identification time. Therefore, this study uses drivers’ EEG (electroencephalograph) data as input parameters to identify driver behaviors and intentions. The key to the driving intention recognition of EEG signals is to reduce their noise. Noise

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Taiwo, Olusegun Austine, Sitti Asmah Hassan, Rahmat Bin Mohsin, and Norashikin Mahmud. "Analysis of Accident Predictability and the Use of Driver Behaviour Questionnaire: A Systematic Review." International Journal of Research and Innovation in Social Science VIII, no. III (2024): 2347–66. http://dx.doi.org/10.47772/ijriss.2024.803164.

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Road Traffic Accidents (RTAs) have continued to be a menace in the world with the rising increase in road crashes due to driver behaviours. Despite using the Driver Behaviour Questionnaire (DBQ) for examining driver behaviours, little is known about the variability in predicting accident. The present systematic literature review study investigated the variation of accident predictors through DBQ approach. The PRISMA procedure was used to extract the reviewed articles from Scopus database using the keywords, “Driver Behaviour Questionnaire” OR “Driver Behavior Questionnaire” OR “DBQ” AND “Accid

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Ren, Hongze, Yage Guo, Zhonghao Bai, and Xiangyu Cheng. "A Multi-Semantic Driver Behavior Recognition Model of Autonomous Vehicles Using Confidence Fusion Mechanism." Actuators 10, no. 9 (2021): 218. http://dx.doi.org/10.3390/act10090218.

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With the rise of autonomous vehicles, drivers are gradually being liberated from the traditional roles behind steering wheels. Driver behavior cognition is significant for improving safety, comfort, and human–vehicle interaction. Existing research mostly analyzes driver behaviors relying on the movements of upper-body parts, which may lead to false positives and missed detections due to the subtle changes among similar behaviors. In this paper, an end-to-end model is proposed to tackle the problem of the accurate classification of similar driver actions in real-time, known as MSRNet. The propo

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Wen, Xiamei, Liping Fu, Ting Fu, Jessica Keung, and Ming Zhong. "Driver Behavior Classification at Stop-Controlled Intersections Using Video-Based Trajectory Data." Sustainability 13, no. 3 (2021): 1404. http://dx.doi.org/10.3390/su13031404.

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Understanding how drivers behave at stop-controlled intersection is of critical importance for the control and management of an urban traffic system. It is also a critical element of consideration in the burgeoning field of smart infrastructure and connected and autonomous vehicles (CAV). A number of past efforts have been devoted to investigating the driver behavioral patterns when they pass through stop-controlled intersections. However, the majority of these studies have been limited to qualitative descriptions and analyses of driver behavior due to the unavailability of high-resolution veh

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Sakshi, Ingale1 Neha Thakur2 Mrunmayee Bodhale3 Vaishnavi Dighe4 Saili Sable5 Prasad Dhore6. "Survey on Driver's Drowsiness Monitoring System Using Visual Behavior." Scandinavian Journal of Information Systems 35, no. 1 (2023): 158–64. https://doi.org/10.5281/zenodo.7858199.

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The one of the main factors contributing to traffic accidents is tired riding. Accident rates can be lowered if the driver's drowsiness can be identified early on and detected, as well as if the driver is made aware of it. This study proposes a machine learning-based driver sleepiness detection system. The primary goal of this research is to develop a drowsiness system that is based on the eyes. It is thought that it is possible to identify the signs of driver weariness early enough to prevent a collision. In this scenario, a warning signal is sent to the driver when drowsiness is discover

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Payyanadan, Rashmi P., and Linda S. Angell. "A Framework for Building Comprehensive Driver Profiles." Information 13, no. 2 (2022): 61. http://dx.doi.org/10.3390/info13020061.

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Conventional approaches to modelling driver risk have incorporated measures such as driver gender, age, place of residence, vehicle model, and annual miles driven. However, in the last decade, research has shown that assessing a driver’s crash risk based on these variables does not go far enough—especially as advanced technology changes today’s vehicles, as well as the role and behavior of the driver. There is growing recognition that actual driver usage patterns and driving behavior, when it can be properly captured in modelling risk, offers higher accuracy and more individually tailored proj

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Sulmicki, Maciej. "The impact of infrastructure on driver behavior on pedestrian crossings – case studies in two Mazovian cities." Mazowsze Studia Regionalne 2020, no. 33 (2020): 97–117. http://dx.doi.org/10.21858/msr.33.06.

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In 2019 field studies were conducted in order to check how various aspects of pedestrian and cycle crossing infrastructure influence driver behavior. The overall goal was to verify the adequacy of the road safety-related provisions of the main strategic and planning documents of the Mazovia Region. The crossings analyzed in Warsaw and Radom were chosen so as to take into account all the types of traffic calming mentioned in the Spatial Development Plan of Mazovia as serving to improve safety on pedestrian crossings. Other aspects taken into account included road width, type of intersection and

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Venkatapathi, Pallam, Kondu Vinay, Kummathi Harshavardhan Reddy, Mulli Karthik, and Dr Sudhakar Alluri. "Real Time Driver Gaze Tracking and Eyes off the Road Detection System." International Journal for Research in Applied Science and Engineering Technology 11, no. 10 (2023): 1906–11. http://dx.doi.org/10.22214/ijraset.2023.56316.

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Abstract: Driver Gaze Tracking and Eyes Off the Road Detection System is a technology-driven solution designed to enhance road safety by monitoring driver behavior and detecting instances when the driver's attention is diverted from the road. The system utilizes computer vision techniques and machine learning algorithms to track the driver's eye movements and identify potential distractions. By providing real-time alerts and warnings, this system aims to mitigate the risk of accidents caused by distracted driving. Distracted driving poses a significant risk to road safety, and traditional meth

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Liu, Junhui, Yajuan Jia, Yaya Wang, and Petr Dolezel. "Development of Driver-Behavior Model Based onWOA-RBM Deep Learning Network." Journal of Advanced Transportation 2020 (September 29, 2020): 1–11. http://dx.doi.org/10.1155/2020/8859891.

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Human drivers’ behavior, which is very difficult to model, is a very complicated stochastic system. To characterize a high-accuracy driver behavior model under different roadway geometries, the paper proposes a new algorithm of driver behavior model based on the whale optimization algorithm-restricted Boltzmann machine (WOA-RBM) method. This method establishes an objective optimization function first, which contains the training of RBM deep learning network based on the real driver behavior data. Second, the optimal training parameters of the restricted Boltzmann machine (RBM) can be obtained

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Arabiat, Areen, and Muneera Altayeb. "Driving behavior analytics: an intelligent system based on machine learning and data mining techniques." Bulletin of Electrical Engineering and Informatics 14, no. 3 (2025): 2055–65. https://doi.org/10.11591/eei.v14i3.9095.

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One of the most common causes of road accidents is driver behavior. To reduce abnormal driver behavior, it must be detected early on. Previous research has demonstrated that behavioral and physiological indicators affect drivers' performance. The goal of this study is to consider the feasibility of classifying driver behavior as either aggressive (sudden left or right turns, accelerating and braking), normal (average driving events) or slow (keeping a lower-than-average speed). Innovation in data mining and machine learning (ML) has allowed for the creation of powerful prediction tools. ML tec

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Bouhsissin, S., N. Sael, and F. Benabbou. "CLASSIFICATION AND MODELING OF DRIVER BEHAVIOR DURING YELLOW INTERVALS AT INTERSECTIONS." International Archives of the Photogrammetry, Remote Sensing and Spatial Information Sciences XLVIII-4/W3-2022 (December 2, 2022): 33–40. http://dx.doi.org/10.5194/isprs-archives-xlviii-4-w3-2022-33-2022.

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Abstract. The violation of traffic rules is, nowadays, the most important cause of accidents. Passing an intersection or a red light can be fatal for a driver and lead to serious damage. In fact, when the driver encounters a signal change from green to yellow, he or she is required to make a decision to stop or to go based on many factors. Making the wrong decision will result in a red-light violation or an abrupt stop at the intersection. Researchers typically focus on the connection between driving behavior and decision-making because of its importance in controlling aggressive drivers’ beha

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Tanprasert, Thitaree, Chalermpol Saiprasert, and Suttipong Thajchayapong. "Combining Unsupervised Anomaly Detection and Neural Networks for Driver Identification." Journal of Advanced Transportation 2017 (2017): 1–13. http://dx.doi.org/10.1155/2017/6057830.

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This paper proposes an algorithm for real-time driver identification using the combination of unsupervised anomaly detection and neural networks. The proposed algorithm uses nonphysiological signals as input, namely, driving behavior signals from inertial sensors (e.g., accelerometers) and geolocation signals from GPS sensors. First anomaly detection is performed to assess if the current driver is whom he/she claims to be. If an anomaly is detected, the algorithm proceeds to find relevant features in the input signals and use neural networks to identify drivers. To assess the proposed algorith

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Ghaemi, Sehraneh, Sohrab Khanmohammadi, and Mohammadali Tinati. "Driver's Behavior Modeling Using Fuzzy Logic." Mathematical Problems in Engineering 2010 (2010): 1–29. http://dx.doi.org/10.1155/2010/172878.

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In this study, we propose a hierarchical fuzzy system for human in a driver-vehicle-environment system to model takeover by different drivers. The driver's behavior is affected by the environment. The climate, road and car conditions are included in fuzzy modeling. For obtaining fuzzy rules, experts' opinions are benefited by means of questionnaires on effects of parameters such as climate, road and car conditions on driving capabilities. Also the precision, age and driving individuality are used to model the driver's behavior. Three different positions are considered for driving and decision

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Park, Changwoo, Wonbin Na, and Hyeongcheol Lee. "Driver Friendly Adaptive Cruise Control by Driver Behavior." Transaction of the Korean Society of Automotive Engineers 26, no. 3 (2018): 416–25. http://dx.doi.org/10.7467/ksae.2018.26.3.416.

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Morando, Alberto, Pnina Gershon, Bruce Mehler, and Bryan Reimer. "Visual attention and steering wheel control: From engagement to disengagement of Tesla Autopilot." Proceedings of the Human Factors and Ergonomics Society Annual Meeting 65, no. 1 (2021): 1390–94. http://dx.doi.org/10.1177/1071181321651118.

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Previous research indicates that drivers may forgo their supervisory role with partial-automation. We investigated if this behavior change is the result of the time automation was active. Naturalistic data was collected from 16 Tesla owners driving under free-flow highway conditions. We coded glance location and steering-wheel control level around Tesla Autopilot (AP) engagements, driver-initiated AP disengagements, and AP steady-state use in-between engagement and disengagement. Results indicated that immediately after AP engagement, glances downwards and to the center-stack increased above 1

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Merickel, Jennifer, Robin High, Lynette Smith, et al. "At-Risk Driving Behavior in Drivers with Diabetes: A Neuroergonomics Approach." Proceedings of the Human Factors and Ergonomics Society Annual Meeting 61, no. 1 (2017): 1881–85. http://dx.doi.org/10.1177/1541931213601950.

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This pilot study tackles the overarching need for driver-state detection through real-world measurements of driver behavior and physiology in at-risk drivers with type 1 diabetes mellitus (DM). 35 drivers (19 DM, 14 comparison) participated. Real-time glucose levels were measured over four weeks with continuous glucose monitor (CGM) wearable sensors. Contemporaneous real-world driving performance and behavior were measured with in-vehicle video and electronic sensor instrumentation packages. Results showed clear links between at-risk glucose levels (particularly hypoglycemia) and changes in dr

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