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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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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 (September 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 following the receipt of feedback and one week post feedback. The results showed that all types of Feedback were effective in improving young drivers’ speed management behavior compared to the control group. Providing feedback about financial implications of speeding was found to be the best in improving young drivers’ speed management behavior across all tested conditions. These findings have important implications for the development of a new approach to improve young drivers’ speed management behavior.
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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 (March 15, 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 behavior toward cyclists were linked to drivers engaging more frequently in such behavior, but no such link was found for perceived green psychological workplace climate. However, perceived green psychological workplace climate moderated the link between perceived norms regarding aggressive driver behavior toward cyclists and drivers engaging in such behavior. When drivers perceived aggression toward cyclists to be common on the road, perceived green psychological workplace climate weakened the link between perceived norms regarding aggressive driver behavior towards cyclists and drivers engaging in such behavior. Findings reinforce the role of perceived road context norms regarding aggressive driver behavior toward cyclists for drivers engaging in such behavior. They illustrate that, while not directly linked, sustainability norms perceived in other contexts have a role in shaping car driver behavior towards cyclists. The study’s findings suggest that interventions targeted at aggressive behavior toward cyclists in road contexts can focus on driver behavior norms and can be complemented by normative interventions in other settings to shape a key deterrent to cycling.
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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 (December 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 overall results found that drivers with age group ‘18-21 year’ and drivers with driving experience less than one year are more likely to involve in risky driver behavior factors as compared to other studied groups. Furthermore, the Budapest drivers with age group ‘18-21 year’ and driving experience less than one year are more concerned in risky driver behavior factors such as ‘disregard speed limit’, ‘failing to use personal intelligent assistant’ and ‘frequently changing lanes’. While Islamabad drivers with the same demographic characteristics are more concerned in several risky driver behavior factors as compared to other age and driving experience groups. Moreover, ANOVA analysis was run to measure the statistical significance of risky driver behavior factors between designated groups of drivers. Finally, relative risk (RR) was measured to compare that how much times one driver group is more likely to involve in risky driver behavior factors as compared to the other driver group in the sample. The study highlighted the most frequent risky driver behavior factors for each observed group to help the local policymakers to solve related road safety issues.
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Khotimah, Khusnul, and Yogi Arisandi. "Analysis Young Driver Behavior in “Z” Generation." Journal of Sosial Science 1, no. 3 (July 26, 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 the characteristics of the generation driver "Z" in low traffic tend to move lane under the right conditions, brake suddenly with very minimal dexterity, this causes collisions with other vehicles with a very short reaction time without agility of 0.012 minutes, and not yet can improve how to drive and can not prevent wasteful consumption of fuel even in traffic that is not dense with Asymp.Sig values. (2-sided) in the amount of 0.010 - 0.014. In heavy traffic conditions, the "Z" Generation driver has the characteristic of tapping the horn and tends to brake suddenly with a very small braking distance or too close to the vehicle in front of him and very minimal dexterity with the Asymp.Sig value. (2-sided) in the amount of 0.014 - 0.017.
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Abosaq, Hamad Ali, Muhammad Ramzan, Faisal Althobiani, Adnan Abid, Khalid Mahmood Aamir, Hesham Abdushkour, Muhammad Irfan, et al. "Unusual Driver Behavior Detection in Videos Using Deep Learning Models." Sensors 23, no. 1 (December 28, 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, accounting for nearly 20% of all accidents, according to international data on accident causes. To avoid serious consequences, abnormal driving behaviors must be identified and avoided. As it is difficult to monitor anyone continuously, automated detection of this condition is more effective and quicker. To increase drivers’ recognition of their driving behaviors and prevent potential accidents, a precise monitoring approach that detects abnormal driving behaviors and identifies abnormal driving behaviors is required. The most common activities performed by the driver while driving is drinking, eating, smoking, and calling. These types of driver activities are considered in this work, along with normal driving. This study proposed deep learning-based detection models for recognizing abnormal driver actions. This system is trained and tested using a newly created dataset, including five classes. The main classes include Driver-smoking, Driver-eating, Driver-drinking, Driver-calling, and Driver-normal. For the analysis of results, pre-trained and fine-tuned CNN models are considered. The proposed CNN-based model and pre-trained models ResNet101, VGG-16, VGG-19, and Inception-v3 are used. The results are compared by using the performance measures. The results are obtained 89%, 93%, 93%, 94% for pre-trained models and 95% by using the proposed CNN-based model. Our analysis and results revealed that our proposed CNN base model performed well and could effectively classify the driver’s abnormal behavior.
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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 (June 28, 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 engineering department having high driving experience were asked to fill the dynamic questionnaire survey. The analytic network process (ANP) was applied based on pairwise comparisons of driver responses to rank the risky driver behavior factors. Network model results were used to differentiate more significant and less significant risky driving behavior factors based on measured criteria on perceived road safety issues. The analysis results revealed that "driving without alcohol use" was the most significant factor and "obeying speed limits" was the least significant factor for road safety as compared to other factors. The high rank risky driver behavior factors should be more focused to solve road safety issues.
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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 (April 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 revealed a significant influence of different social factors, such as driving behavior, and contextual factors such as weather conditions, or vehicle shape and size. Findings delivered important insights and a deeper understanding on collaborative driving that may influence future V2V communication technologies. Additionally, the collaborative driving behavior of the driver/co-driver pairs could be transferred to a multi-agent framework.
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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’ risk perception and propensity for risky driving behavior and conducted a comparative analysis of the associations between risk perception, propensity for risky driving behavior, and other factors in the presence and absence of streetlights. Participants in Hefei city, China, were asked to complete a demographic questionnaire, the Driver Behavior Questionnaire (DBQ), and the Driver Skill Inventory (DSI). Multiple linear regression analyses identified some predictors of driver behavior. The results indicated that both the DBQ and DSI are valuable instruments in traffic safety analysis in low illumination conditions and indicated that errors, lapses, and risk perception were significantly different between with and without streetlight conditions. Pearson’s correlation test found that elderly and experienced drivers had a lower likelihood of risky driving behaviors when driving in low illumination conditions, and crash involvement was positively related to risky driving behaviors. Regarding the relationship between study variables and driving skills, the research suggested that age, driving experience, and annual distance were positively associated with driving skills, while myopia, penalty points, and driving self-assessment were negatively related to driving skills. Furthermore, the differences across age groups in errors, lapses, violations, and risk perception in the presence of streetlights were remarkable, and the driving performance of drivers aged 45–55 years was superior to that of drivers in other age groups. Finally, multiple linear regression analyses showed that education background and crash involvement had a positive influence on error, whereas risk perception had a negative effect on errors; crash involvement had a positive influence, while risk perception had a negative effect on lapse; driving experience and crash involvement had a positive influence on violation; and age had a negative influence on it.
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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 over a defined study stretch. An algorithm is developed for data extraction and total 7548 acceleration and 6156 braking maneuvers and corresponding driving performance features are extracted. The driving maneuver data are analyzed using the unsupervised techniques (PCA and K-means clustering) and three patterns of acceleration and braking are identified, which are further associated with two patterns of speed behavior. The results showed that each driver is found to exhibit different driving patterns in different driving regimes and no driver shows constantly safe or aggressive behavior. The aggression scores are found to be different among drivers, indicating the behavioral heterogeneity. This study results demonstrate that, driver’s level of aggression in different driving regimes is not constant and characterizing the driver by means of abstract driving features is not indicative of the diversified driving behavior. The proposed method identifies the individualized driving behaviors, reflecting the driver’s choice of driving maneuvers. Thus, the insights from the study are highly useful to design driver-specific safety models for driver assistance and driver identification.
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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 (August 12, 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 and Road Accident Inventory. The data collected were analysis confirmatory factor analysis and Structural Equation Model (SEM) Findings - The SEM results show that the model hypothesis predictor index of road accidents have a good match but personality factors do not have affect directly on road accident and the stress of driver and driver behavior have a significant effect on Road accident; therefore the model needs to be re-specified.All of the predictors have influenced for 4% of variance on road accidents. Two predictor variables were accounted for 24% of variance on the behavior of drivers. Stress drivers directly affecting road accidents by (β = .13), and driver behavior (β = .07) .Two predictor variables on the driver behavior also reveals that the personality basis directly affects the behavior of the driver (β = .18), followed by stress of the driver have a direct influence on the behavior of drivers (β = .38). The factor of driver behavior error and lapses have strong effect to road accident Novelty - The implication this study show that there is a need for an intervention program in order to reduce the prevalence of accident involvement due to personality factors. The latter should be focused on managing driving behavior. Type of Paper - Emperical Keywords: Driver Stress, Driver Behavior, Road, Accident, Indonesia.
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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 (April 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 behavior rating scale: Driver Behavior Survey (DBS) with 21 items.ResultsThe mean age of the drivers was 40.8 ± 10.2 years. The sex ratio was 0.98. 75.9% were married. 6.9% lived alone. 53.4% were smokers and 25.9% drank alcohol. Coffee and tea consumption were 59% and 33% respectively. 67% had a pathological personal history, including osteoarticular pathologies. 17.2% had a history of serious accidents. The behavior related to anxiety among taxi drivers was 74.66 ± 13.35. The hostile behavior was 18.88 ± 8, the exaggerated safety behavior was 38.31 ± 7.3 and the deficit performance related to anxiety was 17.47 ± 7.1. The problematic behavior in our population was significantly associated with lifestyle alone, coffee consumption and with serious accidents.ConclusionsThe results of our study identified some risk factors that could lead to poorly adaptive driving behaviors among Taxi drivers. These elements reinforce us in the idea that this population requires special care with a meeting with the doctor.
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Turunen, Esko, and Klara Dolos. "Revealing Driver’s Natural Behavior—A GUHA Data Mining Approach." Mathematics 9, no. 15 (July 31, 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 significant differences between drivers? The analyzed data consist of 94,380 measurements and contain clear and understandable patterns to be found by LISp-Miner. In conclusion, we find that the GUHA method is well suited for such tasks.
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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 (December 24, 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 regarding their influences on driving safety and driving comfort of the mainline vehicle platoon. The results revealed significant variation of the algorithm influences. Specifically, the results show that the reference-trajectory-based merging algorithm may outperform the social-psychology-based merging algorithm which only considers the ramp vehicles. Originality/value To the best of the authors’ knowledge, this is the first time to evaluate a CAV control algorithm considering realistic driver interactions rather than by the simulation. To achieve the research purpose, a novel multi-driver driving simulator was developed, which enables multi-drivers to simultaneously interact with each other during a virtual driving test. The results are expected to have practical implications for further improvement of the CAV merging algorithm.
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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 (June 4, 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 nominated evaluator groups in order to detect the difference of responses on perceived road safety issues. The comparison scales had been averaged prior to computing the weights of driver behavior factors. The AHP ranking results have revealed that most of the drivers are most concerned about the “Errors”, followed by the “Lapses” for the first level. The highest influential sub-criteria for the second level is the “Aggressive violations” and for the third level, the “Drive with alcohol use”. Kendall’s rank correlation has also been applied to detect the agreement degree among the evaluator groups for each level in the hierarchical structure. The estimated results indicate that road management authorities should focus on high-rank significant driver behavior criteria to solve road safety issues for sustainable traffic safety.
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Lee, John D. "Driving Safety." Reviews of Human Factors and Ergonomics 1, no. 1 (June 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 drive safely in a broad range of situations but fail when expectations are violated or when feedback is inadequate. The review concludes by considering driving safety from a societal risk management perspective.
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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 changing with distance in positive-linear. The regressive formulas were also given. This research help us to comprehend driver behavior under emergency.
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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 game process, and the driver's optimal decision behaviors in a dynamic game was obtained. The illustration shows that impulse drivers in the decision-making period of time are more willing to choose to accelerate the first strategy; mild drivers prefer to choose acceleration strategy or uniform strategy; cautious drivers prefer to choose to uniform or deceleration strategy.
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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 (September 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, or difficulty, in using the systems. Respondents’ age and experience with Level 0 or Level 1 technologies revealed additional group differences, suggesting older drivers (55+), and those with only Level 0 systems as using ADAS more often. These data are interpreted using the Driver Behavior Questionnaire framework and offer a snapshot of the pervasiveness of certain driver safety systems.
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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 (June 6, 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 interference has a significant influence on EEG driving intention recognition. To substantially denoise EEG signals, this study selects wavelet transform theory and wavelet packet transform technology, collects the EEG signals during driving, uses the threshold noise reduction method on EEG signals to reduce noise, and achieves noise reduction through wavelet packet reconstruction. After the wavelet packet coefficients of EEG signals are obtained, the energy characteristics of the wavelet packet coefficients are extracted as input to the Bayesian theoretical model for driver behavior and intention recognition. Results show that the maximum recognition rate of the Bayesian theoretical model reaches 82.6%. Early driver behavior and intention recognition has important research significance for traffic safety and sustainable traffic development.
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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 (August 31, 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 proposed architecture is made up of two major branches: the action detection network and the object detection network, which can extract spatiotemporal and key-object features, respectively. Then, the confidence fusion mechanism is introduced to aggregate the predictions from both branches based on the semantic relationships between actions and key objects. Experiments implemented on the modified version of the public dataset Drive&Act demonstrate that the MSRNet can recognize 11 different behaviors with 64.18% accuracy and a 20 fps inference time on an 8-frame input clip. Compared to the state-of-the-art action recognition model, our approach obtains higher accuracy, especially for behaviors with similar movements.
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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 (September 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 and others’ safety, and the degree to which drivers are receptive to new in-vehicle technologies to supplement signage. Driver attitudes and reported interactions with phones while driving is discussed. The study explores the safety and acceptance potential of an in-vehicle, smartphone-based, work zone messaging system on driver behavior and roadway safety.
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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 (June 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 presence/type of traffic lights. The field studies focused on the behavior of drivers towards pedestrians and cyclists, including behavior which determines how quickly a driver can react to a non-motorized person appearing. The crossings were observed from a distance, so that the presence of the observer wouldn’t influence the participants’ behavior. Each crossing was observed and recorded for at least thirty minutes in order to identify how often a driver: stops before a crossing, drives across in front of or behind a non-motorized person, stops on the crossing or drives fast across it. In selected places, another recorded aspect was whether the driver looks around before driving across the crossing. However, such detailed observation was not possible in the majority of places due to high traffic and/or inadequate visibility of the interiors of cars. The field studies in Radom were conducted by Sebastian Pawłowski and Łukasz Zaborowski of the Radom branch of the Mazovian Office for Regional Planning. The study results indicate that dangerous driver behavior is influenced by: the width of the road on the crossing, bicycle crossings and right-of-way provisions, physical traffic calming measures and traffic lights. Measures which were found to be ineffective include hatched road markings signaling a part of the road which is not to be driven across and red lights with a green arrow allowing for a conditional right-turn after stopping, which were in fact treated as green right-turn lights. The study confirmed the accuracy of the measures indicated in the strategic and spatial planning documents of the Mazovia Region, as well as the need for them to be implemented more often. An analysis of the field study results allowed for the identification of the impact of individual road crossing parameters on drivers’ behavior, thus providing new material in reference to earlier local studies and a 2018 Polish national study. A Polish version of this article will also be published in a later issue of this periodical.
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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 (January 29, 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 vehicle data and sound methodology for classifying various driver behaviors. In this paper, we introduce a methodology that uses computer-vision vehicle trajectory data and unsupervised clustering techniques to classify different types of driver behaviors, infer the underlying mechanism and compare their impacts on safety. Two major types of behaviors are investigated, including vehicle stopping behavior and vehicle approaching patterns, using two clustering algorithms: a bisecting K-means algorithm for classifying stopping behavior, and the improved density-based spatial clustering of applications with noise (DBSCAN) algorithm for classifying vehicle approaching patterns. The methodology is demonstrated using a case study involving five stop-controlled intersections in Montreal, Canada. The results from the analysis show that there exist five distinctive classes of driver behaviors representing different levels of risk in both vehicle stopping and approaching processes. This finding suggests that the proposed methodology could be applied to develop new safety surrogate measures and risk analysis methods for network screening and countermeasure analyses of stop-controlled intersections.
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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 through the whale optimization algorithm. Finally, the well-trained model can be used to represent the human drivers’ operation effectively. The MATLAB simulation results showed that the driver model can achieve an accuracy of 90%.
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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 (May 1, 2018): 416–25. http://dx.doi.org/10.7467/ksae.2018.26.3.416.
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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’ behavior. This work aims to compare the potential of machine learning techniques to classify driver behavior at intersections and follows a data preparation process to expect interesting performance results. A comparative study was therefore conducted to explore the various data source and algorithms employed to classify driver behaviors at intersections and to address the most important techniques used. Two experiments were also developed in this paper. The first experience attempts to classify driver behavior in intersections into (1) stopping and (2) going at intersections. The second experience was based on stopping observations when approaching intersections. We classified these drivers into two categories: those who stop beyond the line (1) are considered dangerous or unsafe stops, and those who stop before the line (2) are considered safe stops. As a result, XBboost archive the best performance with 92.19% of accuracy and 94.38% of precision in the first experience and RF gives the best performance in the second experience with an accuracy of 99.38%.
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Wang, Yonggang, and Jingfeng Ma. "Automation Detection of Driver Fatigue Using Visual Behavior Variables." Archives of Civil Engineering 64, no. 2 (December 31, 2018): 175–85. http://dx.doi.org/10.2478/ace-2018-0023.
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AbstractTo examine the correlation of driver visual behaviors and subjective levels of fatigue, a total of 36 commercial drivers were invited to participate in 2-h, 3-h, and 4-h naturalistic driving tests during which their eye fixation, saccade, blinking variables, and self-awareness of their fatigue levels were recorded. Then, one-way ANOVA was applied to analyze the variations of each variable among different age groups over varying time periods. The statistical analysis revealed that driving duration had a significant effect on the variation of visual behaviors and feelings of fatigue. After 2h of driving, only the average closure duration value and subjective level of fatigue had an increase of one-fifth or more. After 4h of driving, however, all these variables had a significant change except for the number of saccades and pupil diameter measurements. Particularly, driver saccadic eye movement was more sensitive to driving fatigue, and the elderly were more likely to be affected by the duration of the drive. Finally, a predictor of driver fatigue was determined to detect the real-time level of fatigue and alert at the critical moment.
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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 making. A fuzzy model calledModel Iis presented for modeling the change of steering angle and speed control by considering time distances with existing cars in these three positions, the information about the speed and direction of car, and the steering angle of car. Also we obtained two other models based on fuzzy rules calledModel IIandModel IIIby using Sugeno fuzzy inference.Model IIandModel IIIhave less linguistic terms thanModel Ifor the steering angle and direction of car. The results of three models are compared for a driver who drives based on driving laws.
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Merickel, Jennifer, Robin High, Lynette Smith, Chris Wichman, Emily Frankel, Kaitlin Smits, Andjela Drincic, 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 (September 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 driver performance and behavior. DM participants often drove during at-risk glucose levels (low and high) and showed cognitive impairments in key domains for driving, which are likely linked to frequent hypoglycemia. The finding of increased driving risk in DM participants was mirrored in state records of crashes and traffic citations. Combining sensor data and phenotypes of driver behavior can inform patients, caregivers, safety interventions, policy, and design of supportive in-vehicle technology that is responsive to driver state.
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Rejali, Sina, Kayvan Aghabayk, and Nirajan Shiwakoti. "A Clustering Approach to Identify High-Risk Taxi Drivers Based on Self-Reported Driving Behavior." Journal of Advanced Transportation 2022 (April 16, 2022): 1–16. http://dx.doi.org/10.1155/2022/6511225.
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This study aimed to evaluate the driving behavior of taxi drivers in Isfahan, Iran, and assess the probability of a driver being among the high-risk taxi drivers. To identify risky driving behaviors among taxi drivers, the Driver Behavior Questionnaire (DBQ) was used. By collecting data from 548 taxi drivers, exploratory factor analysis identified the significant components of DBQ including “Inattention errors,” “Inexperience errors,” “Lapses,” “Ordinary violations,” and “Aggressive violations.” K-means clustering was conducted to cluster taxi drivers into three risk groups of low-risk, medium-risk, and high-risk taxi drivers based on their self-reported annual traffic crashes and fines. In addition, logistic regressions identified the extent to which drivers’ crashes and traffic fines are related to their driving behavior, and therefore, what aberrant driving behaviors are more important in explaining the presence of taxi drivers in the high-risk cluster. The results revealed that the majority of participants (66.78%) were low-risk taxi drivers. Aggressive violations and ordinary violations were significant predictors of taxi drivers being in the high-risk group, while inattention errors and aggressive violations were significant predictors of being in the medium/high-risk cluster. The findings from this study are valuable resources for developing safety measures and training for new drivers in the taxi industry.
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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 algorithm, real-world data are collected from ten drivers who drive different vehicles on several routes in real-world traffic conditions. Driver identification is performed on each of the seven-second-long driving behavior signals and geolocation signals in a streaming manner. It is shown that the proposed algorithm can achieve relatively high accuracy and identify drivers within 13 seconds. The proposed algorithm also outperforms the previously proposed driver identification algorithms. Furthermore, to demonstrate how the proposed algorithm can be deployed in real-world applications, results from real-world data associated with each operation of the proposed algorithm are shown step-by-step.
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Payyanadan, Rashmi P., and Linda S. Angell. "A Framework for Building Comprehensive Driver Profiles." Information 13, no. 2 (January 25, 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 projections. However, several challenges make this difficult. These challenges include accessing the right types of data, dealing with high-dimensional data, and identifying the underlying structure of the variance in driving behavior. There is also the challenge of how to identify key variables for detecting and predicting risk, and how to combine them in predictive algorithms. This paper proposes a systematic feature extraction and selection framework for building Comprehensive Driver Profiles that serves as a foundation for driver behavior analysis and building whole driver profiles. Features are extracted from raw data using statistical feature extraction techniques, and a hybrid feature selection algorithm is used to select the best driver profile feature set based on outcomes of interest such as crash risk. It can give rise to individualized detection and prediction of risk, and can also be used to identify types of drivers who exhibit similar patterns of driving and vehicle/technology usage. The developed framework is applied to a naturalistic driving dataset—NEST, derived from the larger SHRP2 naturalistic driving study to illustrate the types of information about driver behavior that can be harnessed—as well as some of the important applications that can be derived from it.
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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 (September 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 18% and there was a 32% increase in the proportion of hands-free driving. The decrease in driver engagement in driving was not gradual over-time but occurred immediately after engaging AP. These behaviors were maintained throughout the drive with AP until drivers approached AP disengagement. In conclusion, drivers may not be using AP as recommended (intentionally or not), reinforcing the call for improved ways to ensure drivers’ supervisory role when using partial-automation.
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Zhu, Bing, Yizhou Chen, Jian Zhao, and Yunfu Su. "Design of an Integrated Vehicle Chassis Control System with Driver Behavior Identification." Mathematical Problems in Engineering 2015 (2015): 1–12. http://dx.doi.org/10.1155/2015/954514.
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An integrated vehicle chassis control strategy with driver behavior identification is introduced in this paper. In order to identify the different types of driver behavior characteristics, a driver behavior signals acquisition system was established using the dSPACE real-time simulation platform, and the driver inputs of 30 test drivers were collected under the double lane change test condition. Then, driver behavior characteristics were analyzed and identified based on the preview optimal curvature model through genetic algorithm and neural network method. Using it as a base, an integrated chassis control strategy with active front steering (AFS) and direct yaw moment control (DYC) considering driver characteristics was established by model predictive control (MPC) method. Finally, simulations were carried out to verify the control strategy by CarSim and MATLAB/Simulink. The results show that the proposed method enables the control system to adjust its parameters according to the driver behavior identification results and the vehicle handling and stability performance are significantly improved.
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Duany, John M., and Mustapha Mouloua. "The Role of Trait Mindfulness in Aggressive Driving Behavior." Proceedings of the Human Factors and Ergonomics Society Annual Meeting 66, no. 1 (September 2022): 968–71. http://dx.doi.org/10.1177/1071181322661441.
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Aggressive driving behavior is regarded as a social dysfunction that poses a major risk to traffic safety. Previous research has indicated trait mindfulness as a protective factor for maladaptive thoughts and driver anger. The current study was designed to examine the mediating role of trait driver aggressiveness on the relationship between trait mindfulness and propensity for aggressive driving behavior. A sample of 122 drivers responded to a series of online questionnaires that assessed trait mindfulness, trait driver aggressiveness, and propensity for aggressive driving behavior. Results indicated that trait mindfulness was a significant predictor of trait driver aggressiveness. Similarly, trait mindfulness had also a moderating effect on the relationship between propensity for aggressive driving and trait driver aggressiveness. This indicated that those with higher trait mindfulness scores and lower trait driver aggressiveness scores were less likely to engage in aggressive driving behavior. Both theoretical and practical implications are discussed, and directions for future research are presented.
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Wang, Zheng, Satoshi Suga, Edric John Cruz Nacpil, Bo Yang, and Kimihiko Nakano. "Effect of Fixed and sEMG-Based Adaptive Shared Steering Control on Distracted Driver Behavior." Sensors 21, no. 22 (November 19, 2021): 7691. http://dx.doi.org/10.3390/s21227691.
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Driver distraction is a well-known cause for traffic collisions worldwide. Studies have indicated that shared steering control, which actively provides haptic guidance torque on the steering wheel, effectively improves the performance of distracted drivers. Recently, adaptive shared steering control based on the forearm muscle activity of the driver has been developed, although its effect on distracted driver behavior remains unclear. To this end, a high-fidelity driving simulator experiment was conducted involving 18 participants performing double lane change tasks. The experimental conditions comprised two driver states: attentive and distracted. Under each condition, evaluations were performed on three types of haptic guidance: none (manual), fixed authority, and adaptive authority based on feedback from the forearm surface electromyography of the driver. Evaluation results indicated that, for both attentive and distracted drivers, haptic guidance with adaptive authority yielded lower driver workload and reduced lane departure risk than manual driving and fixed authority. Moreover, there was a tendency for distracted drivers to reduce grip strength on the steering wheel to follow the haptic guidance with fixed authority, resulting in a relatively shorter double lane change duration.
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Zoupos, Alexandros, Apostolos Ziakopoulos, and George Yannis. "Modelling self-reported driver perspectives and fatigued driving via deep learning." Traffic Safety Research 1 (November 16, 2021): 000003. http://dx.doi.org/10.55329/galf7789.
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Driving while fatigued is a considerably understudied risk factor contributing to car crashes every year. The first step in mitigating the respective crash risks is to attempt to infer fatigued driving from other parameters, in order to gauge its extend in road networks. The aim of this study is to investigate the extent to which declared fatigued driving behavior can be predicted based on overall driver opinions and perceptions on that issue. For that purpose, a broad cross-country questionnaire from the ESRA2 survey was used. The questionnaire is related to self-declared beliefs, perception, and attitudes towards a wide range of traffic safety topics. Initially, a binary logistic regression model was trained to provide causal insights on which variables affect the likelihood that a driver engaged in driving while fatigued. Drivers reporting driving under the influence of drugs, fatigue, or alcohol, as well as speeding, safety, and texting while driving or drivers who were more acceptable of fatigued driving were more likely to have recently driven while fatigued. In contrast, acceptability of other hazardous behaviors, namely mobile phone use and drunk driving, was negatively correlated with fatigued driving behavior, as were more responsible driver perspectives overall. To provide a more accurate detection mechanism, which would also incorporate non-linear effects, a Deep Neural Network (DNN) was subsequently trained on the data, slightly outperforming the binary logistic model. From the results of both models, it was concluded that declared fatigued driving behavior can be predicted from questionnaire data, providing new insights to fatigue detection.
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Kolarik, Branden S., Kyra B. Phillips, Jacqueline F. Zimmermann, and David A. Krauss. "Driver stopping behavior at stop-controlled intersections with sightline limitations." Proceedings of the Human Factors and Ergonomics Society Annual Meeting 64, no. 1 (December 2020): 1471–75. http://dx.doi.org/10.1177/1071181320641350.
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Though drivers approaching a stop-sign-controlled intersection are legally required to stop at the limit line if one is present, it is well established that many drivers fail to do so. At many intersections, stopping at the limit line does not afford drivers a full view of approaching traffic, so drivers must travel past the limit line to overcome sightline obstructions including vegetation, buildings, or parked vehicles. In the present observational study, typical driver stopping/slowing behavior was studied via a camera placed at three stop-sign-controlled T-intersections. The presence of buildings at the corner of two intersections, obstructing drivers’ sightlines, explained variation in stopping behaviors across intersections. While drivers were more likely to stop at these two intersections, they reached a minimum speed further past the limit line. The findings support overcoming sight restrictions as one possible reason for the commonly observed behavior of drivers slowing or stopping beyond the limit line.
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Matawaha, Jamal Al, Khair Jadaan, and Brian Freeman. "Analysis of Speed Related Behavior of Kuwaiti Drivers Using the Driver Behavior Questionnaire." Periodica Polytechnica Transportation Engineering 48, no. 2 (May 23, 2019): 150–58. http://dx.doi.org/10.3311/pptr.13167.
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The Manchester Driver Behaviour Questionnaire (DBQ) is widely used to measure driving styles and investigate the relationship between driving behaviour and accidents involvement. Recent evaluations of different population groups have taken place throughout the world, including countries in the Arabian Gulf. This study seeks to extend the application of the DBQ to Kuwait with its mix of native and expatriate drivers, by examining the relationships between speed-related behavior and accident involvement using a speed-related score (SRS). For this purpose, 536 respondents (425 Kuwaitis and 111 Non-Kuwaitis) were asked to complete a questionnaire based on the DBQ parameters as well as background information. The results showed that young Kuwaiti male drivers scored highest in most of the areas. Factor analysis resulted in four significant dimensions; speed-related violations, anger related violations, errors, and lapses. The study focused on the speed related violation score (SRS) as the dependent variable. The statistical analysis using ANOVA and t- test showed that there is a significant effect of such factors as accident involvement, age, gender, nationality, education level, driving experience and marital status. Some countermeasures to reduce accidents were identified focusing on those groups with higher SRS values.
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Qian, Huihuan, Yongsheng Ou, Xinyu Wu, Xiaoning Meng, and Yangsheng Xu. "Support Vector Machine for Behavior-Based Driver Identification System." Journal of Robotics 2010 (2010): 1–11. http://dx.doi.org/10.1155/2010/397865.
Full textAbstract:
We present an intelligent driver identification system to handle vehicle theft based on modeling dynamic human behaviors. We propose to recognize illegitimate drivers through their driving behaviors. Since human driving behaviors belong to a dynamic biometrical feature which is complex and difficult to imitate compared with static features such as passwords and fingerprints, we find that this novel idea of utilizing human dynamic features for enhanced security application is more effective. In this paper, we first describe our experimental platform for collecting and modeling human driving behaviors. Then we compare fast Fourier transform (FFT), principal component analysis (PCA), and independent component analysis (ICA) for data preprocessing. Using machine learning method of support vector machine (SVM), we derive the individual driving behavior model and we then demonstrate the procedure for recognizing different drivers by analyzing the corresponding models. The experimental results of learning algorithms and evaluation are described.