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Journal articles on the topic 'Driver behaviour models'
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Li, Yi, Yuren Chen, and Fan Wang. "The Impact of Traffic Environmental Vision Pressure on Driver Behaviour." Journal of Advanced Transportation 2018 (June 5, 2018): 1–12. http://dx.doi.org/10.1155/2018/4941605.
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Car-following (CF) and lane-changing (LC) behaviours are basic components in driving process. Previous models described them as physical processes with vehicle dynamics and physical criteria. However, drivers’ decisions are greatly influenced by their subjective vision information of various traffic environment elements. To solve this problem, we propose a new concept of traffic environmental vision pressure to explain these two behaviours. The pressure source consists of two parts: nearby vehicles and infrastructures. Pressure models were built to quantify the impact of traffic and roadside infrastructures on these two behaviours. 103 field tests (53 LC and 50 CF) carried out by 40 drivers were conducted to test and calibrate the models. Drivers’ psychological data and vehicle data were collected and postprocessed. Results showed positive relationship between drivers’ psychological stress and vision pressure, which verified the assumption that traffic environmental vision information would have certain effect on driver behaviour. Quantitative thresholds of pressure value were also given and explained with test data. It is concluded that the traffic environmental vision pressure in CF and LC behaviours is quite different, and higher pressure has more impact on behaviour change. We believe that these results will be helpful to study the micro driver behaviour.
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Chodur, Janusz, and Radosław Bąk. "Study of driver behaviour at turbo-roundabouts." Archives of Transport 38, no. 2 (June 30, 2016): 17–28. http://dx.doi.org/10.5604/08669546.1218790.
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The article presents the results of preliminary research into the behaviour of drivers at turbo-roundabouts. The subject of the research included the frequency of driver behaviour against the traffic rules, and the speed at which vehicles drive through turbo-roundabouts. One of the crucial problems which was analysed was the influence of different kinds of traffic lane division on the behaviour of drivers. The analysis results affirm that the raised lane dividers can visibly improve the propensity of drivers to stay within the designated traffic corridor. However, it does not eliminate the phenomenon of improper lane changing on circulatory roadway. The physical separation of traffic lanes has not been determined to introduce any additional hazard. The speed of vehicles encroaching upon the neighbouring traffic corridor is visibly higher than this of vehicles following traffic rules. Using crash prediction models developed for single- and multi-lane roundabouts, the authors of the research estimated that lane dividers may reduce the number of crashes from about 10% to 17%.
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McGordon, A., J. E. W. Poxon, C. Cheng, R. P. Jones, and P. A. Jennings. "Development of a driver model to study the effects of real-world driver behaviour on the fuel consumption." Proceedings of the Institution of Mechanical Engineers, Part D: Journal of Automobile Engineering 225, no. 11 (July 20, 2011): 1518–30. http://dx.doi.org/10.1177/0954407011409116.
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The real-world fuel economy of vehicles is becoming increasingly important to manufacturers and customers. One of the major influences in this is driver behaviour, but it is difficult to study in a controlled and repeatable manner. An assessment of driver models for studying real-world driver behaviour has been carried out. It has been found that none of the currently existing driver models has sufficient fidelity for studying the effects of real-world driver behaviour on the fuel economy of the individual vehicle. A decision-making process has been proposed which allows a driver model with a range of driving tasks to be developed. This paper reports the initial results of a driver model as applied to the conceptually straightforward scenario of high-speed cruising. Data for the driver model have been obtained through real-world data logging. It has been shown that the simulation driver model can provide a good representation of real-world driving behaviour in terms of the vehicle speed, and this is compared with a number of logged driver speed traces. A comparison of the modelled fuel economy for logged and driver model real-world drivers shows good agreement.
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Manjunath, Abhijna. "Prediction of Crash Risk based on Driving Behaviour." International Journal for Research in Applied Science and Engineering Technology 10, no. 7 (July 31, 2022): 453–56. http://dx.doi.org/10.22214/ijraset.2022.45252.
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Abstract: The major interest which is increasing for many kinds of applications includes human driver’s characterisation. There are different promising approaches in order to characterise the drivers by means of control theoretic driver models. The driver state is monitored by applying features of driver model from survey till real road distraction experiment. The dataset for the experiment consists of driving behavior with visuomotor and even few secondary tasks like auditory and even driving reference. The individual estimation of model parameters uses data of driving of nearly eleven drivers for error prediction identification. Few hand gestures and head movements are gentle way of getting distracted by drivers which covers many states like eye closure either short or long term. This paper represents the distraction detection system with the help of attention strategy. By matching the scaled features, the transformation of frontal face of the driver, driver recognition can be made. The severity of accident zone is found in particular area based on dataset. Driver behavior at particular hotspot location is found which is considered as the accident hotspot in order to gain better accuracy. The results help in validation of robustness and effectiveness of the model. Th
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Babojelić, Karlo, and Luka Novacko. "Modelling of Driver and Pedestrian Behaviour – A Historical Review." Promet - Traffic&Transportation 32, no. 5 (October 5, 2020): 727–45. http://dx.doi.org/10.7307/ptt.v32i5.3524.
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Driver and pedestrian behaviour significantly affect the safety and the flow of traffic at the microscopic and macroscopic levels. The driver behaviour models describe the driver decisions made in different traffic flow conditions. Modelling the pedestrian behaviour plays an essential role in the analysis of pedestrian flows in the areas such as public transit terminals, pedestrian zones, evacuations, etc. Driver behaviour models, integrated into simulation tools, can be divided into car-following models and lane-changing models. The simulation tools are used to replicate traffic flows and infer certain regularities. Particular model parameters must be appropriately calibrated to approximate the realistic traffic flow conditions. This paper describes the existing car-following models, lane-changing models, and pedestrian behaviour models. Further, it underlines the importance of calibrating the parameters of microsimulation models to replicate realistic traffic flow conditions and sets the guidelines for future research related to the development of new models and the improvement of the existing ones.
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Rothkrantz, Leon, Madalina Toma, and Mirela Popa. "AN INTELLIGENT CO-DRIVER SURVEILLANCE SYSTEM." Acta Polytechnica CTU Proceedings 12 (December 15, 2017): 83. http://dx.doi.org/10.14311/app.2017.12.0083.
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In recent years many car manufacturers developed digital co-drivers , which are able to monitor the driving behaviour of a car. Sensors in the car measure if a car passes speed limits, leaves its lane, or violates other traffic rules. A new generation of co-drivers is based on sensors in the car which are able to monitor the driver behaviour. Driving a car is a sequence of actions. In case a driver doesn’t show one of the actions the co-driver generates a warning signal. Experiments in the car simulator TORC were performed to extract the actions of a car driver. These actions were used to develop probabilistic models of the driving behaviour. A prototype of a warning system has been developed and tested in the car simulator. The experiments and test results will be reported in this paper.
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Yannes, Craig D., and Nicholas E. Lownes. "Driver behaviour considerations in calibrating microsimulation models for capacity." International Journal of Society Systems Science 2, no. 1 (2010): 84. http://dx.doi.org/10.1504/ijsss.2010.031468.
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Arumugam, Subramanian, and R. Bhargavi. "Road Rage and Aggressive Driving Behaviour Detection in Usage-Based Insurance Using Machine Learning." International Journal of Software Innovation 11, no. 1 (March 2, 2023): 1–29. http://dx.doi.org/10.4018/ijsi.319314.
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Driving behaviour is a critical issue in modern transportation systems due to the increasing concerns about the safety of drivers, passengers, and road users. Machine learning models are capable of learning driving patterns from sensor data and recognizing individuals by their driving behaviours. This paper presents a novel framework for aggressive driving detection and driver classification based on driving events identified from GPS data collected with smartphones and heart rate of the driver captured with a wearable device. The proposed system for road rage and aggressive driving detection (RAD) is realized with an integral framework with components for data acquisition, event detection, driver classification, and model interpretability. The system is implemented by generating a prediction model by training machine learning classifiers with a dataset collected in a cohort to classify drivers into good, unhealthy, road rage, and always bad. The proposed system is to improve road safety and to customize insurance premiums in the best interest of policy holders and insurance companies.
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Mirbeygi Moghaddam, Amirreza, Ali Ghaffari, and Alireza Khodayari. "Adaptive comfort-oriented vehicle lateral control with online controller adjustments according to driver behaviour and look-ahead dynamics." Proceedings of the Institution of Mechanical Engineers, Part K: Journal of Multi-body Dynamics 234, no. 2 (December 23, 2019): 272–87. http://dx.doi.org/10.1177/1464419319895835.
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This paper presents a comfort-oriented adaptive fuzzy-model predictive control strategy to control the lateral motion of a vehicle with the steering angle as the input while preventing sudden changes and unwanted motions. This is reached by utilizing three main contributions: an adaptive fuzzy model based on look-ahead dynamics, limiting the controller to the acceptable range of states to the driver and introducing an adjustment factor to the controller's cost function. Using adaptive-fuzzy models to describe the vehicle lateral dynamics and driver behaviour for the purpose of the control-oriented model enables this method of control to advantage from the low computational effort of the models while maintaining the accuracy and adaptive properties which are crucial to the performance and robustness of the system during the manoeuvres. Also, the characteristics of each driver's behaviour modify the controller towards a more comfort-oriented drive with means of the cost function and constraints. The comparative simulation indicates that the developed method leads to a comfort-oriented drive, in that the vehicle's states stay within the limits of the driver behaviour range, the fluctuations are insignificant and the control strategy is more accurate than previous methods.
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Bevrani, Kaveh, Edward Chung, and Pauline Teo. "The Space-Based Car-Following Model: Development and Application for Managed Motorway System Safety Evaluation." Future Transportation 1, no. 3 (September 24, 2021): 443–65. http://dx.doi.org/10.3390/futuretransp1030024.
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Traffic safety studies need more than what the current micro-simulation models can provide, as they presume that all drivers exhibit safe behaviors. Therefore, existing micro-simulation models are inadequate to evaluate the safety impacts of managed motorway systems such as Variable Speed Limits. All microscopic traffic simulation packages include a core car-following model. This paper highlights the limitations of the existing car-following models to emulate driver behaviour for safety study purposes. It also compares the capabilities of the mainstream car-following models, modelling driver behaviour with precise parameters such as headways and time-to-collisions. The comparison evaluates the robustness of each car-following model for safety metric reproductions. A new car-following model, based on the personal space concept and fish school model is proposed to simulate more accurate traffic metrics. This new model is capable of reflecting changes in the headway distribution after imposing the speed limit from variable speed limit (VSL) systems. This model can also emulate different traffic states and can be easily calibrated. These research findings facilitate assessing and predicting intelligent transportation systems effects on motorways, using microscopic simulation.
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Jurecki, Rafał S., and Tomasz L. Stańczyk. "Modelling Driver’s Behaviour While Avoiding Obstacles." Applied Sciences 13, no. 1 (January 2, 2023): 616. http://dx.doi.org/10.3390/app13010616.
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This article presents a short description of mathematical driver models. In the literature, there are no models that are generally considered fully satisfactory for use in analysing drivers’ behaviour in emergencies. This paper presents a concept of model, which includes two sub-models related to the driver’s defensive manoeuvres—avoiding the obstacle and braking. This article describes a model used for a simple road situation—a single obstacle (pedestrian) appearing on the road in front of the vehicle. In the model, the method of artificial potential fields was used, but it was enriched with the concept of safety zones around the vehicle and obstacles for three variants of the proposed shape, namely a rectangle, a circle, and an ellipse. In the simulations, parameters important for the model’s operation were used. The proposed model can be used for the simulation of human behaviour in specialised programs for accident reconstruction and in the future in assistant systems.
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Olstam, Johan, Viktor Bernhardsson, Charisma Choudhury, Gerdien Klunder, Isabel Wilmink, and Martijn van Noort. "Modelling Eco-Driving Support System for Microscopic Traffic Simulation." Journal of Advanced Transportation 2019 (December 25, 2019): 1–16. http://dx.doi.org/10.1155/2019/2162568.
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Microscopic traffic simulation is an ideal tool for investigating the network level impacts of eco-driving in different networks and traffic conditions, under varying penetration rates and driver compliance rates. The reliability of the traffic simulation results however rely on the accurate representation of the simulation of the driver support system and the response of the driver to the eco-driving advice, as well as on a realistic modelling and calibration of the driver’s behaviour. The state-of-the-art microscopic traffic simulation models however exclude detailed modelling of the driver response to eco-driver support systems. This paper fills in this research gap by presenting a framework for extending state-of-the-art traffic simulation models with sub models for drivers’ compliance to advice from an advisory eco-driving support systems. The developed simulation framework includes among others a model of driver’s compliance with the advice given by the system, a gear shifting model and a simplified model for estimating vehicles maximum possible acceleration. Data from field operational tests with a full advisory eco-driving system developed within the ecoDriver project was used to calibrate the developed compliance models. A set of verification simulations used to illustrate the effect of the combination of the ecoDriver system and drivers’ compliance to the advices are also presented.
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Ali, Faryal, Zawar Hussain Khan, Fayaz Ahmad Khan, Khurram Shehzad Khattak, and Thomas Aaron Gulliver. "A New Driver Model Based on Driver Response." Applied Sciences 12, no. 11 (May 26, 2022): 5390. http://dx.doi.org/10.3390/app12115390.
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In this paper, a new microscopic traffic model based on forward and rearward driver response is proposed. Driver response is characterized using the distance and time headways. Existing models such as the Intelligent Driver (ID) model characterize traffic flow based on a constant acceleration exponent. This exponent reflects uniform driver behaviour during different conditions which is unrealistic. Driver response is slow with a large distance headway and quick with a short time headway. Conversely, it is quick with a small distance headway and slow with a long time headway. Thus, a new microscopic traffic model is proposed which incorporates driver response. Results are given that show the proposed model provides better traffic stability than the ID model as this stability is based on traffic physics. Further, for effective utilization of road infrastructure, shorter time and longer distance headways are preferred. The performance of the ID and proposed models was evaluated over an 800 m circular road with a string of vehicles for s. These models are numerically discretized using the Euler scheme. The results obtained show that traffic queue dissemination with the proposed model is more realistic than with the ID model and the changes in density with the proposed model are smaller. This is because traffic dissemination with the proposed model is based on traffic parameters rather than a constant exponent.
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Vamshi, B., Challa Sri Nikitha, Gangula Sreya, G. Sai Sindhuja, and Hasan Nazneen. "Detection of Distracted Driver Using Convolutional Neural Network." International Journal for Research in Applied Science and Engineering Technology 11, no. 6 (June 30, 2023): 3642–51. http://dx.doi.org/10.22214/ijraset.2023.54176.
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Abstract: Abnormal driving behaviour detection helps to ensure safety of driver and passenger. Recent studies have concluded that talking on phone while driving distracts driver attention upto 20%, which leads to accidents. Deep learning models can be used to find these distracted actions. In this system the abnormal behaviour of the driver like reaching behind, hair and makeup, drinking, texting etc. are detected through deep learning. Densely connected Convolutional Neural Networks, Residual Networks are used for detection. AWGRD model, the most sophisticated model of ResNet, is formedby superpositions of previous layers, and is used to detect the driver behaviour. The input to the proposed system will be a video file. The video input can be a real time live video or it can be uploaded. In output the system detects the activity or behaviour of the driver and warns them.
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Li, S. H., and J. Y. Ren. "Driver Steering Control and Full Vehicle Dynamics Study Based on a Nonlinear Three-Directional Coupled Heavy-Duty Vehicle Model." Mathematical Problems in Engineering 2014 (2014): 1–16. http://dx.doi.org/10.1155/2014/352374.
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Under complicated driving situations, such as cornering brake, lane change, or barrier avoidance, the vertical, lateral, and longitudinal dynamics of a vehicle are coupled and interacted obviously. This work aims to propose the suitable vehicle and driver models for researching full vehicle dynamics in complicated conditions. A nonlinear three-directional coupled lumped parameters (TCLP) model of a heavy-duty vehicle considering the nonlinearity of suspension damping and tire stiffness is built firstly. Then a modified preview driver model with nonlinear time delay is proposed and connected to the TCLP model to form a driver-vehicle closed-loop system. The presented driver-vehicle closed-loop system is evaluated during a double-lane change and compared with test data, traditional handling stability vehicle model, linear full vehicle model, and other driver models. The results show that the new driver model has better lane keeping performances than the other two driver models. In addition, the effects of driver model parameters on lane keeping performances, handling stability, ride comfort, and roll stability are discussed. The models and results of this paper are useful to enhance understanding the effects of driver behaviour on full vehicle dynamics.
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Banks, Victoria A., and Neville A. Stanton. "Contrasting models of driver behaviour in emergencies using retrospective verbalisations and network analysis." Ergonomics 58, no. 8 (February 2, 2015): 1337–46. http://dx.doi.org/10.1080/00140139.2015.1005175.
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Rebitschek, Felix G., Gerd Gigerenzer, Ariane Keitel, Sarah Sommer, Christian Groß, and Gert G. Wagner. "Acceptance of criteria for health and driver scoring in the general public in Germany." PLOS ONE 16, no. 4 (April 22, 2021): e0250224. http://dx.doi.org/10.1371/journal.pone.0250224.
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Numerous health insurers offer bonus programmes that score customers’ health behaviour, and car insurers offer telematics tariffs that score driving behaviour. In many countries, however, only a minority of customers participate in these programmes. In a population-representative survey of private households in Germany (N = 2,215), we study the acceptance of the criteria (features) on which the scoring programmes are based: the features for driver scoring (speed, texting while driving, time of driving, area of driving, accelerating and braking behaviour, respectively) and for health scoring (walking distance per day, sleeping hours per night, alcohol consumption, weight, participation in recommended cancer screenings, smoking status). In a second step, we model participants’ acceptance of both programmes with regard to the underlying feature acceptance. We find that insurers in Germany rarely use the features which the participants consider to be the most relevant and justifiable, that is, smoking status for health scoring and smartphone use for driver scoring. Heuristic models (fast-and-frugal trees) show that programme acceptance depends on the acceptance of a few features. These models can help to understand customers’ preferences and to design scoring programmes that are based on scientific evidence regarding behaviours and factors associated with good health and safe driving and are thus more likely to be accepted.
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Savolainen, Reijo. "Information need as trigger and driver of information seeking: a conceptual analysis." Aslib Journal of Information Management 69, no. 1 (January 16, 2017): 2–21. http://dx.doi.org/10.1108/ajim-08-2016-0139.
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Purpose The purpose of this paper is to elaborate the picture of the motivators for information behaviour by examining the nature of information need as a trigger and driver of information seeking. Design/methodology/approach A conceptual analysis was made by focussing on the ways in which researchers have conceptualised information need in models for human information behaviour (HIB). The study draws on conceptual analysis of 26 key studies focussing on the above topic. Findings Researchers have employed two main approaches to conceptualise information needs in the HIB models. First, information need is approached as a root factor which motivates people to identify and access information sources. Second, information need is approached as a secondary trigger or driver determined by more fundamental factors, for example, the information requirements of task performance. The former approach conceptualises information need as a trigger providing an initial impetus to information seeking, while the latter approach also depicts information need as a driver that keeps the information-seeking process in motion. The latter approach is particularly characteristic of models depicting information seeking as a cyclic process. Research limitations/implications As the study focusses on information need, no attention is devoted to related constructs such as anomalous state of knowledge and uncertainty. Originality/value The study pioneers by providing an in-depth analysis of the nature of information need as a trigger and driver of information seeking. The findings refine the picture of motivators for information behaviour.
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Markkula, G., O. Benderius, and M. Wahde. "Comparing and validating models of driver steering behaviour in collision avoidance and vehicle stabilisation." Vehicle System Dynamics 52, no. 12 (September 12, 2014): 1658–80. http://dx.doi.org/10.1080/00423114.2014.954589.
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Hoseini, Seyyed Mohammad Sadat. "Comparison of Microscopic Drivers' Probabilistic Lane-changing Models With Real Traffic Microscopic Data." PROMET - Traffic&Transportation 23, no. 4 (January 25, 2012): 241–51. http://dx.doi.org/10.7307/ptt.v23i4.127.
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The difficulties of microscopic-level simulation models to accurately reproduce real traffic phenomena stem not only from the complexity of calibration and validation operations, but also from the structural inadequacies of the sub-models themselves. Both of these drawbacks originate from the scant information available on real phenomena because of the difficulty in gathering accurate field data. This paper studies the traffic behaviour of individual drivers utilizing vehicle trajectory data extracted from digital images collected from freeways in Iran. These data are used to evaluate the four proposed microscopic traffic models. One of the models is based on the traffic regulations in Iran and the three others are probabilistic models that use a decision factor for calculating the probability of choosing a position on the freeway by a driver. The decision factors for three probabilistic models are increasing speed, decreasing risk of collision, and increasing speed combined with decreasing risk of collision. The models are simulated by a cellular automata simulator and compared with the real data. It is shown that the model based on driving regulations is not valid, but that other models appear useful for predicting the driver’s behaviour on freeway segments in Iran during noncongested conditions.
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Hart, Edward, Adam Stock, George Elderfield, Robin Elliott, James Brasseur, Jonathan Keller, Yi Guo, and Wooyong Song. "Impacts of wind field characteristics and non-steady deterministic wind events on time-varying main-bearing loads." Wind Energy Science 7, no. 3 (June 8, 2022): 1209–26. http://dx.doi.org/10.5194/wes-7-1209-2022.
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Abstract. This work considers the characteristics and drivers of the loads experienced by wind turbine main bearings. Simplified load response models of two different hub and main-bearing configurations are presented, representative of both inverting direct-drive and four-point-mounted geared drivetrains. The influences of deterministic wind field characteristics, such as wind speed, shear, yaw offset, and veer, on the bearing load patterns are then investigated for similarity scaled 5, 7.5, and 10 MW reference wind turbine models. Main-bearing load response in cases of deterministic gusts and extreme changes in wind direction are also considered for the 5 MW model. Perhaps surprisingly, veer is identified as an important driver of main-bearing load fluctuations. Upscaling results indicate that similar behaviour holds as turbines become larger, but with mean loads and load fluctuation levels increasing at least cubically with the turbine rotor radius. Strong links between turbine control and main-bearing load response are also observed.
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Brow, Gerald R. "Traffic conflicts for road user safety studies." Canadian Journal of Civil Engineering 21, no. 1 (February 1, 1994): 1–15. http://dx.doi.org/10.1139/l94-001.
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In this paper we advocate an approach to road safety studies in which traffic conflicts are used as indicators of roadway hazard. The critical traffic process event used to define conflict severity is the time to collision, modified by an observer's evaluation of the risk of collision. Field studies were done to assess the validity, reliability, and practicality of traffic conflicts. Experiments were conducted to test the threshold value of time to collision, and to examine a visual perception theory of crash avoidance behaviour. The experiments demonstrate that a time to collision threshold value of 1.5 s is a reasonable time-based index of hazard and that a visual perception model of breaking is a promising concept. The wider potential for traffic conflicts as a measure of driver risk, and as a basis for safety models, is discussed in the paper. The technique has been used for some 45 intersection safety studies. From the application to date it appears that the systematic observation of crash avoidance behaviour of drivers in unsafe conditions provides a useful tool for traffic engineering analysis of intersection traffic control and design problems. Key words: road safety, traffic conflicts, time to collision, driver risk, safety models.
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ŽURA, Marijan. "Roundabout Capacity Estimation Model Considering Driver Behaviour on the Exiting and Entry Flows." Promet - Traffic&Transportation 34, no. 3 (May 31, 2022): 397–405. http://dx.doi.org/10.7307/ptt.v34i3.3973.
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Roundabout capacity estimation has been the subject of several types of research in recent decades. Most of the analyses are based on the empirical or analytical models (e.g. gap acceptance theory) considering various kinds of conflicting flows, namely entry, circulating, and exit-ing flow. The drivers on the exiting flow either obey the traffic rule (use the right-turn indicator) or disobey the traffic rule (do not use the right-turn indicator). Accord-ing to the reviewed literature, the impact of these driv-ers on the roundabout capacity has not been studied to a greater extent. Therefore, this study aims to develop an analytical roundabout capacity estimation model that also takes into account a share of exiting flow. It extends Brilon-Wu’s model, by including the impact of exiting drivers who disobey the traffic rule on the gap accep-tance of the entering drivers. The proposed model was validated using the quasi-observation data generated by a microscopic model. The results obtained by our model were compared with Bovy’ and Yaps’ empirical models as well as Brilon-Wu’s analytical model for a single-lane roundabout. Using the RMSE and regression analysis, it is proved that the proposed model outperforms the exist-ing models in terms of estimating the capacity and delays of roundabouts.
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Othman, Walaa, Alexey Kashevnik, Ammar Ali, and Nikolay Shilov. "DriverMVT: In-Cabin Dataset for Driver Monitoring including Video and Vehicle Telemetry Information." Data 7, no. 5 (May 11, 2022): 62. http://dx.doi.org/10.3390/data7050062.
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Developing a driver monitoring system that can assess the driver’s state is a prerequisite and a key to improving the road safety. With the success of deep learning, such systems can achieve a high accuracy if corresponding high-quality datasets are available. In this paper, we introduce DriverMVT (Driver Monitoring dataset with Videos and Telemetry). The dataset contains information about the driver head pose, heart rate, and driver behaviour inside the cabin like drowsiness and unfastened belt. This dataset can be used to train and evaluate deep learning models to estimate the driver’s health state, mental state, concentration level, and his/her activity in the cabin. Developing such systems that can alert the driver in case of drowsiness or distraction can reduce the number of accidents and increase the safety on the road. The dataset contains 1506 videos for 9 different drivers (7 males and 2 females) with total number of frames equal 5119k and total time over 36 h. In addition, evaluated the dataset with multi-task temporal shift convolutional attention network (MTTS-CAN) algorithm. The algorithm mean average error on our dataset is 16.375 heartbeats per minute.
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Hassein, Udai, Maksym Diachuk, and Said Easa. "Evaluation of dynamic passing gap acceptance on two-lane highways using field data." Canadian Journal of Civil Engineering 44, no. 11 (November 2017): 871–80. http://dx.doi.org/10.1139/cjce-2016-0572.
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Gap availability is an important element of safe passing on two-lane highways. Time gaps are used to determine passing behaviour based on human factors. In this paper, the decision whether to accept or reject an available passing gap is modelled using logistic regression technique that included driver characteristics (age and experience) and the gap size. Field studies were conducted to collect experimental data regarding passing driver behaviour. The data were collected using dual camera Car DVRs and a GPS data logger device that records the instantaneous speed and position of the three vehicles involved in the passing maneuver: passing vehicle, impeding vehicle, and opposing vehicle. Regression models that include driver age and gender (required as input to the gap acceptance model) were established for initial passing time, starting gap, ending gap, and time to collision. The gap acceptance model was implemented in Simulink and the results revealed that driver characteristics significantly affect gap acceptance decisions.
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Al-Jameel, Hamid Athab. "DEVELOPING A NEW HYBRID SAFETY CAR-FOLLOWING MODEL." Kufa Journal of Engineering 5, no. 2 (September 2, 2014): 47–60. http://dx.doi.org/10.30572/2018/kje/521327.
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For more than five decades, car-following models have been developed based on different theoretical backgrounds and conditions. The importance of these models belongs to their representation of longitudinal movement for any simulation model. Therefore, the accuracy of the car-following model is still the core of strength of any simulation model. For the purpose of this study, a hybrid car-following model has been developed to represent “go and stop” conditions. These conditions mostly occur in a weaving section. This model has been developed as a corner stone for a microscopic simulation model of representation of driver behaviour at a weaving section. A new condition that improves a driver behaviour in a situation at which the driver should not accelerate under specific condition as approaching from decelerated leading vehicle. Some assumptions of this model have been adopted from the CARSIMmodel. After developing this model using Visual Compact Fortran (version 6.5), the developed model has been calibrated with a set of field data from Germany. The results show that this model is more reasonable than other models such as Paramics, VISSIM, AIMSUNand CARSIMusing the same set of data. This model show significantly improvement in representing the reality than the others.
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Nirmale, Sangram Krishna, and Abdul Rawoof Pinjari. "Discrete choice models with multiplicative stochasticity in choice environment variables: Application to accommodating perception errors in driver behaviour models." Transportation Research Part B: Methodological 170 (April 2023): 169–93. http://dx.doi.org/10.1016/j.trb.2023.02.014.
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Bhamjee, Muaaz, Simon H. Connell, and André Leon Nel. "The Modification of the Dynamic Behaviour of the Cyclonic Flow in a Hydrocyclone under Surging Conditions." Mathematical and Computational Applications 27, no. 6 (October 22, 2022): 88. http://dx.doi.org/10.3390/mca27060088.
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The aim in this study was to determine how surging modifies the dynamic behaviour of the cyclonic flow in a hydrocyclone using computational fluid and granular dynamics models. The Volume-of-Fluid model was used to model the air-core formation. Fluid–particle, particle–particle, and particle–wall interactions were modelled using an unsteady two-way coupled Discrete Element Method. Turbulence was modelled using both the Reynold’s Stress Model and the Large Eddy Simulation. The model predictions indicate that the phenomenon of surging modifies the dynamics of the cyclonic flow in hydrocyclones and subsequently impacts separation. The results reveal that the primary cyclonic separation mechanisms break down during surging and result in air-core suppression. The flow and primary separation mechanism in the core of the hydrocyclone is driven by the pressure drop and the flow and primary separation mechanism near the wall is primarily driven by the gravitational and centrifugal force-induced momentum. However, surging causes a breakdown in this mechanism by swapping this primary flow and separation behaviour, where the pressure drop becomes the primary driver of the flow near the walls and gravitational and centrifugal force-induced momentum primarily drives the flow in the core of the hydrocyclone.
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Errampalli, Madhu, Masashi Okushima, and Takamasa Akiyama. "Fuzzy Logic Based Lane Change Model for Microscopic Traffic Flow Simulation." Journal of Advanced Computational Intelligence and Intelligent Informatics 12, no. 2 (March 20, 2008): 172–81. http://dx.doi.org/10.20965/jaciii.2008.p0172.
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Lane changing phenomenon is vital in representing individual vehicle behaviour in microscopic traffic simulation, yet many lane change models do not consider the uncertainties and perceptions in human behaviour that are involved in modelling lane changing. In the present study, fuzzy reasoning in lane changing model is introduced to reflect these uncertainties and perceptions to represent lane changing behaviour more realistically. The comparison of simulated results with observed data indicated that fuzzy reasoning represents driver behaviour more realistically than standard modelling. The effectiveness of the proposed technique is demonstrated in a real urban network with bus lane policy.
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Orecchini, Fabio, Adriano Santiangeli, Fabrizio Zuccari, Adriano Alessandrini, Fabio Cignini, and Fernando Ortenzi. "Real Drive Truth Test of the Toyota Yaris Hybrid 2020 and Energy Analysis Comparison with the 2017 Model." Energies 14, no. 23 (December 1, 2021): 8032. http://dx.doi.org/10.3390/en14238032.
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This paper presents the performance analysis of a latest-generation hybrid vehicle (Toyota Yaris 2020) with a testing campaign in real road conditions and a comparison with the previous model (Toyota Yaris 2017). The study was conducted by applying the Real Drive Truth Test protocol, developed by the research group, validated and spread to other full hybrid vehicles: Toyota Prius IV (2016) and Toyota Yaris 2017 (2017). In the case of the 2020 tests, the co-presence on board—deemed unsafe in the usual ways given the ongoing pandemic—was achieved through precise and sophisticated remote control. An on-board diagnostic computer, video transmission and recording equipment guarantee the virtual co-presence of a technical control room and a driver. Thus, several engineers can follow and monitor each vehicle via a 4G modem (installed in each vehicle), analysing data, route and driver behaviour in real-time, and therefore even in the presence of a single occupant in the car under test. The utmost attention has also been paid to adopting anti-COVID behaviours and safety standards: limited personal interactions, reduced co-presence in shared rooms (especially in the control room), vehicle sanitising between different drivers, computers and technicians and video technicians working once at a time. The comparison between the two subsequent vehicle models shows a significant improvement in the performance of the new generation Yaris, both in terms of operation in ZEV (zero-emission vehicle) mode (+15.3%) and in terms of consumption (−35.1%) and overall efficiency of the hybrid powertrain (+8.2%).
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Shankar, Reddy Shiva, Pilli Neelima, Voosala Priyadarshini, and Swaroop Ravi Chigurupati. "An approach to classify distraction driver detection system by using mining techniques." Indonesian Journal of Electrical Engineering and Computer Science 27, no. 3 (September 1, 2022): 1670. http://dx.doi.org/10.11591/ijeecs.v27.i3.pp1670-1680.
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According to the motor vehicle safety division, over the past 5-10 years, usage of motor vehicles has rapidly increased, in that specifical usage of cars has grown tremendously. The major contribution of this paper is a systematic evaluation of the scholarly literature on driver distraction detection techniques. Our driver distraction detection framework offers a systematic overview of evaluated methodologies for detecting driver attention. So, we need to develop a model that classifies each driver's behaviour and determines its corresponding class name. To overcome this dispute, we have attained an appreciable number of deep learning algorithms on the dataset like convolutional neural network (CNN) and VGG16 to detect what the driver is doing in the car as given in the driver images. This process can be done by predicting the likelihood of the driver's actions in each picture. Of all models, we distinguished that the VGG16 Algorithm has conquered CNN with a loss of 0.298 and an Accuracy of 91.7%.
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St John, Freya A. V., Gareth Edwards-Jones, and Julia P. G. Jones. "Conservation and human behaviour: lessons from social psychology." Wildlife Research 37, no. 8 (2010): 658. http://dx.doi.org/10.1071/wr10032.
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Despite increased effort from non-governmental organisations, academics and governments over recent decades, several threats continue to cause species declines and even extinctions. Resource use by a growing human population is a significant driver of biodiversity loss, so conservation scientists need to be interested in the factors that motivate human behaviour. Economic models have been applied to human decision making for many years; however, humans are not financially rational beings and other characteristics of the decision maker (including attitude) and the pressure that people perceive to behave in a certain way (subjective norms) may influence decision making; these are characteristics considered by social psychologists interested in human decision making. We review social-psychology theories of behaviour and how they have been used in the context of conservation and natural-resource management. Many studies focus on general attitudes towards conservation rather than attitudes towards specific behaviours of relevance to conservation and thus have limited value in designing interventions to change specific behaviours (e.g. reduce hunting of a threatened species). By more specifically defining the behaviour of interest, and investigating attitude in the context of other social-psychological predictors of behaviour (e.g. subjective norms, the presence of facilitating factors and moral obligation), behaviours that have an impact on conservation goals will be better understood, allowing for the improved design of interventions to influence them.
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Lapiedra, Oriol, Daniel Sol, Salvador Carranza, and Jeremy M. Beaulieu. "Behavioural changes and the adaptive diversification of pigeons and doves." Proceedings of the Royal Society B: Biological Sciences 280, no. 1755 (March 22, 2013): 20122893. http://dx.doi.org/10.1098/rspb.2012.2893.
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What factors determine the extent of evolutionary diversification remains a major question in evolutionary biology. Behavioural changes have long been suggested to be a major driver of phenotypic diversification by exposing animals to new selective pressures. Nevertheless, the role of behaviour in evolution remains controversial because behavioural changes can also retard evolutionary change by hiding genetic variation from selection. In the present study, we apply recently implemented Ornstein–Uhlenbeck evolutionary models to show that behavioural changes led to associated evolutionary responses in functionally relevant morphological traits of pigeons and doves (Columbiformes). Specifically, changes from terrestrial to arboreal foraging behaviour reconstructed in a set of phylogenies brought associated shorter tarsi and longer tails, consistent with functional predictions. Interestingly, the transition to arboreality accelerated the rates of evolutionary divergence, leading to an increased morphological specialization that seems to have subsequently constrained reversals to terrestrial foraging. Altogether, our results support the view that behaviour may drive evolutionary diversification, but they also highlight that its evolutionary consequences largely depend on the limits imposed by the functional demands of the adaptive zone.
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Lyu, Aihong. "Driver Distracted Behavior Detection Using a Light Weight Model based on the W-MSA." Journal of Physics: Conference Series 2560, no. 1 (August 1, 2023): 012046. http://dx.doi.org/10.1088/1742-6596/2560/1/012046.
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Abstract The percentage of traffic accidents caused by driver factors is about 90% in the world. Despite the great development of autonomous driving, it is still not completely self-driving. So, it is still not possible to avoid traffic accidents caused by drivers. Computer vision technology has made great progress with deep learning development. That makes it possible to detect the driver’s behaviour using a camera. To reduce the detection price, this paper presents a light weight model to detect the driver’s behavior based on the W-MSA. This model consists of 2 encoder modules and a classification module. And it used the Global Avgpool and W-MSA to reduce the model parameter and FLOPs. To avoid the low accuracy of the detection, this paper also used label smoothing regularization and CBAM technologies to improve the accuracy. This paper also used a visualization method to show the interpretability of the proposed model. The results show that the accuracy of the proposed model is 98% on the Kaggle driving test dataset. Compared to other state-of-the-art models, our method has a high accuracy with fewer model parameters.
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Vaa, Truls. "From Gibson and Crooks to Damasio: The role of psychology in the development of driver behaviour models." Transportation Research Part F: Traffic Psychology and Behaviour 25 (July 2014): 112–19. http://dx.doi.org/10.1016/j.trf.2014.02.004.
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Russo, Francesca, Salvatore Antonio Biancardo, and Mariarosaria Busiello. "OPERATING SPEED AS A KEY FACTOR IN STUDYING THE DRIVER BEHAVIOUR IN A RURAL CONTEXT." TRANSPORT 31, no. 2 (June 28, 2016): 260–70. http://dx.doi.org/10.3846/16484142.2016.1193054.
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The research aims to explore the effects of geometric road features on driver speed behaviour in order to identify unsafe road segments where high reductions in speed between successive road elements occur. The sample involves two-lane rural roads on flat terrain (vertical grade less than 5%) in Southern Italy, totalling 184 km without spiral transition curves between the tangent segments and circular elements. The testing was carried out on 567 study sites, of which 248 are on circular curves and 319 on tangents. Speed data collection was carried out in environmental and traffic conditions using a laser. The conditions were the following: dry roads, free flow conditions, daylight hours and good weather conditions. The main goal was to calibrate and validate different operating speed prediction models: a) one model on tangent segments; b) one model on circular curves; c) only one model to be used at the same time on tangents and circular curves. The validation process involved almost 10% of the total road network length, that was removed from the calibration phase. The speed measurements of each of the first two datasets (a, b) were grouped into ten homogeneous substrates while for the remaining dataset (c) sixteen substrates were defined by using a hard c-means algorithm. Two statistical criteria were used to remove anomalous operating speed values from each group of three datasets, namely, the Chauvenet criterion and the Vivatrat method. The first criterion was preferred in the final process of model selection. The results of the first filtering procedure showed more homogeneous samples that guaranteed a higher correlation coefficient and lower residuals of the predictive models during the validation phase than the Vivatrat method. The models were developed using an Ordinary Least Squares (OLS) method. The explanatory variables were total segment length, lane width, curvature of the road element, the curvature change rate on homogeneous road segments, and the number of residential driveways per km. ANOVA and additional synthetic statistical parameters were assessed to check the effectiveness of using a single general model to predict operating speeds at the same time on tangents and on circular curves alike. The results suggested the reliability of this hypothesis and its effectiveness in bringing advantages during the application phase.
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Rudisill, Toni Marie, Motao Zhu, and Haitao Chu. "Association between cellphone use while driving legislation and self-reported behaviour among adult drivers in USA: a cross-sectional study." BMJ Open 9, no. 2 (February 2019): e023456. http://dx.doi.org/10.1136/bmjopen-2018-023456.
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ObjectivesCellphone use behaviours can vary demographically in the USA. This study examined whether legislation restricting cellphone use while driving was associated with lower self-reported hand-held cellphone conversations or texting behaviours among adult drivers of different ages (19–24, 25–39, 40–59,≥60 years), sex, race/ethnicity (white non-Hispanic, black non-Hispanic, Hispanic, Other) or rurality (urban, rural).DesignCross-sectional study.SettingUSA.ParticipantsIndividuals ≥19 years of age who indicated they were a current driver and participated in the 2011–2014 Traffic Safety Culture Index Surveys (n=9706).Primary outcomeThe exposure was the presence of a hand-held calling or texting ban applicable to all drivers (ie, universal) at time of survey. Modified Poisson regression with robust SE was used to estimate the risk of engaging in these self-reported behaviours.ResultsIn fully adjusted models, universal texting bans were not associated with lower texting behaviours (adjusted risk ratio [aRR]=0.92; 95% CI 0.84, 1.01). In stratified, fully adjusted models, men and those of other racial/ethnic origin were 13% and 33% less likely, respectively (aRR=0.87; 95% CI 0.77, 0.98; aRR=0.67; 95% CI 0.46, 0.97), to engage in texting behaviours if a universal texting ban was effective in their state. Conversely, universal hand-held calling bans were associated with lower self-reported hand-held cellphone conversations across every sub-group. In fully adjusted models, the presence of a hand-held calling ban was associated with 40% lower (aRR=0.60, 95% CI 0.54, 0.67) self-reported hand-held cellphone conversations while driving.ConclusionsUniversal hand-held calling bans were associated with lower self-reported cellphone conversations for adult drivers. More interventional work targeting adult drivers may be needed to reduce texting while driving.
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RADOVIĆ, Dunja, Mithun MOHAN, and Vuk BOGDANOVIĆ. "Comparative Analysis of Critical Headway Estimation at Urban Single-Lane Roundabouts." Promet - Traffic&Transportation 34, no. 2 (April 15, 2022): 323–35. http://dx.doi.org/10.7307/ptt.v34i2.3902.
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According to models commonly used in practice, the capacity of roundabouts largely depends on the value of critical headway. The value of critical headway depends on the characteristics of vehicles, driving conditions, and geometric characteristics of intersections, but also on driver behaviour. Driver behaviour is the result of many factors that depend on the influence of the local environment, driver habits, mentality, etc. Accordingly, to calculate the capacity of roundabouts within the op-erational and planning analyses of roundabouts more accurately, it is necessary to use data that correspond to local conditions. In this paper, the critical headway was estimated at five urban single-lane roundabouts using five methods: Harders’, Logit, Raff’s, Wu’s, and the max-imum likelihood method. In order to determine which of the stated methods provides the most realistic estimate of critical headway, a comparison of field capacity values with theoretical capacity values was performed. Based on the comparative analysis performed in MATLAB, as well as the calculation of percentage prediction error, it was found that the Harders' method provides the most accurate estimate of critical headway at observed round-abouts in two cities in Bosnia and Herzegovina. Due to the similarity in the design of roundabouts and driver be-haviour, the results obtained in this paper can be applied in the surrounding countries, i.e., Southeast Europe
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Poor Arab Moghadam, M., and P. Pahlavani. "MOVING OBJECTS TRAJECTOTY PREDICTION BASED ON ARTIFICIAL NEURAL NETWORK APPROXIMATOR BY CONSIDERING INSTANTANEOUS REACTION TIME, CASE STUDY: CAR FOLLOWING." ISPRS - International Archives of the Photogrammetry, Remote Sensing and Spatial Information Sciences XL-1-W5 (December 11, 2015): 577–82. http://dx.doi.org/10.5194/isprsarchives-xl-1-w5-577-2015.
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Car following models as well-known moving objects trajectory problems have been used for more than half a century in all traffic simulation software for describing driving behaviour in traffic flows. However, previous empirical studies and modeling about car following behavior had some important limitations. One of the main and clear defects of the introduced models was the very large number of parameters that made their calibration very time-consuming and costly. Also, any change in these parameters, even slight ones, severely disrupted the output. In this study, an artificial neural network approximator was used to introduce a trajectory model for vehicle movements. In this regard, the Levenberg-Marquardt back propagation function and the hyperbolic tangent sigmoid function were employed as the training and the transfer functions, respectively. One of the important aspects in identifying driver behavior is the reaction time. This parameter shows the period between the time the driver recognizes a stimulus and the time a suitable response is shown to that stimulus. In this paper, the actual data on car following from the NGSIM project was used to determine the performance of the proposed model. This dataset was used for the purpose of expanding behavioral algorithm in micro simulation. Sixty percent of the data was entered into the designed artificial neural network approximator as the training data, twenty percent as the testing data, and twenty percent as the evaluation data. A statistical and a micro simulation method were employed to show the accuracy of the proposed model. Moreover, the two popular Gipps and Helly models were implemented. Finally, it was shown that the accuracy of the proposed model was much higher - and its computational costs were lower - than those of other models when calibration operations were not performed on these models. Therefore, the proposed model can be used for displaying and predicting trajectories of moving objects being followed.
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Fei, Rong, Shasha Li, Xinhong Hei, Qingzheng Xu, Fang Liu, and Bo Hu. "The Driver Time Memory Car-Following Model Simulating in Apollo Platform with GRU and Real Road Traffic Data." Mathematical Problems in Engineering 2020 (March 17, 2020): 1–18. http://dx.doi.org/10.1155/2020/4726763.
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Car following is the most common phenomenon in single-lane traffic. The accuracy of acceleration prediction can be effectively improved by the driver’s memory in car-following behaviour. In addition, the Apollo autonomous driving platform launched by Baidu Inc. provides fast test vehicle following vehicle models. Therefore, this paper proposes a car-following model (CFDT) with driver time memory based on real-world traffic data. The CFDT model is firstly constructed by embedded gantry control unit storage capacity (GRU assisted) network. Secondly, the NGSIM dataset will be used to obtain the tracking data of small vehicles with similar driving behaviours from the common real road vehicle driving tracks for data preprocessing according to the response time of drivers. Then, the model is calibrated to obtain the driver’s driving memory and the optimal parameters of the model and structure. Finally, the Apollo simulation platform with high-speed automatic driving technology is used for 3D visualization interface verification. Comparative experiments on vehicle tracking characteristics show that the CFDT model is effective and robust, which improves the simulation accuracy. Meanwhile, the model is tested and validated using the Apollo simulation platform to ensure accuracy and utility of the model.
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Comolli, Francesco, Massimiliano Gobbi, and Gianpiero Mastinu. "Study on the Driver/Steering Wheel Interaction in Emergency Situations." Applied Sciences 10, no. 20 (October 11, 2020): 7055. http://dx.doi.org/10.3390/app10207055.
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Advanced driver assistance systems (ADAS) are becoming increasingly prevalent. The tuning of these systems would benefit from a deep knowledge of human behaviour, especially during emergency manoeuvres; however, this does not appear to commonly be the case. We introduced an instrumented steering wheel (ISW) to measure three components of force and three components of the moment applied by each hand, separately. Using the ISW, we studied the kick plate manoeuvre. The kick plate manoeuvre is an emergency manoeuvre to recover a lateral disturbance inducing a spin. The drivers performed the manoeuvre either keeping two hands on the steering wheel or one hand only. In both cases, a few instants after the lateral disturbance induced by the kick plate occurred, a torque peak was applied at the ISW. Such a torque appeared to be unintentional. The voluntary torque on the ISW occurred after the unintentional torque. The emergency manoeuvre performed with only one hand was quicker, since, if two hands were used, an initial fighting of the two hands against each other was present. Therefore, we propose to model the neuro-muscular activity in driver models to consider the involuntary muscular phenomena, which has a relevant effect on the vehicle dynamic response.
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Othman, Walaa, Batol Hamoud, Alexey Kashevnik, Nikolay Shilov, and Ammar Ali. "A Machine Learning-Based Correlation Analysis between Driver Behaviour and Vital Signs: Approach and Case Study." Sensors 23, no. 17 (August 24, 2023): 7387. http://dx.doi.org/10.3390/s23177387.
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Driving behaviour analysis has drawn much attention in recent years due to the dramatic increase in the number of traffic accidents and casualties, and based on many studies, there is a relationship between the driving environment or behaviour and the driver’s state. To the best of our knowledge, these studies mostly investigate relationships between one vital sign and the driving circumstances either inside or outside the cabin. Hence, our paper provides an analysis of the correlation between the driver state (vital signs, eye state, and head pose) and both the vehicle maneuver actions (caused by the driver) and external events (carried out by other vehicles or pedestrians), including the proximity to other vehicles. Our methodology employs several models developed in our previous work to estimate respiratory rate, heart rate, blood pressure, oxygen saturation, head pose, eye state from in-cabin videos, and the distance to the nearest vehicle from out-cabin videos. Additionally, new models have been developed using Convolutional Neural Network (CNN) and Bidirectional Long Short-Term Memory (BiLSTM) to classify the external events from out-cabin videos, as well as a Decision Tree classifier to detect the driver’s maneuver using accelerometer and gyroscope sensor data. The dataset used includes synchronized in-cabin/out-cabin videos and sensor data, allowing for the estimation of the driver state, proximity to other vehicles and detection of external events, and driver maneuvers. Therefore, the correlation matrix was calculated between all variables to be analysed. The results indicate that there is a weak correlation connecting both the maneuver action and the overtaking external event on one side and the heart rate and the blood pressure (systolic and diastolic) on the other side. In addition, the findings suggest a correlation between the yaw angle of the head and the overtaking event and a negative correlation between the systolic blood pressure and the distance to the nearest vehicle. Our findings align with our initial hypotheses, particularly concerning the impact of performing a maneuver or experiencing a cautious event, such as overtaking, on heart rate and blood pressure due to the agitation and tension resulting from such events. These results can be the key to implementing a sophisticated safety system aimed at maintaining the driver’s stable state when aggressive external events or maneuvers occur.
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Suchoy, Lior, Saskia Goes, Benjamin Maunder, Fanny Garel, and Rhodri Davies. "Effects of basal drag on subduction dynamics from 2D numerical models." Solid Earth 12, no. 1 (January 20, 2021): 79–93. http://dx.doi.org/10.5194/se-12-79-2021.
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Abstract. Subducting slabs are an important driver of plate motions, yet the relative importance of different forces in governing subduction motions and styles remains incompletely understood. Basal drag has been proposed to be a minor contributor to subduction forcing because of the lack of correlation between plate size and velocity in observed and reconstructed plate motions. Furthermore, in single subduction system models, low basal drag leads to subduction behaviour most consistent with the observation that trench migration velocities are generally low compared to convergence velocities. By contrast, analytical calculations and global mantle flow models indicate basal drag can be substantial. In this study, we revisit this problem by examining the drag at the base of the lithosphere, for a single subduction system, in 2D models with a free trench and composite non-linear rheology. We compare the behaviour of short and long plates for a range of asthenospheric and lithospheric rheologies. We reproduce results from previous modelling studies, including low ratios of trench over plate motions. However, we also find that any combination of asthenosphere and lithosphere viscosity that produces Earth-like subduction behaviour leads to a correlation of velocities with plate size, due to the role of basal drag. By examining Cenozoic plate motion reconstructions, we find that slab age and plate size are positively correlated: higher slab pull for older plates tends to be offset by higher basal drag below these larger plates. This, in part, explains the lack of plate velocity–size correlation in observations, despite the important role of basal drag in the subduction force balance.
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Lim, Clark, Tarek Sayed, and Francis Navin. "A driver visual attention model. Part 1. Conceptual framework." Canadian Journal of Civil Engineering 31, no. 3 (June 1, 2004): 463–72. http://dx.doi.org/10.1139/l04-020.
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This paper describes a driver visual attention model that gathers information based on a selective process so that events such as distractions can be modelled. This model contains visual information gathering capabilities and visual attention mechanisms based on subjective and objective factors. As the research focused on applicability, the model's framework was designed to be integrated as a component processor within a microscopic computer traffic simulation. The model determines visual attention using two mechanisms: internal and external focusing. The internal focusing mechanism is a proactive attention director. This subjective-based mechanism moves the head and eye to a general direction such that information relevant to the current task is actively searched for based on the driver's expectancy. The external focusing mechanism is a reactive attention director based on the characteristics of the objects within the driver's visual field. External control allows for distractions to be modelled, since irrelevant information may objectively demand higher attention than relevant information. For each visible object, these two control mechanisms determine its attention demand value (ADV). Visual information from the object with the highest ADV is then acquired. The ADV also plays a role in determining the information processing time and amount of attention allocated to driving. With the use of this model and its input of various internal and external variables, it is hoped that a variety of driver types with varying visual abilities (age-related, intoxicated) can be simulated within visually detailed environments.Key words: driver behaviour, visibility, driver visual attention, attention demand value, driver simulation models
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Hassan, Yasser, and Tarek Sayed. "Effect of driver and road characteristics on required preview sight distance." Canadian Journal of Civil Engineering 29, no. 2 (April 1, 2002): 276–88. http://dx.doi.org/10.1139/l02-002.
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Highway geometric design is a complex process that is closely related to human perception and behaviour. Among the human perception issues that can affect highway geometric design is the preview sight distance, which has been defined as the distance required to perceive a horizontal curve and react properly to it. Previous attempts to quantify preview sight distance included measurement on actual roads, physical modelling, and computer animation. This paper presents a computer animation experiment that was designed to examine the effects of geometric parameters and driver characteristics on preview sight distance and to statistically model preview sight distance. Statistical analysis showed that preview sight distance depends on geometric parameters such as the horizontal curve radius, use of spiral curve and its length, presence of crest vertical curve, algebraic difference of vertical grades, vertical curvature, and road delineation. On the other hand, driver characteristics were mostly found to be insignificant parameters. Finally, statistical models were developed to predict the value of preview sight distance using linear regression analysis. The models vary in simplicity and accuracy and were formulated as a function of the general alignment configuration or as a function of the exact geometric parameters.Key words: highway geometric design, sight distance, driver characteristics, three-dimensional alignment.
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Priya, V. Vijay, and M. Uma. "EEG based Drowsiness Prediction Using Machine Learning Approach." Webology 18, no. 2 (December 23, 2021): 740–55. http://dx.doi.org/10.14704/web/v18i2/web18351.
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Drowsiness is the main cause of road accidents and it leads to severe physical injury, death, and significant economic losses. To monitor driver drowsiness various methods like Behaviour measures, Vehicle measures, Physiological measures and Hybrid measures have been used in previous research. This paper mainly focuses on physiological methods to predict the driver’s drowsiness. Several physiological methods are used to predict drowsiness. Among those methods, Electroencephalography is one of the non-invasive physiological methods to measure the brain activity of the subject. EEG brain signal extracted from the human scalp is analysed with various features and used for various health application like predicting drowsiness, fatigue etc. The main objective of the proposed system is to early predict the driver drowsiness with high accuracy so that we have divided our work into two steps. The first step is to collect the publicly available dataset of EEG based Eye state as (Eye open and Eye closed) where the signal acquisition process was done from Emotiv EEG Neuroheadset (14 electrodes) and analysed various feature engineering techniques and statistical techniques. The second step was applied with the machine learning classification model as K-NN and performance-based predicting models are used. In the Existing System, they used various machine learning classification models like K-NN and SVM for EEG Eye state classification and produced results around 80% -97%. Compared to the Existing system our proposed method produced better classification models for predicting driver drowsiness using different Feature engineering process and classification models as K-NN produced 98% of accuracy.
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Ellsworth, L. M., A. P. Dale, C. M. Litton, and T. Miura. "Improved fuel moisture prediction in non-native tropical Megathyrsus maximus grasslands using Moderate-Resolution Imaging Spectroradiometer (MODIS)-derived vegetation indices." International Journal of Wildland Fire 26, no. 5 (2017): 384. http://dx.doi.org/10.1071/wf16131.
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The synergistic impacts of non-native grass invasion and frequent human-derived wildfires threaten endangered species, native ecosystems and developed land throughout the tropics. Fire behaviour models assist in fire prevention and management, but current models do not accurately predict fire in tropical ecosystems. Specifically, current models poorly predict fuel moisture, a key driver of fire behaviour. To address this limitation, we developed empirical models to predict fuel moisture in non-native tropical grasslands dominated by Megathyrsus maximus in Hawaii from Terra Moderate-Resolution Imaging Spectroradiometer (MODIS)-based vegetation indices. Best-performing MODIS-based predictive models for live fuel moisture included the two-band Enhanced Vegetation Index (EVI2) and Normalized Difference Vegetation Index (NDVI). Live fuel moisture models had modest (R2=0.46) predictive relationships, and outperformed the commonly used National Fire Danger Rating System (R2=0.37) and the Keetch–Byram Drought Index (R2=0.06). Dead fuel moisture was also best predicted by a model including EVI2 and NDVI, but predictive capacity was low (R2=0.19). Site-specific models improved model fit for live fuel moisture (R2=0.61), but limited extrapolation. Better predictions of fuel moisture will improve fire management in tropical ecosystems dominated by this widespread and problematic non-native grass.
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Stanton, Neville A., James W. Brown, Kirsten M. A. Revell, Jed Clark, Joy Richardson, Pat Langdon, Mike Bradley, Nermin Caber, Lee Skrypchuk, and Simon Thompson. "Modelling Automation–Human Driver Handovers Using Operator Event Sequence Diagrams." Future Transportation 1, no. 2 (September 1, 2021): 351–69. http://dx.doi.org/10.3390/futuretransp1020020.
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This research aims to show the effectiveness of Operator Event Sequence Diagrams (OESDs) in the normative modelling of vehicle automation to human drivers’ handovers and validate the models with observations from a study in a driving simulator. The handover of control from automation to human operators has proved problematic, and in the most extreme circumstances catastrophic. This is currently a topic of much concern in the design of automated vehicles. OESDs were used to inform the design of the interaction, which was then tested in a driving simulator. This test provided, for the first time, the opportunity to validate OESDs with data gathered from videoing the handover processes. The findings show that the normative predictions of driver activity determined during the handover from vehicle automation in a driving simulator performed well, and similar to other Human Factors methods. It is concluded that OESDs provided a useful method for the human-centred automation design and, as the predictive validity shows, can continue to be used with some confidence. The research in this paper has shown that OESDs can be used to anticipate normative behaviour of drivers engaged in handover activities with vehicle automation in a driving simulator. Therefore, OESDs offer a useful modelling tool for the Human Factors profession and could be applied to a wide range of applications and domains.
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Li, Yi, and Yuren Chen. "A New Method Based on Field Strength for Road Infrastructure Risk Assessment." Journal of Advanced Transportation 2018 (September 25, 2018): 1–13. http://dx.doi.org/10.1155/2018/6379146.
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Because road infrastructures have significant impact on driving safety, their risk levels need to be evaluated dynamically according to drivers’ perception. To achieve this, this paper proposes two field strength models to quantify the impact of road infrastructures on drivers. First, road infrastructures are classified into two types (continuous and discrete). Then, two field strength models for these types are proposed. Continuous field strength model describes the impact of long-belt-shape infrastructure by differential and integral methods. Discrete field strength model describes the static and dynamic characteristics of infrastructures. This model includes four parameters: mass of vehicles, mass of infrastructures, warning level, and kinetic energy of road infrastructures. The field strength is a relative concept, which changes with vehicle state. At the end of this paper, risk assessment principles are listed to clarify the nature of road infrastructure risk evaluation. A workflow of risk assessment and a case study are presented to illustrate the application of this novel method. The result of this study shows that ① the field strength is positively related to its risk level; ② the distribution of road infrastructure risks explains driver behaviour correctly; ③ drivers tend to keep driving in low-risk area. These findings help to explain the impact mechanism of road infrastructures on drivers, which can be applied in AI-based driving assistance system in the future.
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Harri, Ardian, Drini Imami, and Edvin Zhllima. "The effect of the COVID-19 pandemic on consumer savings and retail sales: Evidence from a postcommunist transition economy." Ekonomski anali 67, no. 233 (2022): 39–59. http://dx.doi.org/10.2298/eka2233039h.
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When coupled with strong external shocks such as COVID-19, the high levels of uncertainty that characterise fragile economies can have a strong impact on household consumption and saving behaviour. This paper analyses household consumption and saving behaviour in conjunction with COVID-19 in the context of a post-communist economy. Models and intervention analysis are used to identify the effect of catastrophic events such as the COVID-19 pandemic on two key macroeconomic measures for the Albanian economy. The findings show that the pandemic period caused a significant contraction of consumer spending and a significant increase in savings. Higher uncertainty appears to have been a key driver of such household behaviour. The effect on savings will endure in the long run, while retail trade is expected to recover. These findings call for a more astute use of fiscal and monetary policies to address the harmful emerging short-run effect of reduced household spending.