Gotowe bibliografie tematyczne / Driver behaviour models

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Artykuły w czasopismach

Gotowa bibliografia na temat „Driver behaviour models”

Autor: Grafiati

Data publikacji: 6 września 2023

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Artykuły w czasopismach na temat "Driver behaviour models"

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 (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 (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 (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 (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 (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 (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 (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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