Improve driver performance by experience of driver cognitive behavior model's practice

Liu, Y. F.; Wang, Y. M.; Li, W. S.; Xu, Wenxiang; Gui, Jiangsheng

doi:10.1109/ivs.2009.5164324

Cited by 10 publications

(6 citation statements)

References 19 publications

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“…To validate the Computational-RDT model, an experimental design was conducted based on this study [8]. The experimental design in this study involves only a post-test experiment.…”

Section: Validation Of C-rdt Modelmentioning

confidence: 99%

“…In experimental research, the focus is the representativeness of participants ahead of other randomization technique [14], Babbie added that in experimental research design, representativeness of participants is more crucial than the sample size. Therefore, to ensure representativeness of participants in this study, 20 participants (18 males, and 2 females) were selected [8,15,16] using criteria, namely valid driving licence, driving experience of more than 5 years for experienced drivers, and less than 1 year for inexperienced drivers, and minimum driving covering 5,000 km mileage per year. In addition, the participants were middle-age drivers (23 to 53 years old), having knowledge of computer, video gaming, driving game simulator, and having willingness to play the game simulator.…”

Section: Participantsmentioning

confidence: 99%

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Validation of Computational-Rabi’s Driver Training Model for Prime Decision-Making

Mustapha

Ahmed

Ahmad

2021

Communications in Computer and Information Science

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This paper explains the validation of Computational-Rabi's Driver Training (C-RDT) model for Primed Decision-making. To prove the workability of this model, evaluation using validation is indispensable. Hence, validation is a method used to ensure the logical correctness of the model. Therefore, evaluating the model by validation method is yet to be achieved in literature. Hence, this study bridged this gap by providing it, and it serves as the novelty of this study. To validate the C-RDT model, experimental method was adopted whereby an experiment was conducted using human. An adapted game driving simulator features were mapped with the external factors of the awareness component of the model, and the instrument used for the validation was also designed based on the external and temporal factors of the training component of the model. Only a post-test experiment involved to examine the effectiveness of the model factors. The experiment determines the effect of the training with the game simulator on the automaticity of the driver to make effective prime decision. For the experiment, participants were divided into control and experimental groups. The experimental group were trained while the control group were not trained. Two hypotheses were set based on the outcomes of training: H0 and H1. The results obtained has shown that the experimental group participants have better decision-making skill as compared to control group. This supports the alternative hypothesis (H1), which implies that training factors in the model improved the driver's prime decision-making during emergency and this proved the validity of the model.

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“…To validate the Computational-RDT model, an experimental design was conducted based on this study [8]. The experimental design in this study involves only a post-test experiment.…”

Section: Validation Of C-rdt Modelmentioning

confidence: 99%

Section: Participantsmentioning

confidence: 99%

Validation of Computational-Rabi’s Driver Training Model for Prime Decision-Making

Mustapha

Ahmed

Ahmad

2021

Communications in Computer and Information Science

View full text Add to dashboard Cite

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“…Some try to analyse and construct a driving behaviour model [1], [2] in order to recognise and predict individual users' actions: turning left, changing lane, etc. Others try to train the driver in unexpected events [3] or The GPR Prediction Process. Gaussian Process Regression algorithm (GPR) uses the already trained user model to predict the next system state from the current state created by the user.…”

Section: Related Workmentioning

confidence: 99%

Predicting car states through learned models of vehicle dynamics and user behaviours

Georgiou

Demiris

2015

2015 IEEE Intelligent Vehicles Symposium (IV)

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Abstract-The ability to predict forthcoming car states is crucial for the development of smart assistance systems. Forthcoming car states do not only depend on vehicle dynamics but also on user behaviour. In this paper, we describe a novel prediction methodology by combining information from both sources -vehicle and user -using Gaussian Processes. We then apply this method in the context of high speed car racing. Results show that the forthcoming position and speed of the car can be predicted with low Root Mean Square Error through the trained model.

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“…Adaptive Control of Thought-Rational (ACT-R) is human cognitive architecture. Liu et al [18] introduced this cognitive architecture in driver behavior modeling to improve Driver performance. Bi et al [20] developed a computational model of a Driver lateral control based on the queuing network-model human processor (QN-MHP) cognitive architecture [19].…”

Section: Introductionmentioning

confidence: 99%

Modeling of Driver Behavior on Trajectory–Speed Decision Making in Minor Traffic Roadways With Complex Features

Jin

Hai-bo

Shao

2019

IEEE Trans. Intell. Transport. Syst.

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The selection of trajectory and speed is a crucial factor in automobile driving behavior. However, almost all the research objects of existing Driver models are developed for driving on city streets and general highways but are not applicable to driving simulations on minor traffic roads with complex shapes, in particular. Therefore, according to the practical automobile driving processes and characteristics in the real world, this paper proposed a trajectory calculation strategy, which we named as "selecting trajectory point on a preview cross section." According to this strategy, objective functions was established to describe the different selection patterns of "trajectory and speed" of drivers. Constraint expressions were then designed on the basis of the roadway geometry and pavement condition, passenger car performance and ride comfort. A rolling-horizon algorithm for simultaneous solving, i.e., "target trajectory-target speed," was proposed. Eventually, validations of the proposed model were conducted using a race circuit and a complex mountain road as a simulation example, and the results indicated that the anticipated computational results could be obtained using the proposed decision-making model and algorithm. The result also showed a fairly good agreement with the trajectory and speed of a car on a practical circuit and mountain road.

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Improve driver performance by experience of driver cognitive behavior model's practice

Cited by 10 publications

References 19 publications

Validation of Computational-Rabi’s Driver Training Model for Prime Decision-Making

Validation of Computational-Rabi’s Driver Training Model for Prime Decision-Making

Predicting car states through learned models of vehicle dynamics and user behaviours

Modeling of Driver Behavior on Trajectory–Speed Decision Making in Minor Traffic Roadways With Complex Features

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