A Computational Agent Model of Automaticity for Driver’s Training

Mustapha, Rabi; Yusof, Yuhanis; Aziz, Azizi Ab

doi:10.1088/1757-899x/226/1/012083

Cited by 3 publications

(4 citation statements)

References 6 publications

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“…The C-RDT model has thirty-one (31) driver's training factors as against twelve (12) factors for the IDM. The 31 training factors identified were categorized into three viz.…”

Section: Computational-rabi's Driver Training (C-rdt) Modelmentioning

confidence: 99%

“…There are three driving scenarios set up based on the external factors that are in line with the three simulated scenarios in a simulation environment using MATLAB. The three driving scenarios set up are good driving conditions (low risk), average driving conditions (moderate risk) and bad driving conditions (high risk) [12,13]. The three different driving scenarios consist of different conditions in driving environment that includes road, traffic, obstacles, car condition and visibility.…”

Section: Mapping Driving Scenarios With Awareness Factors Of the Rdt Model And The Simulator Featuresmentioning

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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“…The C-RDT model has thirty-one (31) driver's training factors as against twelve (12) factors for the IDM. The 31 training factors identified were categorized into three viz.…”

Section: Computational-rabi's Driver Training (C-rdt) Modelmentioning

confidence: 99%

Section: Mapping Driving Scenarios With Awareness Factors Of the Rdt Model And The Simulator Featuresmentioning

confidence: 99%

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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“…Driver training is a process in which driving knowledge, skills, and experiences are imparted to the trainees. Driver training is essential for assessing and providing drivers with adequate skills to drive in complex and dynamic environments [1]. Currently, most people still follow a coach to learn driving skills in driving schools.…”

Section: Introductionmentioning

confidence: 99%

“…However, motor vehicle driving is a complex multi-objective decision-making process [1], and with a high real-time component. In addition, the trainee’s maneuver in each subject must be immediately successful.…”

Section: Introductionmentioning

confidence: 99%

An Intelligent Driver Training System Based on Real Cars

Duan

Yan

2019

Sensors

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In driver training, the correct observation of the trainees’ operation is the key to ensure the training quality. The operation of the vehicle can be expressed by the vehicle state changes. This paper proposes a driver training model based on a multiple-embedded-sensor net. Six vehicle state parameters are identified as the critical features of the reverse parking machine learning model and represented quantitatively. A multiple-embedded-sensor net-based system mounted on a real vehicle is developed to collect the actual data of the six critical features. The data collected at the same time are bound together and encapsulated into a vector and sequenced by time with a label given by the multiple-embedded-sensor net. All vectors are evaluated by subjective assessment conclusions from experienced driving instructors and the positive ones are used as the training data of the model. The trained model can remind the driver of the next correct operation during training, and can also analyze the improvements after the training. The model has achieved good results in practical application. The experiments prove the validity and reliability of the proposed driver training model.

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