Adaptive driving simulation-based training: framework, status, and needs

Zahabi, Maryam; Park, JunHo; Razak, Ashiq Mohammed Abdul; McDonald, Anthony D.

doi:10.1080/1463922x.2019.1698673

Cited by 11 publications

(2 citation statements)

References 75 publications

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“…SA's versatility allows for the simulation of diverse driving scenarios, conditions, and performance metrics, facilitating both customized rehabilitation programs and controlled research environments [6] , [18] . SA comes with its unique benefits, allowing the driver to experience various types of driving scenarios in safe conditions without the risks of real-life danger [19] .…”

Section: Introductionmentioning

confidence: 99%

A systematic review of major evaluation metrics for simulator-based automatic assessment of driving after stroke

Taveekitworachai,

Chanmas,

Paliyawan

et al. 2024

Heliyon

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Section: Introductionmentioning

confidence: 99%

A systematic review of major evaluation metrics for simulator-based automatic assessment of driving after stroke

Taveekitworachai,

Chanmas,

Paliyawan

et al. 2024

Heliyon

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“…Virtual reality (VR) has emerged as a promising technique in this field, providing on-demand training to trainees [7][8][9][10]. Integrating task or training procedure modules provided by simulators within a VR environment has shown successful results in some training tasks.…”

Section: Introductionmentioning

confidence: 99%

Learner engagement regulation of dual-user training based on deep reinforcement learning

Yang,

Liu,

Liu

et al. 2023

Robotica

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The dual-user training system is essential for fostering motor skill learning, particularly in complex operations. However, the challenge lies in the optimal tradeoff between trainee ability and engagement level. To address this problem, we propose an intelligent agent that coordinates trainees’ control authority during real task engagement to ensure task safety during training. Our approach avoids the need for manually set control authority by expert supervision. At the same time, it does not rely on pre-modeling the trainee’s skill development. The intelligent agent uses a deep reinforcement learning (DRL) algorithm based on trainee performance to adjust adaptive engagement during the training process. Our investigation aims to provide reasonable engagement for trainees to improve their skills while ensuring task safety. Our results demonstrate that this system can seek the policy to maximize trainee participation while guaranteeing task safety.

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Adaptive Instructional System for Complex Equipment Trainings in the Post-covid Era: Breaking the Ice of Time-Consuming Tasks

Nazarova

Butyaev

Bouaouina

et al. 2022

Lecture Notes in Computer Science

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Adaptive driving simulation-based training: framework, status, and needs

Cited by 11 publications

References 75 publications

A systematic review of major evaluation metrics for simulator-based automatic assessment of driving after stroke

A systematic review of major evaluation metrics for simulator-based automatic assessment of driving after stroke

Learner engagement regulation of dual-user training based on deep reinforcement learning

Adaptive Instructional System for Complex Equipment Trainings in the Post-covid Era: Breaking the Ice of Time-Consuming Tasks

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