Cognitive Implementation of Metaverse Embedded Learning and Training Framework for Drivers in Rolling Stock

Abstract

Public safety is prime concern in rail industry and driver training on hazard perception is crucial. Additionally, a new driver’s skill set determines the productivity and quality of existing driver training methods. Apprentice train drivers are required to complete massive hours under supervision of experienced drivers to attain the required skill sets causing productivity issues. Traditional driver training is paper based, and assessments are individually evaluated without any scientific rigor, resulting in quality challenges. This paper proposes a Metaverse embedded learning and training framework for drivers in rolling stock. The framework includes driver vision analysis by eye tracking and pupil dilation focusing on enhancing the productivity and quality of driver training and hazard detection for drivers in rolling stock. Metaverse embedded training and learning enhances experiential learning with unique benefits. In this paper, a metaverse-based training framework is proposed for train drivers to enhance productivity, quality, and safety aspects through case studies including: (i) driver sightline studies and (ii) vision analysis. The studies developed quantifying driver hazard perceptions and related comprehension rates based on eye tracking and vision studies. In conclusion, the overall savings on cost and time are 95% effective using Metaverse-based training method compared to traditional methods. Stakeholders need to supervise on driver tasks, knowledge retention, damage control due to the occurrence of hazards. The framework substantially reduced hazards to 50% with saving up to 3696 man-hours. The assessment was completely automated to provide real time assessment thus providing 93% more positive results compared to traditional methods.