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Blueprint for a simulation framework to increase driver training safety in north America: Case study

  • SUNY Buffalo
  • Gilbane Building Company
  • Buffalo Public Schools
  • Boeing
  • Honda Motor Co., Ltd.
  • Adient
  • General Electric
  • Atlas Copco Group

Research output: Contribution to journalArticlepeer-review

3 Scopus citations

Abstract

Despite numerous recent advances in the classroom and in-vehicle driver training and education over the last quarter-century, traffic accidents remain a leading cause of mortality for young adults—particularly, those between the ages of 16 and 19. Obviously, despite recent advances in conventional driver training (e.g., classroom, in-vehicle, Graduated Driver Licensing programs), this remains a critical public safety and public health concern. As advanced vehicle technologies continue to evolve, so too does the unintended potential for mechanical, visual, and/or cognitive driver distraction and adverse safety events on national highways. For these reasons, a physics-based modeling and high-fidelity simulation have great potential to serve as a critical supplementary component of a near-future teen-driver training framework. Here, a case study is presented that examines the specification, development, and deployment of a “blueprint” for a simulation framework intended to increase driver training safety in North America. A multi-measure assessment of simulated driver performance was developed and instituted, including quantitative (e.g., simulator-measured), qualitative (e.g., evaluator-observed), and self-report metrics. Preliminary findings are presented, along with a summary of novel contributions through the deployment of the training framework, as well as planned improvements and suggestions for future directions.

Original languageEnglish
Article number24
JournalSafety
Volume7
Issue number2
DOIs
StatePublished - Apr 1 2021

Keywords

  • Driver education
  • Driver training
  • Driving simulation
  • Human factors
  • Modeling and simulation (M&S)
  • Physics-based modeling
  • Public health
  • Safety

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