Simulators, driver education and disadvantaged groups: A scoping review

Bates, Lyndel; Larue, Grégoire; Filtness, Ashleigh; Hawkins, Alana · 2019 · Crossref

DOI: 10.33492/jacrs-d-17-00244

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Summary

This scoping review investigates the role of driving simulators in driver education programs for disadvantaged populations, specifically those with intellectual impairments or ADHD, and those from indigenous ethnic groups. The research addresses two primary questions: whether simulation can serve as an effective educational intervention for individuals with individually-oriented disadvantages, and whether it can assist those disadvantaged by indigenous ethnicity. The study was motivated by the high crash rates among young drivers and specific vulnerable groups, alongside the potential of simulators to provide controlled, flexible training environments. However, existing literature lacked guidance on which skills to target, intervention duration, or how to integrate simulators into programs for these specific demographics. The authors conducted a systematic search of major databases from January 1945 to March 2017, identifying 2,420 records. After screening, 13 studies were included in the final synthesis: seven involving simulator-based interventions for individuals with disabilities (intellectual disability, ADHD, or autism spectrum disorder) and six discussing interventions for indigenous Australians. The review methodology followed established scoping review frameworks, extracting data on target populations, intervention features, simulator specifications, and outcomes. Notably, no studies were found that combined simulator use with driver education for indigenous populations. The findings reveal significant gaps and inconsistencies in the current evidence base. For individuals with disabilities, simulator interventions showed mixed results. Studies involving participants with autism spectrum disorder reported improvements in simulator tasks but lacked on-road assessment or control groups. Research on ADHD participants indicated potential benefits, such as improved hazard perception and reduced speeding in naturalistic driving data, but study designs varied widely, ranging from standalone simulator sessions to comprehensive programs including parental involvement. Crucially, there was no consistency in simulator program duration or delivery method. Furthermore, none of the six studies addressing indigenous Australians included a simulator component; instead, they focused on mentorship, community-led programs, and case management. The review highlights that while simulators can improve specific skills like hazard perception, there is limited evidence linking these improvements to actual crash reduction, and no guidance exists for tailoring these tools to indigenous learners. The significance of this review lies in its identification of a critical evidence gap in road safety policy and practice. The authors conclude that there is insufficient high-quality empirical research to inform the development of evidence-based policies for using simulators with disadvantaged groups. Specifically, the absence of simulator-based interventions for indigenous populations represents a major oversight, given their elevated crash risks. The paper calls for rigorous, evaluated research to determine which driving skills can be effectively trained via simulation for these groups and how such interventions should be structured. Without this evidence, it remains difficult to justify the implementation of simulator-based education programs for these vulnerable populations or to ensure they are culturally and individually appropriate.

Key finding

There is limited research evidence regarding the use of driving simulators for disadvantaged groups, with no identified interventions incorporating simulators for indigenous populations and inconsistent guidance on program design for those with intellectual or attentional impairments.

Methodology

review

Sample size: 13

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discover success Crossref 1 2026-06-05
archive success canonical_url 1 2026-06-06
extract success cached 3 2026-06-10
clean success clean 1 2026-06-07
chunk success chunk 1 2026-06-07
embed success embed Qwen/Qwen3-Embedding-8B 1 2026-06-07
promote success 1 2026-06-05
summarize success llm qwen3.6-27b-prismaquant summ-v5 2 2026-06-10
tag success vector_similarity 15 2026-06-11
verify success 2 2026-06-10

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