Driver Training Research and Guidelines for Automated Vehicle Technology [supporting datasets]
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Summary
This document serves as a metadata record and dataset description for the study "Driver Training Research and Guidelines for Automated Vehicle Technology," conducted under the Safety through Disruption (Safe-D) University Transportation Center. The research was motivated by the need to develop training protocol guidelines for automated vehicle trainers. The primary objective was to optimize the use of automated vehicle systems and, consequently, enhance transportation safety. The study specifically aimed to evaluate the effectiveness of different training protocols on driving performance. The experimental design involved 30 older adult participants who were randomly assigned to one of three groups: a control group receiving no training, a group receiving video-based training, and a group receiving demonstration-based training. Following the training interventions, participants completed three drives, each consisting of eight segments. These segments alternated between manual driving conditions, where the automated vehicle (AV) system could not be activated, and automated driving conditions, where the AV system was active. The study focused on Level 2 automation features, specifically adaptive cruise control and lane keep assist systems. The research was conducted at the Texas A&M Transportation Institute, with contributions from researchers at Virginia Tech Transportation Institute and San Diego State University. The provided text functions primarily as a repository record rather than a full research report, detailing the availability of the supporting dataset rather than presenting specific statistical results or findings. The dataset, titled "Driver Training for Automated Vehicle Technology (01-004)," is preserved by the Virginia Tech Transportation Institute (VTTI) and includes an Excel file containing the final dataset and a PDF design specification. The metadata indicates that the study evaluated the overall effectiveness of the two training protocols compared to the control group, but the specific outcomes regarding driving performance metrics, such as workload or error rates, are not detailed in this text. The dataset is intended to support the final report available through the National Transportation Library’s Digital Repository. The significance of this work lies in its contribution to the development of standardized training guidelines for automated vehicles. By evaluating training methods for older adults, the study addresses a critical demographic in the adoption of Level 2 automation technologies. The availability of the dataset allows for further analysis of how video-based and demonstration-based training impacts driver behavior and safety in mixed manual-automated driving scenarios. This research supports the broader goal of optimizing transportation safety through improved human-machine interaction protocols.
Sample size: 30
Provenance
The full processing record for this entry. Every stage of this paper's journey through the pipeline is logged — what ran, with which tool and model, how many attempts it took, and when it last completed. Discovered via bulk_ingest_rosap on 2026-05-23 (7 acquisition events logged).
| Stage | Outcome | Tool | Model | Prompt | Attempts | Completed |
|---|---|---|---|---|---|---|
| discover | success | rosap | — | — | 2 | 2026-05-23 |
| archive | success | — | — | — | 1 | 2026-05-23 |
| extract | success | cached | — | — | 3 | 2026-06-10 |
| clean | success | — | — | — | 1 | 2026-06-01 |
| chunk | success | — | — | — | 1 | 2026-06-01 |
| embed | success | — | — | — | 1 | 2026-06-02 |
| enrich | success | — | — | — | 1 | 2026-05-23 |
| promote | success | — | — | — | 1 | 2026-05-23 |
| summarize | success | llm | qwen3.6-27b-prismaquant | summ-v5 | 4 | 2026-06-10 |
| tag | success | vector_similarity | — | — | 19 | 2026-06-11 |
| verify | success | — | — | — | 3 | 2026-06-10 |
Summary generated by qwen3.6-27b-prismaquant on 2026-06-10; verification: verified.
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- Methodological Resource: dataset resource, tool software
- Theoretical Contribution: computational model