Evaluating Older Drivers’ Reaction to Forward Collision Warning (FCW) and Automated Emergency Braking (AEB) Systems Under Conditions of Distraction Using a Driving Simulator
DOI: 10.2139/ssrn.5676616
archive: archived pipeline: cataloged verified
Get this paper ↗ (DOI — opens at the source; we link to it, we don't host it)
Summary
This report documents Phase 1 of a research project evaluating how older drivers (aged 65 and older) respond to Forward Collision Warning (FCW) and Automated Emergency Braking (AEB) systems under conditions of visual distraction. The study is motivated by rising traffic fatalities and the specific vulnerabilities of older adults, who experience age-related declines in attention, visual processing, and reaction times. While Advanced Driver Assistance Systems (ADAS) like FCW and AEB can significantly reduce crash risks, older drivers often exhibit low adoption rates due to distrust, perceived complexity, and concerns about loss of control. This project aims to address these gaps by assessing behavioral effectiveness and user perceptions to inform inclusive safety technology design. The reported work focuses on preparatory activities rather than experimental data collection. The researchers conducted a comprehensive literature review identifying that FCW can reduce front-to-rear crashes by up to 27% and AEB can reduce injury crashes by up to 45%, though adoption among seniors remains hindered by usability barriers. To support future testing, the team developed pre- and post-experiment questionnaires using Qualtrics to capture demographic data, baseline ADAS attitudes, and post-simulation perceptions of trust, comfort, and reliability. An Institutional Review Board (IRB) protocol was submitted and approved, outlining ethical standards, recruitment strategies for a demographically balanced sample, and data security measures. Additionally, the team programmed interactive driving scenarios in a high-fidelity Realtime Technologies RDS-2000 simulator using SimCreatorDX® software. These scenarios simulate urban driving conditions with randomized configurations of FCW, AEB, or both, under distracted and non-distracted conditions, allowing for precise monitoring of vehicle dynamics and time-to-collision metrics. The key outcomes of this phase include the establishment of a validated experimental framework, IRB approval for human subject research, and the creation of functional simulator scenarios ready for deployment. The literature review highlighted that older drivers prefer simple, intuitive designs with customizable alerts, particularly visual and haptic feedback, over loud auditory warnings. The report concludes that Phase 2 will involve participant recruitment, pilot testing, and data collection to analyze behavioral responses and safety metrics. The findings are intended to support the development of user-centered ADAS strategies that enhance mobility and reduce crash risk for aging populations, aligning with national safety goals.
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.
| Stage | Outcome | Tool | Model | Prompt | Attempts | Completed |
|---|---|---|---|---|---|---|
| discover | success | Crossref | — | — | 1 | 2026-06-07 |
| archive | success | canonical_url | — | — | 7 | 2026-06-09 |
| extract | success | cached | — | — | 2 | 2026-06-10 |
| clean | success | clean | — | — | 1 | 2026-06-09 |
| chunk | success | chunk | — | — | 1 | 2026-06-09 |
| embed | success | embed | Qwen/Qwen3-Embedding-8B | — | 1 | 2026-06-09 |
| enrich | failed | — | — | — | 3 | 2026-07-02 |
| promote | success | — | — | — | 1 | 2026-06-07 |
| summarize | success | llm | qwen3.6-27b-prismaquant | summ-v5 | 1 | 2026-06-10 |
| tag | success | vector_similarity | — | — | 8 | 2026-06-11 |
| verify | success | — | — | — | 1 | 2026-06-10 |
Summary generated by qwen3.6-27b-prismaquant on 2026-06-10; verification: verified.
Topics
Ranked by relevance to this paper. Hover a topic for its definition.
Information type
What kind of knowledge this paper contributes, grouped by family — independent of topic (what it is about) and method (how it was studied).
- Methodological Resource: tool software, dataset resource
- Theoretical Contribution: computational model