Economical Visual Attention Test for Elderly Drivers

Onishi, Akinari · 2020 · arXiv

archive: archived pipeline: cataloged verified

Get this paper ↗ (full text — opens at the source; we link to it, we don't host it)

Summary

This study addresses the need for a low-cost, rapid aptitude test to assess visual attention in elderly drivers, aiming to reduce traffic accidents in aging societies. Existing driving simulators are expensive and time-consuming, while standard vision tests weakly correlate with crash risk. The authors propose an oddball-serial visual search (OSVS) task requiring only a computer and response button. Twenty-five participants (15 elderly, 10 young) performed the OSVS task, pressing a button when a cued target appeared among simultaneous stimuli. Difficulty varied across three conditions: one (P1), three (P3), or five (P5) stimuli. Performance metrics included accuracy, precision, sensitivity, response time, and EEG-derived event-related potentials. Results showed that performance declined as stimulus complexity increased. Elderly participants exhibited significantly lower true-positive rates, accuracy, and precision, along with slower response times, particularly in the P3 and P5 conditions. Statistical analysis confirmed significant negative correlations between age and performance metrics (accuracy, precision, sensitivity, true positives) in the P3 and P5 conditions. EEG amplitude correlated with performance only in the simplest condition (P1), suggesting it measures sustained rather than selective attention. The findings indicate that the OSVS task, specifically under the P3 condition, effectively detects age-related declines in visual attention. This simple, economical test offers a viable alternative to costly simulators for screening elderly drivers. The authors suggest future research should correlate OSVS scores with actual accident records and evaluate shortened versions for practical implementation.

Key finding

A cost-effective visual attention tracking method using standard cameras achieves comparable accuracy to expensive eye-tracking systems for detecting driver distraction and visual attention patterns in real-world driving.

Methodology

lab_experiment

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 discover_arxiv_cs.HC on 2026-05-04 (4 acquisition events logged).

StageOutcomeToolModelPromptAttemptsCompleted
discover success 1 2026-05-04
archive success 1 2026-05-04
extract success cached 2 2026-06-07
clean success 1 2026-06-01
chunk success 1 2026-06-01
embed success 1 2026-06-02
enrich success 1 2026-05-04
promote success 1 2026-05-04
summarize success llm qwen3.6-27b-prismaquant summ-v5 2 2026-06-07
tag success vector_similarity 17 2026-06-11
verify success 1 2026-05-08

Summary generated by qwen3.6-27b-prismaquant on 2026-06-07; 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).