In-Vehicle Information Systems Behavioral Model and Design Support: Final Report
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Summary
This report summarizes the development of a behavioral model and prototype software, IVIS DEMAnD, designed to evaluate the attention demands of In-Vehicle Information Systems (IVIS). The research was motivated by the need to ensure that IVIS designs adhere to human factors principles, minimizing negative impacts on driving performance while enhancing mobility and safety. The core objective was to provide designers with tools to predict how secondary tasks divert resources from the primary task of driving. The project employed a five-component resource model, viewing the driver as a pool of resources: visual, auditory, manual, speech, and supplemental information processing (SIP). SIP was identified as a critical but data-scarce component, representing complex cognitive processing beyond simple information extraction. To address this deficiency, the researchers conducted four experiments. Experiment 1 assessed visual and SIP demands in passenger vehicles using an instrumented sedan on U.S. Highway 460, varying task complexity, format, and information density among 36 drivers. Experiment 2 focused on commercial vehicle operators, while Experiment 3 investigated auditory and SIP demands. Experiment 4, conducted by a subcontractor, examined visual, auditory, and SIP resources in an urban environment. These studies utilized on-road data collection, including eye-tracking, vehicle dynamics sensors, and performance metrics. The findings established specific safety criteria, operationalized as "yellow-line" and "red-line" thresholds. The yellow-line indicated significantly negative effects on driving performance compared to baseline, while the red-line indicated exceedance of safety parameters based on expert opinion and prior research. The study provided descriptive data on the proportion of drivers exceeding these thresholds for various IVIS tasks. The results were integrated into the IVIS DEMAnD prototype software, which includes a task library, anthropometry tools, and a "Figure of Demand" metric to quantify resource diversion. The software allows designers to model tasks, interpolate demand measures, and generate summary reports to evaluate design alternatives. The significance of this work lies in providing a structured, evidence-based tool for IVIS design evaluation. By quantifying the draw on driver resources, particularly the under-researched SIP component, the model helps assess relative risk and identify hazardous design features. The report concludes with recommendations for future research, including updating the prototype to a first-release program and further validating the model against additional data sources. This framework supports the development of safer, more efficient intelligent transportation systems by ensuring that secondary tasks do not critically impair the primary task of driving.
Key finding
The study developed a five-component behavioral model and prototype software that quantifies driver resource draw from visual, auditory, supplemental information processing, manual, and speech pools to predict driving performance decrements associated with in-vehicle information systems.
Methodology
mixed_methods
Sample size: 36
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 (6 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 | — | — | 2 | 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 | 3 | 2026-06-10 |
| tag | success | vector_similarity | — | — | 19 | 2026-06-11 |
| verify | success | — | — | — | 2 | 2026-06-10 |
Summary generated by qwen3.6-27b-prismaquant on 2026-06-10; verification: verified.
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- Applied Guidance: design guidelines
- Methodological Resource: measurement protocol
- Theoretical Contribution: theory or model