Heavy vehicle driver workload assessment. Task 3, task analysis data collection
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Summary
This report details Task 3 of a National Highway Traffic Safety Administration (NHTSA) project aimed at assessing heavy vehicle driver workload to support the development of standardized evaluation protocols. The research addresses the need to define workload from the driver’s perspective, quantify the demand of various driving conditions, and establish baseline data for perceptual, motor, and cognitive loads. The study was motivated by the necessity to fill data gaps regarding how professional drivers interpret workload and how external factors influence their operational stress, particularly in the context of integrating high-technology in-cab devices. The methodology comprised five distinct data collection efforts involving professional truck drivers. First, interviews with 41 drivers explored their subjective definitions of workload. Second, a psychological scaling study using conjoint analysis involved 55 drivers who evaluated tradeoff pairs of driving conditions—varying traffic density, lighting, roadway type, visibility, and traction—to determine relative demand. Third, 30 drivers rated the safety criticality and difficulty of standard driving tasks under three distinct condition levels. Fourth, naturalistic field observations were conducted with nine drivers during revenue runs, utilizing video recording to capture visual allocation strategies and manual activity sampling to assess hand usage on the steering wheel versus in-cab tasks. Finally, a preliminary task analysis identified key features of high-technology in-cab devices, including voice communication, navigation, and text systems. Key findings revealed that drivers primarily associate workload with time stress and schedule delays rather than purely cognitive load. Traffic density and environmental factors, such as weather and construction zones, were identified as the primary contributors to high workload, while truck-specific factors were rarely cited. The conjoint analysis successfully produced a unidimensional scale of driving demand, validating that drivers perceive specific combinations of environmental factors as significantly more demanding. Field observations provided baseline metrics for visual glance allocation and manual activity, showing that common in-cab tasks could serve as reference points for future device evaluations. Drivers reported coping with high workload through increased attention, reduced speed, and increased mirror sampling. The significance of this work lies in its establishment of a foundational framework for heavy vehicle workload assessment. By linking job-level interpretations of stress with device-level interaction loads, the study provides critical baseline data for evaluating the safety and usability of advanced in-cab technologies. The derived demand scales and task difficulty ratings offer a validated method for prioritizing driving conditions and tasks in future experimental protocols. These findings underscore the importance of considering time stress and environmental variability when designing systems intended to reduce driver workload and improve safety.
Key finding
Professional truck drivers define workload primarily as time stress caused by schedule delays, and naturalistic observations of nine drivers established baseline visual allocation patterns for common in-cab tasks.
Methodology
mixed_methods
Sample size: 95
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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- Empirical Findings: physiological data
- Methodological Resource: measurement protocol
- Theoretical Contribution: theory or model