Development of a Human Performance Simulation Model to Evaluate In-Vehicle Information and Control Systems in Commercial Trucking Operations

Griffin, Meredith J.; Racheru, Karthik; Johnson, Steven L.; Nam, Chang Soo · 2007 · ROSA P / Mack-Blackwell National Rural Transportation Study Center (U.S.)

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Summary

This study addresses the growing concern regarding driver distraction caused by in-vehicle information and control systems (IVIS), such as cell phones and navigation devices, in commercial trucking operations. As technology integration increases, there is a risk that secondary tasks compete with primary driving duties, leading to degraded performance and accidents. The research aims to validate a computer-simulated human performance workload model capable of evaluating the effects of primary driving tasks, secondary distractions, and active safety devices. The model is grounded in the Multiple Resource Theory, which posits that drivers have finite cognitive, visual, auditory, and response resources. The researchers developed a demand model categorizing workload into eight specific resources: visual focal, auditory focal, haptic focal, cognitive spatial, cognitive verbal, response spatial, and response verbal. A demand matrix was constructed to quantify the resource requirements of various tasks. To validate this model, an experiment was conducted using a driving simulator (STISIM) and a discrete event simulation tool (MicroSaint). Participants performed primary driving tasks involving lanekeeping and traffic avoidance under varying conditions of road curvature and traffic density. Simultaneously, they engaged in secondary distraction tasks of low, medium, and high difficulty. Data collection focused on objective performance metrics, specifically lane root mean square error (RMSE) and answer times for the distraction tasks, alongside the simulated workload values. The results demonstrated significant main effects and interactions among curvature, traffic type, and task difficulty on both driving performance and distraction task response times. Higher road curvature, complex traffic scenarios, and difficult distraction tasks all increased lane RMSE and answer times. Regression analyses revealed strong correlations between the simulated workload metrics and actual performance outcomes. Specifically, visual focal, cognitive spatial, and response spatial workloads were significant predictors of lanekeeping errors. Similarly, these workload components correlated with increased answer times for secondary tasks. The study also identified specific workload adjustments necessary to account for performance variations, confirming that the model accurately reflects the resource competition between driving and secondary tasks. The significance of this work lies in the successful validation of a simulation model that can predict driver workload and performance degradation without requiring extensive real-world testing. By quantifying how different driving conditions and IVIS designs consume limited human resources, the model provides a tool for designers and regulators to evaluate the safety of in-vehicle technologies. This approach allows for the assessment of whether specific system designs impose excessive cognitive or visual demands that could compromise safety, thereby supporting the development of safer, less distracting interfaces for commercial trucking operations.

Key finding

Increased road curvature and traffic complexity significantly degraded lane-keeping performance and increased response times, with regression analyses confirming strong correlations between calculated total workload metrics and observed performance decrements.

Methodology

simulator

Provenance

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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.

• mental demand
• workload measurement
• road complexity
• stress driving
• temporal
• visual
• time perception

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).

• Empirical Findings: behavioral performance data
• Theoretical Contribution: theory or model, computational model