Multiple Resource Modeling of Task Interference in Vehicle Control, Hazard Awareness and In-vehicle Task Performance

Horrey, William J.; Wickens, John D · 2003 · Unknown

DOI: 10.17077/drivingassessment.1082

archive: archived pipeline: cataloged verified

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Summary

This paper presents and validates a computational model designed to predict task interference in driving scenarios, specifically addressing how concurrent in-vehicle tasks degrade performance in vehicle control and hazard awareness. The research is motivated by the need for designers to assess the safety impact of in-vehicle technologies (IVT) without relying solely on time-consuming driver-in-the-loop simulations. Grounded in multiple resource theories, the model posits that performance decrements arise from competition over limited cognitive resources defined by four dimensions: processing stage (perception, cognition, response), processing code (verbal, spatial), input modality (visual, auditory), and visual channel (focal, ambient). The computational model operates through a structured algorithm. First, each task is assigned a "demand vector" based on its reliance on specific resources, with values increasing with task difficulty. These vectors are summed to create a "demand scalar" representing total difficulty. Second, a "conflict matrix" quantifies the degree of resource overlap between concurrent tasks, assigning higher conflict values to tasks sharing identical resources (e.g., two visual-focal tasks). The total interference score is derived by combining the total demand score and the resource-conflict score. This score predicts the magnitude of performance degradation, which can be apportioned to primary or secondary tasks based on prioritization strategies. To validate the model, the authors analyzed data from a simulated driving study involving 25 drivers. Participants performed driving tasks of varying complexity (urban, rural straight, rural curved) while engaging in secondary IVT tasks presented via head-up displays (HUD), head-down displays (HDD), or auditory channels. The model’s predicted interference scores were compared against actual performance decrements in lane keeping, IVT response times, and hazard response times. The results demonstrated strong predictive validity for specific metrics: the model explained 85% of the variance in secondary task latency and 98% of the variance in hazard response times. However, it failed to predict variance in lane keeping ($R^2 = 0.02$). The authors attribute this lack of fit to driver prioritization strategies, where participants protected continuous vehicle control at the expense of secondary task performance and hazard detection. The study concludes that the computational model is a robust, theory-based tool for predicting relative task interference and identifying which aspects of driving are most vulnerable to IVT distraction. While the model requires expertise to establish demand vectors and does not output absolute performance losses, it effectively highlights safety risks, particularly in hazard detection. The findings suggest that while drivers may maintain lane stability through resource prioritization, they remain susceptible to significant delays in responding to critical road hazards when engaged in visually demanding secondary tasks. This approach offers a practical method for evaluating the ergonomic safety of future automotive interfaces.

Key finding

The computational multiple resource model accurately predicted performance decrements in secondary task response times and hazard detection but failed to predict lane-keeping interference, indicating that drivers prioritize vehicle control over other tasks.

Methodology

simulation_modeling

Sample size: 25

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 author_sweep_intake on 2026-05-28.

Summary generated by qwen3.6-27b-prismaquant on 2026-06-10; verification: verified.

Topics

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• dual task performance
• mental demand
• manual
• auditory
• attention
• dual task multitasking

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).

• Applied Guidance: design guidelines
• Theoretical Contribution: theory or model, computational model