Task-Difficulty Homeostasis in Car Following Models: Experimental Validation Using Self-Paced Visual Occlusion

Pekkanen, Jami; Lappi, Otto; Itkonen, Teemu H.; Summala, Heikki · 2017 · Crossref

DOI: 10.1371/journal.pone.0169704

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Summary

This study experimentally validates the Task-Capability Interface (TCI) model within car following (CF) simulations, addressing a critical gap in traffic engineering where human factors are often overlooked. Traditional CF models fail to account for how drivers dynamically adjust behavior based on capability and task difficulty. The authors specifically test assumptions from recent TCI-augmented models by Hoogendoorn et al. and Saifuzzaman et al., which propose that drivers maintain a preferred level of task difficulty by adjusting parameters like time headway in response to changes in capability, such as distraction. To operationalize these concepts, the researchers conducted a driving simulator experiment with 18 participants. The study utilized a self-paced visual occlusion paradigm to simulate "eyes-off-the-road" distraction. Participants performed a car following task where their view of the lead vehicle was masked by a black rectangle. They could briefly remove the occlusion for 300 milliseconds by pressing a lever, effectively controlling their visual sampling rate. The primary metrics were time headway (task demand) and occlusion duration (inverse proxy for visual capability/distraction). The experiment compared unoccluded baseline driving with occluded conditions, analyzing both aggregate long-term averages and instantaneous short-term adjustments. The results demonstrated a strong, approximately one-to-one linear correspondence between increased occlusion duration and increased time headway. At the aggregate level, the data supported a "baseline independent" relationship, where the increase in time headway was directly proportional to the average occlusion duration, explaining 84% of the variance. This finding aligns with the formulation proposed by Saifuzzaman et al. rather than the "baseline relative" model of Hoogendoorn et al. Furthermore, analysis at the individual sample level revealed that drivers adapt their visual sampling on a short timescale (seconds). When time headway transiently increased, drivers allowed longer occlusion durations, and vice versa, indicating a dynamic feedback loop where visual sampling adjusts to immediate safety margins. These findings provide quantitative evidence for incorporating human factors into car following models. The study confirms that drivers actively regulate task difficulty through compensatory behaviors, specifically by increasing safety margins (time headway) when visual capability is reduced. The identified linear relationship offers a concrete operationalization for modeling driver capability in traffic simulations. Additionally, the short-timescale adaptation suggests that current models lack mechanisms for real-time visual sampling adjustments, highlighting an area for future refinement in both theoretical traffic psychology and engineering applications.

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Topics

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• visual occlusion
• following distance
• temporal
• visual
• situational awareness
• traffic density

Information type

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• Empirical Findings: behavioral performance data
• Methodological Resource: measurement protocol
• Theoretical Contribution: theory or model