Driver Adaptive Task Allocation: A Field Driving Study

Lei, S.; Toriizuka, Takashi; Roetting, Mattias · 2017 · Crossref

DOI: 10.3917/th.801.0093

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Summary

This study investigates the feasibility of Adaptive Task Allocation (ATA) in a real-world field driving context, aiming to optimize human-machine interaction by dynamically adjusting secondary task loads based on the driver’s mental workload. Motivated by the need for human-centered adaptive systems that prevent cognitive overload or underload, the research tests whether real-time monitoring of psychophysiological signals can effectively regulate workload to maintain optimal performance levels. The authors build upon previous theoretical models, specifically the Demand-Workload-Matched Adaptive Task Allocation (DWM-ATA) model, which suggests that task allocation should respond not just to task demand but to the actual experienced workload. The experimental design comprised two stages: a pre-study with nine participants and a main study with twelve participants, all licensed drivers. The primary task involved driving a vehicle through four conditions of increasing complexity, ranging from stationary sitting to complex figure-eight maneuvers at a constant speed of 20 km/h. The secondary task was a modified 1-back working memory task displayed on a touchscreen, with difficulty manipulated by varying the number of letters (smiley, 1, 2, or 3 letters) and their spatial positions. Mental workload was assessed using electroencephalogram (EEG) data, specifically frontal theta and parietal alpha power, processed via a Logistic Function Model (LFM) to generate a continuous workload index. In the main study, participants performed tasks under two conditions: "Static," where secondary task difficulty was randomly distributed, and "ATA," where the secondary task load was dynamically adjusted every second based on the real-time EEG workload index to keep workload within a moderate range. The pre-study validated the assumption that increased driving complexity and working memory load significantly elevated subjective workload (measured via NASA-TLX) and degraded secondary task accuracy, confirming the sensitivity of the EEG-based workload index. In the main study, the results indicated that the ATA system successfully maintained the driver’s mental workload at a moderate level, preventing extreme fluctuations observed in the static condition. However, despite this stabilization of workload, the study found no significant improvements in overall task performance, including driving accuracy or secondary task reaction times and accuracy, when comparing the ATA condition to the static condition. The significance of this research lies in demonstrating the technical feasibility of implementing closed-loop, EEG-driven adaptive task allocation in a field driving environment. While the system effectively regulated the driver’s physiological state of mental workload, the lack of corresponding performance gains suggests that maintaining moderate workload alone may not be sufficient to enhance operational efficiency in this specific context. The findings highlight the complexity of human-machine interaction, indicating that while ATA can mitigate workload extremes, its impact on performance metrics requires further investigation, potentially considering additional factors such as strategy, motivation, or the specific nature of the dissociation between task demand and experienced workload.

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Topics

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• mental demand
• workload measurement
• temporal
• stress driving
• dual task performance
• cognitive capacity variation

Information type

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