Highly Automated Driving, Secondary Task Performance, and Driver State

Merat, Natasha; Jamson, A. Hamish; Lai, Frank; Carsten, Oliver · 2012 · OpenAlex-citations

DOI: 10.1177/0018720812442087

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Summary

This study investigates how highly automated driving affects driver performance and physiological state, particularly when drivers must transition from low-workload monitoring to high-workload control during unexpected incidents. As Advanced Driver Assistance Systems (ADAS) become more prevalent, drivers shift from operators to supervisors, raising concerns about situation awareness and the ability to regain control safely. The research specifically examines how sudden workload increases, induced by critical incidents and secondary tasks, impact driving behavior and blink patterns, which serve as objective measures of workload. The experiment utilized a driving simulator with fifty participants who drove in both manual and highly automated modes. The automated system handled longitudinal and lateral control, requiring drivers to monitor the environment and intervene if necessary. Workload was manipulated using a cognitive secondary task (Twenty Questions Task) and a critical incident involving a lane obstruction that required a lane change. The study employed a within-subjects design to compare performance metrics, such as speed and lane-change timing, alongside physiological data collected via eye-tracking to measure blink frequency and duration. Results indicated that without the secondary task, driver response to critical incidents was similar in both manual and automated conditions, with comparable lane-change rates and speeds. However, performance deteriorated significantly when drivers in the automated mode were distracted by the secondary task while needing to regain control. These drivers reduced speed less effectively than manual drivers and exhibited the worst overall performance. Physiological data revealed that blink frequency was generally suppressed during high visual workload but increased when attention was diverted to the cognitive secondary task. Notably, blink patterns were more consistent in manual driving, while automated driving showed higher blink rates during free driving but significant suppression during the combined high-workload scenario. The findings suggest that highly automated driving does not inherently impair driver performance if attention remains on the road. However, the combination of automation and cognitive distraction creates a dangerous state where drivers fail to respond adequately to sudden hazards. This supports the Malleable Attentional Resources Theory, indicating that automation reduces attentional resources available for unexpected events. The study concludes that keeping drivers engaged is critical for safety and highlights blink frequency as a promising, non-intrusive metric for monitoring driver workload and state in real-time.

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Topics

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• manual
• situational awareness
• automation surprise
• automation
• mode awareness
• automation complacency bias

Information type

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