Effects of non-driving related tasks during autonomous driving

Horáková, Miroslava · 2023 · OpenAlex-citations

DOI: 10.5507/dvp.2023.006

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Summary

This review paper examines the impact of non-driving related tasks (NDRTs) on driver performance during autonomous driving, specifically focusing on Level 3 (conditional automation) and Level 4 (high automation) systems. The study is motivated by the safety risks associated with "takeover performance," where drivers must resume control from the automated system. While automation aims to reduce human error, it introduces challenges regarding driver readiness, situational awareness, and the ability to safely intervene when the system reaches its operational limits. The paper synthesizes existing literature to define automation levels according to SAE standards and categorizes driving tasks into primary, secondary, and tertiary (NDRT) activities. It analyzes empirical studies regarding takeover time, quality, and safety metrics such as time-to-collision (TTC) and collision frequency. The review also explores the effects of prolonged autonomous driving on driver fatigue and alertness. Theoretical explanations for task interference are grounded in Wickens’ Multiple Resources Model (MRM), which posits that tasks competing for the same sensory, cognitive, or motor resources cause performance degradation. Key findings indicate that NDRTs negatively affect takeover performance, resulting in longer reaction times, reduced TTC, and increased collision rates. The severity of this impact depends on the task’s characteristics. Visual, cognitively demanding, and manual tasks exert the greatest negative influence. Specifically, tasks requiring the driver to hold an object (e.g., a smartphone) significantly delay takeover compared to tasks using integrated vehicle systems, due to the additional time required to stow the object and reposition hands. Furthermore, prolonged autonomous driving (exceeding 15–20 minutes) leads to passive fatigue and degraded cognitive performance, worsening takeover outcomes. Auditory tasks generally yield better performance than visual-manual tasks because they do not compete for visual resources. The significance of this research lies in its implications for the design of human-machine interfaces and automation strategies. The findings suggest that a minimum takeover time of 10–15 seconds is necessary for drivers to achieve adequate situational awareness and safe control, particularly when engaged in distracting activities. The paper highlights the limitations of current automation levels, where drivers are neither fully engaged nor fully disengaged. It concludes that understanding resource competition through frameworks like MRM is essential for predicting driver behavior and developing systems that mitigate the risks associated with task switching and prolonged automation use.

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Topics

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• manual
• automation
• takeover transitions
• automation surprise
• visual manual

Information type

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