How Do Non-driving-related Tasks Affect Engagement Under Highly Automated Driving Situations? A Literature Review

Jaussein, Marie; Lévêque, Lucie; Deniel, Jonathan; Bellet, Thierry; Tattegrain, Hélène; Marin-Lamellet, Claude · 2021 · Crossref

DOI: 10.3389/ffutr.2021.687602

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Summary

This literature review addresses the impact of non-driving-related tasks (NDRTs) on driver engagement during highly automated driving (HAD), specifically focusing on control transitions where drivers must resume manual control. The study is motivated by the impending deployment of SAE Level 3 and 4 automated vehicles, which allow drivers to act as passengers. While previous research often viewed NDRTs merely as distractions, recent perspectives suggest they might help maintain cognitive readiness. However, findings on whether NDRTs improve or degrade takeover performance are conflicting. The authors aim to synthesize existing literature to clarify how task engagement is investigated, moving beyond techno-centric approaches that focus on system states toward a cognitive-centred view that accounts for the evolution of driver engagement mechanisms during transition stages. The authors conducted a systematic review of studies published between 2010 and 2020, sourced via Google Scholar and reference lists from prior reviews. Inclusion criteria required studies to explicitly mention attention, workload, or engagement; provide detailed NDRT procedures; use ecological tasks; and involve SAE Level 3 or 4 automation. After screening and removing duplicates and incompatible entries, 23 studies were analyzed. The review categorizes these studies based on whether engagement was manipulated as an independent variable (IV) or measured as a dependent variable (DV). For IV studies, the authors further distinguish between "task-driven" approaches, which manipulate engagement by varying task type or difficulty, and "cognition-driven" approaches, which use cognitive theories to design engagement levels. The review identifies two primary ways engagement is utilized in the literature. When manipulated as an IV, studies often employ task-driven methods, such as varying task difficulty (e.g., reading vs. video watching) or modality (auditory vs. visual), to induce different engagement states. For instance, some studies found that higher engagement in NDRTs led to faster reaction times in younger drivers but poorer steering stability in older drivers. Cognition-driven approaches utilize frameworks like threaded cognition theory to manipulate cognitive dimensions. When engagement is measured as a DV, researchers assess it through task-related performance metrics (e.g., reaction time, gaze behavior) or subjective ratings (e.g., NASA-TLX workload, Karolinska Sleepiness Scale). The analysis reveals that while some studies report no significant impact of NDRT type on takeover performance, others indicate that specific engagement levels affect response times and situation awareness. The significance of this review lies in its proposal for a cognitive-centred framework for studying control transitions. By categorizing engagement mechanisms and variables across different transition stages, the authors provide a structured basis for future research. This approach addresses the gap in understanding how drivers disengage from NDRTs and re-engage with driving tasks, offering insights crucial for designing safe human-machine interfaces in automated vehicles. The review underscores the need to consider driver cognitive states, rather than just system parameters, to optimize takeover requests and ensure safety during the transition from automated to manual control.

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Topics

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• takeover transitions
• temporal
• manual
• automation
• hands on hands off engagement
• visual manual

Information type

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