Human factors perspectives on highly automated driving

Navarro, Jordan; Gabaude, Catherine · 2020 · Crossref

DOI: 10.3917/th.834.0285

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Summary

This editorial introduces a special issue of *Le Travail humain* focused on human factors perspectives on Highly Automated Driving (HAD). The authors, Jordan Navarro and Catherine Gabaude, argue that HAD is not merely a technical interface optimization problem but a profound shift in human-technology relationships. They posit that humans and technology evolve symbiotically, a concept termed "techno-symbiosis," where advanced tools shape human cognition and behavior. The paper critiques the tendency in cognitive sciences to view technology solely as a product of human ability, advocating instead for a research domain centered on human-technology interactions. It distinguishes automation, which follows predefined sequences under human authorization, from autonomy, which operates independently. The authors emphasize that as long as humans must supervise automation, issues of trust, calibration of skills, and decision-making remain critical. The review synthesizes existing literature on HAD into five primary areas of investigation. First, studies on attention, distraction, and fatigue indicate a moderate shift in visual attention away from the road during HAD compared to manual driving. Second, research on workload and situation awareness shows that HAD generally decreases mental workload, though findings on situation awareness are contradictory, with some studies reporting deleterious effects and others improvements. Third, behavioral adaptation studies reveal that drivers exhibit smaller safety margins and struggle to resume manual control after periods of automation, becoming "out-of-the-loop." Fourth, trust in automation increases with usage duration, leading to reduced monitoring by drivers. Fifth, acceptance studies show low willingness to use HAD when supervision is required, whereas acceptance rises significantly for fully autonomous systems that do not demand human oversight. The special issue includes five contributions that expand on these themes. Lemonnier et al. provide a systematic review of acceptance determinants, highlighting the need for direct experience with autonomous vehicles. Monsaingeon et al. identify three user profiles—skeptics, compliants, and enthusiasts—based on decisions to deactivate automation under various situational factors. Ouddiz et al. report impaired visual behavior in "drivengers" (drivers acting as passengers), noting specific risks for novice drivers. Chauvin et al. classify drivers into three cognitive control modes (tactical, scrambled, opportunistic) during transitions back to manual driving. Finally, Ah-tchine and de Vries investigate pedestrian interactions with automated vehicles, examining how message content displayed by cars influences crossing decisions and safety perceptions. The authors conclude that future research must address the long-lasting effects of automation on human users, particularly as physical driving behaviors diminish. They recommend shifting toward naturalistic studies and physiological monitoring, such as gaze and brain activity, to better understand human-technology interactions. The editorial asserts that the promise of autonomous driving will not end the study of human factors; rather, it necessitates deeper investigation into how humans adapt to and are shaped by increasingly intelligent tools. This approach aims to improve both the usability of automated systems and the scientific understanding of the cognitive processes involved in human-machine cooperation.

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Topics

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• automation
• automation complacency bias
• automation surprise
• situational awareness
• trust calibration
• acceptance adoption

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• Empirical Findings: self report data
• Theoretical Contribution: conceptual framework, theory or model