The Impact of Autonomous Driving Takeover Assistance Information Design on Driver Takeover Performance and Situational Awareness

Xie, Lintong; Fang Yuan, Xiao · 2024 · Crossref

DOI: 10.54941/ahfe1005232

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Summary

This study addresses the human factors challenges associated with Level 3 (L3) conditional automation, specifically focusing on how Human-Machine Interface (HMI) design for takeover assistance information impacts driver performance and situational awareness (SA). As L3 systems require drivers to resume control when the system reaches its operational boundaries, reduced situational awareness poses significant safety risks. The research identifies a gap in existing literature, which often neglects pre- and post-takeover information needs and fails to account for driving experience as a critical variable. The primary objective was to construct a takeover auxiliary information model based on Endsley’s three levels of situational awareness—perception (SA1), comprehension (SA2), and projection (SA3)—to optimize takeover safety for drivers with varying levels of experience. The methodology involved a multi-stage approach beginning with questionnaire surveys and user interviews to identify key information needs, followed by a driving simulator experiment. Twenty participants were divided into two groups: those with rich driving experience (more than five years) and those with limited experience (less than five years). The experiment simulated unplanned, system-initiated takeovers due to highway maintenance sections. Participants were exposed to three HMI information plans: Plan 1 provided only SA1 (situation awareness); Plan 2 added SA2 (scene understanding); and Plan 3 included SA1, SA2, and SA3 (behavior prediction). Performance was measured using objective metrics such as success rate, braking rate, reaction time, and adherence to recommendations, while subjective situational awareness was assessed using the Situation Awareness Global Assessment Technique (SAGAT) at pre-takeover, during-takeover, and post-takeover stages. The results demonstrated that driving experience significantly influences the effectiveness of takeover assistance information. For experienced drivers, providing SA1 and SA2 information was sufficient to achieve high takeover performance; adding SA3 (behavior prediction) did not significantly improve success rates, reaction times, or braking behavior, and some experienced drivers resisted following predictive recommendations. Conversely, inexperienced drivers benefited substantially from the inclusion of SA3. Plan 3 yielded the highest overall success rate (90%), lowest braking rate (5%), and fastest reaction time (4.207 seconds). Subjective data confirmed that SA3 significantly enhanced the situational awareness of inexperienced drivers, particularly in predicting future states, whereas it had negligible impact on experienced drivers. Based on these findings, the authors constructed a tailored takeover auxiliary information model. For experienced drivers, the HMI should prioritize SA1 and SA2 to support perception and decision-making without cognitive overload. For inexperienced drivers, the HMI must provide SA1, SA2, and SA3 to assist with perception, comprehension, and predictive decision-making. The study concludes that personalized information design strategies, which account for driving experience, are essential for optimizing takeover safety and efficiency in L3 autonomous vehicles. The research highlights the necessity of expanding information design beyond the immediate takeover moment to include pre- and post-takeover phases, providing a theoretical and practical framework for future HMI development.

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Topics

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• situational awareness
• automation
• takeover transitions
• automation surprise
• mode awareness
• odd communication

Information type

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• Methodological Resource: measurement protocol
• Theoretical Contribution: conceptual framework, theory or model