The Effects of Multi-modal Takeover Request on Distracted Drivers’ Takeover Performance and Perception

Yang, Ye; Deng, Mengying; Zhou, Yaohua; Shi, Mingjiang; Gao, Ruixuan · 2022 · Crossref

DOI: 10.54941/ahfe1002430

archive: archived pipeline: cataloged verified

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Summary

This study addresses the safety hazards associated with visual and cognitive distractions in conditionally automated driving, specifically focusing on the effectiveness of Takeover Requests (TOR). Current Human-Machine Interface (HMI) designs, which typically rely on visual symbols and auditory warnings, often fail to capture the attention of drivers engaged in non-driving related tasks. To optimize TOR design, the researchers investigated how different multi-modal combinations affect driver reaction time, reaction quality, and subjective perception under distracted conditions. The experiment utilized a static driving simulator with 18 licensed drivers who performed an N-back memory task to induce cognitive and visual distraction. The study compared four TOR conditions: a control group using symbol and speech (C0), and three experimental groups combining speech with ambient light (E1), vibration (E2), or both light and vibration (E3). Speech was kept constant across all conditions. Performance metrics included reaction time and quality (steering and braking dynamics), while subjective perception was measured using the In-Car Warning Perception Scale (ICWPS), assessing dimensions such as urgency, perceptiveness, and supportiveness. Results indicated that the vibration and speech combination (E2) elicited the fastest average reaction time (3.05 seconds), significantly outperforming the control group (4.48 seconds). However, this combination received negative feedback regarding user experience, with participants reporting high stress and lower scores for supportiveness. The symbol and speech control (C0) was the slowest and hardest to perceive but was rated highest for simplicity and understandability. The ambient light and speech combination (E1) was perceived as guiding rather than urgent, making it suitable for non-critical alerts. The triple-modal combination of light, vibration, and speech (E3) achieved the best overall balance, yielding the highest scores for supportiveness, urgency, and perceptiveness, while mitigating the stress associated with vibration alone. The findings suggest that multi-modal TOR designs significantly improve takeover performance and user experience compared to traditional visual-auditory alerts. Specifically, incorporating ambient light and haptic feedback helps overcome the limitations of focal vision during distraction. The study concludes that while vibration accelerates reaction times, it should be paired with calming modalities like ambient light to prevent negative emotional responses. These insights provide actionable guidelines for designing safer, more effective HMI systems for automated vehicles, emphasizing the need to test specific multi-modal combinations rather than assuming additive benefits.

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Topics

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• haptic feedback
• takeover transitions
• visual manual
• feedback modes
• multimodal feedback
• visual

Information type

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• Empirical Findings: behavioral performance data
• Methodological Resource: measurement protocol
• Theoretical Contribution: conceptual framework