Fatigue and distraction warning system for autonomous vehicle drivers in the process of three-level autonomous driving

Sun, Haoran · 2024 · Crossref

DOI: 10.54254/2755-2721/31/20230124

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Summary

This review paper addresses the critical safety challenge of driver fatigue and distraction during Level 3 autonomous driving, where drivers must take over control in emergency situations. The motivation stems from the high incidence of traffic accidents caused by human error and the specific risk that disengaged drivers in conditionally automated vehicles may fail to respond timely to take-over requests (TOR). The paper reviews existing literature on Human-Machine Interface (HMI) warning systems designed to detect driver state and promote safe handovers, aiming to identify design feasibility, advantages, and limitations. The study analyzes system designs based on Human-Centered Design (HCD) principles, which involve literature reviews, context descriptions, and driver interviews to establish user requirements. These requirements include graduated warning hierarchies, effective information display, tamper-proof mechanisms, and minimal interference with other systems. The reviewed detection systems primarily utilize eye-tracking technology to monitor pupil area changes in real-time, supplemented by foot-mounted cameras to detect pedal movement. While eye-tracking provides physiological state data, foot cameras offer limited accuracy, necessitating combined analysis. The warning systems employ multimodal alerts—auditory, visual (HUD), and tactile (seat vibration)—that escalate in intensity based on the severity of fatigue or distraction. Evaluation of these systems involved user-based tests and checklist-based assessments. In user tests involving 14 experienced drivers in a simulator, participants found the HMI useful, correctly understood the diverse warnings, and reported that the system did not excessively interfere with other tasks or cause undue fear. However, checklist tests revealed issues such as false alarms. The review concludes that while multimodal, progressive warning systems can effectively wake drivers and facilitate the takeover process, significant gaps remain. Specifically, detection errors leading to false alarms persist, and current designs lack robust subsequent insurance measures if a driver remains unresponsive after the highest level of alert. The paper calls for further research to improve detection accuracy and develop fail-safe protocols for scenarios where drivers fail to regain control.

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Topics

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• automation
• mode awareness
• hands on hands off engagement
• automation surprise
• distraction detection algorithms
• situational awareness

Information type

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• Applied Guidance: design guidelines
• Methodological Resource: tool software
• Theoretical Contribution: conceptual framework