Survey and Synthesis of State of the Art in Driver Monitoring

Halin, Anaïs; Verly, Jacques G.; Van Droogenbroeck, Marc · 2021 · OpenAlex-citations

DOI: 10.3390/s21165558

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Summary

This paper addresses the critical need for Driver Monitoring (DM) systems to mitigate road accidents, which are primarily caused by human error. The authors focus on the first step of DM: characterizing the driver’s state. They argue that DM remains essential for all vehicles except fully autonomous ones (SAE Level 5), as it must interact synergistically with Driving Automation (DA) across SAE Levels 0–4. The study aims to provide a structured, comprehensive synthesis of the state-of-the-art in DM characterization techniques, offering a clear view of how DM roles evolve with increasing automation levels. The authors conducted a systematic literature survey using databases including IEEE, ScienceDirect, Sensors, and ResearchGate. Using specific Boolean queries, they identified 154 initial items, removed duplicates, and manually screened the results to retain 56 relevant scientific publications. These references were analyzed to identify key driver (sub)states, indicators, and sensors. The paper categorizes driver states into five primary dimensions: drowsiness, mental workload, distraction, emotions, and being under the influence. It further details the interaction between DM and DA, noting that at SAE Levels 0–2, continuous monitoring is required, while at Levels 3–4, monitoring ensures the driver is "fallback-ready" to take control when automation requests it or reaches its operational limits. The core finding is a synthesized, polychotomous framework presented through interlocked tables that map the five driver (sub)states to their specific indicators and the sensors capable of measuring them. The analysis reveals that indicators fall into three categories: physiological (e.g., heart rate, brain activity), behavioral (e.g., gaze, blink rate, facial expressions), and subjective. Sensors include driver-facing cameras, microphones, wearable devices, and vehicle-based sensors like steering wheel torque or lane deviation metrics. The survey highlights that while vehicle-related indicators were historically common, they become less reliable as Driver Support features automate vehicle control, making direct physiological and behavioral monitoring increasingly necessary. The significance of this work lies in its provision of a unified, structured overview for researchers, equipment providers, and manufacturers. By clearly delineating the relationship between driver states, indicators, and sensors, the paper identifies current implementation options and highlights fruitful areas for future research. It emphasizes that as vehicles move toward higher automation levels, DM systems must shift from monitoring driving performance to assessing the driver’s readiness to intervene, ensuring safety during transitions between automated and manual control.

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Topics

Ranked by relevance to this paper. Hover a topic for its definition.

• situational awareness
• dms regulation
• dms validation
• mode awareness
• drowsiness
• drowsiness detection algorithms

Information type

What kind of knowledge this paper contributes, grouped by family — independent of topic (what it is about) and method (how it was studied).

• Empirical Findings: physiological data
• Methodological Resource: tool software
• Theoretical Contribution: conceptual framework