Driver Distraction Detection Methods: A Literature Review and Framework

Kashevnik, Alexey; Shchedrin, R.A.; Kaiser, Christian; Stocker, Alexander · 2021 · OpenAlex-citations

DOI: 10.1109/access.2021.3073599

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Summary

This paper addresses the critical safety challenge of driver distraction and inattention, which are primary causes of road accidents and fatalities. The authors argue that despite the rapid advancement of automated driving technologies, human drivers will remain essential supervisors for the foreseeable future, particularly in Level 3 automation where responsibility shifts between the driver and the system. Consequently, robust systems to detect driver distraction are necessary to ensure drivers remain "in the loop" and can safely take control when required. The study aims to synthesize existing scientific literature on distraction detection methods and integrate them into a holistic framework to guide future system development. The authors conducted a systematic literature review following established scientific rigor guidelines. They searched academic databases using keywords such as "driver distraction," "driver monitoring," and "distraction detection," covering disciplines including computer science, psychology, and transportation. The review focused on methods for detecting three primary types of distraction: manual (hands/feet off controls), visual (eyes off the road), and cognitive (mind off driving). The authors categorized the reviewed methods into two classes: those used in laboratory environments to measure distraction levels and those designed for real-world in-vehicle detection. The results are structured into a comprehensive detection framework that visualizes the information chain from sensor data to inferred distraction types. The review identifies specific evaluation techniques, including the box task method, lane-change tests, occlusion techniques, and Detection-Response Tasks (DRT), which assess cognitive load and visual demand in simulated settings. For real-world application, the paper details non-intrusive methods utilizing computer vision to monitor eye gaze, head movements, and hand positions, as well as intrusive methods analyzing driving style parameters like lateral acceleration and speed. The framework highlights how different sensors and data processing steps contribute to detecting specific distraction behaviors, noting that many modern approaches rely on fast-growing computer vision technologies. The significance of this work lies in its provision of a structured, holistic framework that maps the entire spectrum of distraction detection methods. By explicitly linking sensors, measured data, and inferred behaviors, the paper offers a valuable resource for researchers and developers aiming to create high-quality detection systems. The authors emphasize that combining different detection approaches can improve accuracy and reliability. This framework supports the development of safety systems that are crucial for the safe integration of automated driving features, ensuring that human supervisors maintain adequate attention levels during critical handover scenarios.

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Topics

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• distraction detection algorithms
• visual
• external distraction
• cognitive
• manual
• situational awareness

Information type

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• Methodological Resource: tool software
• Theoretical Contribution: conceptual framework, theory or model