Driver Distraction Detection Methods: A Literature Review and Framework

Kashevnik, Alexey; Shchedrin, R.A.; Kaiser, Christian; Stocker, Alexander

doi:10.1109/access.2021.3073599

Cited by 85 publications

(44 citation statements)

References 72 publications

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“…Most of the modern approaches to analyzing driver state and identifying abnormal behavior are based on machine learning [ 8 , 9 , 10 ]. There is a large body of knowledge on different machine learning approaches; however, the basic schema of applying machine learning to solve a real-world problem is refined in the MLOps field (the AI domain has some fundamentally different aspects from both software development [ 11 ] and data mining [ 12 ] and, therefore, requires its own specific process).…”

Section: Related Workmentioning

confidence: 99%

Threats Detection during Human-Computer Interaction in Driver Monitoring Systems

Kashevnik

Ponomarev

Shilov

et al. 2022

Sensors

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This paper presents an approach and a case study for threat detection during human–computer interaction, using the example of driver–vehicle interaction. We analyzed a driver monitoring system and identified two types of users: the driver and the operator. The proposed approach detects possible threats for the driver. We present a method for threat detection during human–system interactions that generalizes potential threats, as well as approaches for their detection. The originality of the method is that we frame the problem of threat detection in a holistic way: we build on the driver–ITS system analysis and generalize existing methods for driver state analysis into a threat detection method covering the identified threats. The developed reference model of the operator–computer interaction interface shows how the driver monitoring process is organized, and what information can be processed automatically, and what information related to the driver behavior has to be processed manually. In addition, the interface reference model includes mechanisms for operator behavior monitoring. We present experiments that included 14 drivers, as a case study. The experiments illustrated how the operator monitors and processes the information from the driver monitoring system. Based on the case study, we clarified that when the driver monitoring system detected the threats in the cabin and notified drivers about them, the number of threats was significantly decreased.

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Section: Related Workmentioning

confidence: 99%

Threats Detection during Human-Computer Interaction in Driver Monitoring Systems

Kashevnik

Ponomarev

Shilov

et al. 2022

Sensors

Self Cite

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“…• Driver behavior tracking that, for example, aimed at detection of the driver's drowsiness [77] and distraction [78], unfastened belt [79], etc. ; • Road situation tracking that, for example, aims at detection of road accidents [80], technical works [81], specific weather conditions [82], etc.…”

Section: Related Workmentioning

confidence: 99%

Security and Privacy Analysis of Smartphone-Based Driver Monitoring Systems from the Developer’s Point of View

Levshun

Chechulin

Kotenko

2022

Sensors

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Nowadays, the whole driver monitoring system can be placed inside the vehicle driver’s smartphone, which introduces new security and privacy risks to the system. Because of the nature of the modern transportation systems, the consequences of the security issues in such systems can be crucial, leading to threat to human life and health. Moreover, despite the large number of security and privacy issues discovered in smartphone applications on a daily basis, there is no general approach for their automated analysis that can work in conditions that lack data and take into account specifics of the application area. Thus, this paper describes an original approach for a security and privacy analysis of driver monitoring systems based on smartphone sensors. This analysis uses white-box testing principles and aims to help developers evaluate and improve their products. The novelty of the proposed approach lies in combining various security and privacy analysis algorithms into a single automated approach for a specific area of application. Moreover, the suggested approach is modular and extensible, takes into account specific features of smartphone-based driver monitoring systems and works in conditions of lack or inaccessibility of data. The practical significance of the approach lies in the suggestions that are provided based on the conducted analysis. Those suggestions contain detected security and privacy issues and ways of their mitigation, together with limitations of the analysis due to the absence of data. It is assumed that such an approach would help developers take into account important aspects of security and privacy, thus reducing related issues in the developed products. An experimental evaluation of the approach is conducted on a car driver monitoring use case. In addition, the advantages and disadvantages of the proposed approach as well as future work directions are indicated.

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“…Now, the focus is shifted to research that deals with the driver's voice. For instance, smartphones' recent operating systems implement a feature to reduce the driver distraction and increase his focus on the road [19], [20]. One of the latest operating systems includes a Do Not Disturb While Driving mode (DND) that can block notifications of incoming calls and texts when the iPhone senses driving motion or is connected to a car via Bluetooth.…”

Section: Related Workmentioning

confidence: 99%

“…Furthermore, a summary of the proposed work is discussed. Different studies surveyed the effect of driver inattention and driver distraction as one of the main causes of increased road crashes [1], [2]. Approximately 1.35 million people die each year because of road traffic accidents [3].…”

Section: Introductionmentioning

confidence: 99%

Multimodel System for Driver Distraction Detection and Elimination

et al. 2022

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On average 3,700 people lose their lives on roads every day due to car accidents as a result of drivers' distraction. In this research, a proposed hybrid approach is presented. The approach is based on deep learning to detect the driver's actions and eliminate the driver's distraction as a packed solution. The detection is performed by analyzing the driver's actions and his head pose. The elimination is made by using voice commands that are based on trigger words, speech to text, and text classification models to access the car's functions such as the air-condition, radio, etc. The results of the driver's actions classification showed 94.1% accuracy on the AUC benchmark database for driver distraction achieving the state-of-the-art accuracy on this benchmark. The results of the command to text classification is 95.19% while the results of the head pose estimation show a 6.21-degree MAE in face angles detection. With using our car commands dataset, the domain of the speech recognition output is more focused on car commands. The previously mentioned algorithms are beneficial to the safety of the driver. He can use his voice to operate the car accessories. His alert state is monitored and he is warned through an alarm if a distraction is detected. However, this research is not concerned with the detection of retinal abnormalities such as sleeping with eyes open. The results of real-time testing show 0.080 second response time for the driver's behavior classification and command following with the use of graphical processing units.

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Driver Distraction Detection Methods: A Literature Review and Framework

Cited by 85 publications

References 72 publications

Threats Detection during Human-Computer Interaction in Driver Monitoring Systems

Threats Detection during Human-Computer Interaction in Driver Monitoring Systems

Security and Privacy Analysis of Smartphone-Based Driver Monitoring Systems from the Developer’s Point of View

Multimodel System for Driver Distraction Detection and Elimination

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