Developing a multi sensors system to detect sleepiness to drivers from transport systems

Dumitrescu, Cătălin; Costea, Ilona Mădălina; Nemtanu, Florin Codrut; Badescu, I.; Banica, Andrei

doi:10.1109/siitme.2016.7777271

Cited by 4 publications

(2 citation statements)

References 6 publications

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“…The PSD algorithm analysis procedures are simple and ready for real-time processing, which makes it one of the most common EEG-based driver state feature extraction techniques [88]. In addition, numerous studies illustrate how to tune parameters such as the time window and overlap percentage to improve extraction efficiency [89][90][91]. However, conventional PSD methods are not suitable for short data segments and sharp variations in the spectra [92,93].…”

Section: B)mentioning

confidence: 99%

A Survey and Tutorial of EEG-Based Brain Monitoring for Driver State Analysis

Zhang

Eskandarian

2021

IEEE/CAA J. Autom. Sinica

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Drivers' cognitive and physiological states affect their ability to control their vehicles. Thus, these driver states are important to the safety of automobiles. The design of advanced driver assistance systems (ADAS) or autonomous vehicles will depend on their ability to interact effectively with the driver. A deeper understanding of the driver state is, therefore, paramount. EEG is proven to be one of the most effective methods for driver state monitoring and human error detection. This paper discusses EEG-based driver state detection systems and their corresponding analysis algorithms over the last three decades. First, the commonly used EEG system setup for driver state studies is introduced. Then, the EEG signal preprocessing, feature extraction, and classification algorithms for driver state detection are reviewed. Finally, EEG-based driver state monitoring research is reviewed indepth, and its future development is discussed. It is concluded that the current EEG-based driver state monitoring algorithms are promising for safety applications. However, many improvements are still required in EEG artifact reduction, realtime processing, and between-subject classification accuracy.

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Section: B)mentioning

confidence: 99%

A Survey and Tutorial of EEG-Based Brain Monitoring for Driver State Analysis

Zhang

Eskandarian

2021

IEEE/CAA J. Autom. Sinica

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“…Nevertheless, the fast development of a technology embraced by all the major players in the automotive industry has resulted in recent years in the presentation of accurate and reliable DMSs. There are several studies describing the development of new methods and technology to improve the accuracy of driver distraction and drowsiness detection, exploring facial image processing [23][24][25][26][27][28], heart rate and brain activity [26,29], and, more recently, dynamic driving parameters and infrastructure information [27,28]. However, few studies have been dedicated to explore DMS data from the perspective of driver inattention and its consequences for road safety [2,30].…”

Section: Introductionmentioning

confidence: 99%

Exploring Monitoring Systems Data for Driver Distraction and Drowsiness Research

Lobo

Ferreira

Couto

2020

Sensors

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Driver inattention is a major contributor to road crashes. The emerging of new driver monitoring systems represents an opportunity for researchers to explore new data sources to understand driver inattention, even if the technology was not developed with this purpose in mind. This study is based on retrospective data obtained from two driver monitoring systems to study distraction and drowsiness risk factors. The data includes information about the trips performed by 330 drivers and corresponding distraction and drowsiness alerts emitted by the systems. The drivers’ historical travel data allowed defining two groups with different mobility patterns (short-distance and long-distance drivers) through a cluster analysis. Then, the impacts of the driver’s profile and trip characteristics (e.g., driving time, average speed, and breaking time and frequency) on inattention were analyzed using ordered probit models. The results show that long-distance drivers, typically associated with professionals, are less prone to distraction and drowsiness than short-distance drivers. The driving time increases the probability of inattention, while the breaking frequency is more important to mitigate inattention than the breaking time. Higher average speeds increase the inattention risk, being associated with road facilities featuring a monotonous driving environment.

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Privacy by Design: Analysis of Capacitive Proximity Sensing as System of Choice for Driver Vehicle Interfaces

Frank

Kuijper

2020

Lecture Notes in Computer Science

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Developing a multi sensors system to detect sleepiness to drivers from transport systems

Cited by 4 publications

References 6 publications

A Survey and Tutorial of EEG-Based Brain Monitoring for Driver State Analysis

A Survey and Tutorial of EEG-Based Brain Monitoring for Driver State Analysis

Exploring Monitoring Systems Data for Driver Distraction and Drowsiness Research

Privacy by Design: Analysis of Capacitive Proximity Sensing as System of Choice for Driver Vehicle Interfaces

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