Smart Driver Monitoring: When Signal Processing Meets Human Factors: In the driver's seat

Aghaei, Amirhossein S.; Donmez, Birsen; Liu, Cheng Chen; He, Dengbo; Liu, George; Plataniotis, Konstantinos N.; Chen, Huei-Yen Winnie; Sojoudi, Zohreh

doi:10.1109/msp.2016.2602379

Cited by 52 publications

(22 citation statements)

References 50 publications

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“…A potential approach is to supplement driving metrics with physiological metrics [5]. Driver's attention could be inferred by a wide spectrum of physiological signals (for a review see [6]); this paper focuses on measuring eye movements because they are a strong indicator of where attention is directed [9]. Visual attention is essential for guiding the vehicle as well as detecting and avoiding hazards on the road [10], [11].…”

Section: Accmentioning

confidence: 99%

A Reference Model for Driver Attention in Automation: Glance Behavior Changes During Lateral and Longitudinal Assistance

Morando

Victor

Dozza

2019

IEEE Trans. Intell. Transport. Syst.

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This paper introduces a reference model of glance behavior for driving safety assessment. This model can improve the design of automated and assistive systems. Technological limitations have previously hindered the use of unobtrusive eye trackers to measure glance behavior in naturalistic conditions. This paper presents a comprehensive analysis of eye-tracking data collected in a naturalistic field operation test, using an eye tracker that proved to be robust in real-world driving scenarios. We describe a post-processing technique to enhance the quality of naturalistic eye-tracker data, propose a data-analysis procedure that captures the important features of glance behavior, and develop a model of glance behavior (based on distribution fitting), which was lacking in the literature. The model and its metrics capture key defining characteristics of, and differences between, on-and off-path glance distributions, during manual driving and driving with adaptive cruise control and lane keeping aid active. The results show that drivers' visual response is tightly coupled to the driving context (vehicle automation, car-following, and illumination).

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Section: Accmentioning

confidence: 99%

A Reference Model for Driver Attention in Automation: Glance Behavior Changes During Lateral and Longitudinal Assistance

Morando

Victor

Dozza

2019

IEEE Trans. Intell. Transport. Syst.

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“…A thorough review of available literature on HFs was performed, and this review was mainly focused on cars [15][16][17][18][19][20][21][22][23][24][25][26][27][28][29], planes [30][31][32][33][34][35][36][37][38][39][40][41][42], and NPP operators [43][44][45][46][47][48][49]. The process started with drawing out all possible HFs and listing them up to avoid redundancy and duplicity of information.…”

Section: Human Factors (Hfs) In Control Take-oversmentioning

confidence: 99%

A Few Critical Human Factors for Developing Sustainable Autonomous Driving Technology

Cárdenas¹,

Shin

Kim

2020

Sustainability

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The purpose of this study is to develop a framework that can identify critical human factors (HFs) that can generate human errors and, consequently, accidents in autonomous driving level 3 situations. Although much emphasis has been placed on developing hardware and software components for self-driving cars, interactions between a human driver and an autonomous car have not been examined. Because user acceptance and trust are substantial for the further and sustainable development of autonomous driving technology, considering factors that will influence user satisfaction is crucial. As autonomous driving is a new field of research, the literature review in other established fields was performed to draw out these probable HFs. Herein, interrelationship matrices were deployed to identify critical HFs and analyze the associations between these HFs and their impact on performance. Age, focus, multitasking capabilities, intelligence, and learning speed are selected as the most critical HFs in autonomous driving technology. Considering these factors in designing interactions between drivers and automated driving systems will enhance users' acceptance of the technology and its sustainability by securing good usability and user experiences.

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“…1) Intra-Vehicle Communication: Intra-vehicle communication includes dedicated sensors for water pressure, engine temperature, and cabin temperature, as well as vision sensors based on cameras and image processing techniques [13,14]. Recent research trends are also considering monitoring the state of the driver, for example by using methods based on Electroencephalography (EEG) sensors [28].…”

Section: B Sensors and Iot In Intelligent Vehiclesmentioning

confidence: 99%

“…In the first case, recent research trends are proposing intelligent infrastructures to create smart buildings, smart grids, or even smart cities [4]. In the second case, new-generation devices and advanced algorithms are enabling the introduction of new functionalities in healthcare, smart hospitals, smart homes, and intelligent vehicles, by increasing the number of actions and emotions recognized [11,16], monitoring a wider range of health conditions [3,19,28], and detecting a greater number of dangerous situations [12,13].…”

Section: Applicationsmentioning

confidence: 99%

IoT-based Architectures for Sensing and Local Data Processing in Ambient Intelligence: Research and Industrial Trends

Cai

Genovese

Piuri

et al. 2019

2019 IEEE International Instrumentation and Measurement Technology Conference (I2MTC)

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This paper presents an overview of new-generation technologies based on Internet of Things (IoT) and Ambient Intelligence (AmI), which create smart environments that respond intelligently to the presence of people, by collecting data from sensors, aggregating measurements, and extracting knowledge to support daily activities, perform proactive actions, and improve the quality of life. Recent advances in miniaturized instrumentation, general-purpose computing architectures, advanced communication networks, and non-intrusive measurement procedures are enabling the introduction of IoT and AmI technologies in a wider range of applications. To efficiently process the large quantities of data collected in recent AmI applications, many architectures use remote cloud computing, either for data storage or for faster computation. However, local data processing architectures are often preferred over cloud computing in the cases of privacy-compliant or time-critical applications. To highlight recent advances of AmI environments for these applications, in this paper we focus on the technologies, challenges, and research trends in new-generation IoT-based architectures requiring local data processing techniques, with specific attention to smart homes, intelligent vehicles, and healthcare.

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Smart Driver Monitoring: When Signal Processing Meets Human Factors: In the driver's seat

Cited by 52 publications

References 50 publications

A Reference Model for Driver Attention in Automation: Glance Behavior Changes During Lateral and Longitudinal Assistance

A Reference Model for Driver Attention in Automation: Glance Behavior Changes During Lateral and Longitudinal Assistance

A Few Critical Human Factors for Developing Sustainable Autonomous Driving Technology

IoT-based Architectures for Sensing and Local Data Processing in Ambient Intelligence: Research and Industrial Trends

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