Context-awareness in the smart car

Soultana, Abdelfettah; Benabbou, Faouzia; Sael, Nawal

doi:10.1145/3368756.3369019

Cited by 5 publications

(5 citation statements)

References 26 publications

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“…Fig. 11 shows the experimental results of the interactive mode sensing algorithm when both w and b are set to 0.5 in equation (14). The experimental results show that the interactive mode sensing algorithm designed and implemented in this chapter can better judge whether the mobile phone holder is in the process of using the phone or not.…”

Section: B Spatial Position Awareness Algorithm and Interaction Mode Amentioning

confidence: 98%

“…Yigitbas et al [13] studied how to exploit context-awareness in VR applications based on context parameters like the user, platform, and environment and present a context-aware VR application for first aid training. Soultana et al [14] presented a comparative and statistical study of the research published in the last three years on context-based driver assistance systems, depending on variant criteria such as the context, the algorithms used, and the technologies adopted. They provided an overview of the results of this survey and also proposed a model that classifies the different contexts and their features based on the literature review.…”

Section: Related Workmentioning

confidence: 99%

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Context Awareness-Based Accident Prevention During Mobile Phone Use

Sun

Guo³

et al. 2020

IEEE Access

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In recent years, with the continuous development of technologies, people's dependence on smartphones is growing increasingly. Excessive dependence has been causing a considerable number of people to face potential accidents and injuries on account of the use of mobile phones. Based on the above considerations, this paper defines the dangerous context in mathematical forms, using the data collected by sensors in phones and context-aware technology to study the accident prevention methods in the process of using mobile phones. The research involves three parts. Human behavior awareness subproblem refers to the type of behavior currently being taken by the mobile phone holder, which is different from the traditional human behavior recognition problem. The spatial position awareness subproblem refers to the perception of whether the phone holder is in a dangerous area prone to accidental injury. The interactive awareness subproblem refers to how the mobile phone holder interacts with the mobile phone to determine whether the mobile phone holder is in the process of using the mobile phone. The study of accident prevention methods during the use of mobile phones will be based on the output of the above subproblems. After designing and implementing the solutions toward the three subproblems, algorithms are integrated, and the application is designed. INDEX TERMS Dangerous context, context awareness, mobile phone use, accident prevention.

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Section: B Spatial Position Awareness Algorithm and Interaction Mode Amentioning

confidence: 98%

Section: Related Workmentioning

confidence: 99%

Context Awareness-Based Accident Prevention During Mobile Phone Use

Sun

Guo³

et al. 2020

IEEE Access

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“…This systematic review aims to recognize the different approaches used for driver monitoring, and to identify the different artificial intelligence algorithms for driver distraction and fatigue detection. We aim to recognize what we know in the field of car monitoring systems based on the driver context [33], and to identify challenges to overcome. The comprehensive review of full-text research articles is designed to address the following research questions: RQ1: What are the different approaches used to detect driver inattention (fatigue/ distraction)?…”

Section: Research Questionsmentioning

confidence: 99%

A Systematic Literature Review of Driver Inattention Monitoring Systems for Smart Car

Soultana¹,

Benabbou

Sael³

et al. 2022

Int. J. Interact. Mob. Technol.

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In recent years, a significant increase in road accidents worldwide has been observed. This can partly be due to either driver distraction or fatigue. Therefore, a reliable alerting system that can detect the driver's inattention including fatigue, sleep, and distraction is necessarily required to prevent any potential accidents. The aim of this paper is to conduct a systematic review of literature (SLR) on monitoring driver inattention. In particular, the present study deals with different aspects of prior studies such as the sensors used; the types of data, the feature engineering techniques, the machine-learning techniques applied and their performance along with, the dataset used, etc. anotherFour approaches can be depicted from literature according to indicators they are based on: physiological, physical, driver performance and hybrid approach. We will focus on these different approaches in order to answer different questions, starting with the type of indicators used in the case of distraction or fatigue detection, the different datasets employed, the feature extraction techniques and the machine learning models applied. Furthermore, the study examines the practicality and reliability of each of the four approaches, as well as possible future prospects in the area, and highlights new challenges in the field of driver inattention detection with both forms of fatigue and distraction.

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“…The connections between the driver, the car, and the environment must be investigated to understand driver behavior. Therefore, three contexts have to be considered [26]:…”

Section: Introductionmentioning

confidence: 99%

Driver Behavior Classification: A Systematic Literature Review

2023

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Driver behavior is receiving increasing attention as a result of the staggering number of road accidents. Many road safety reports regard human behavior as the most important factor in the likelihood of accidents. The detection and classification of aggressive or abnormal driver behavior is an essential requirement in the real world to avoid deadly road accidents and to protect road users. The automatic detection of the driver's behavior aids in the prevention of dangerous situations for the driver and all other participants in the driving environment, as well as the implementation of corrective measures. This paper presents a systematic literature review (SLR) of the classification of driver behavior. The study aim is to highlight and analyze the different types of driver behavior, data sources, datasets, features, and artificial intelligence techniques used to classify driver behavior and its performance. Based on the results obtained from the analysis of the selected works, we aim to identify the key contributions and challenges of studying driver behavior classification and propose potential avenues for further directions for practitioners and researchers.INDEX TERMS Driver behavior, intelligent transport system, systematic literature review, machine learning, deep learning.

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Context-awareness in the smart car

Cited by 5 publications

References 26 publications

Context Awareness-Based Accident Prevention During Mobile Phone Use

Context Awareness-Based Accident Prevention During Mobile Phone Use

A Systematic Literature Review of Driver Inattention Monitoring Systems for Smart Car

Driver Behavior Classification: A Systematic Literature Review

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