An Automobile Environment Detection System Based on Deep Neural Network and its Implementation Using IoT-Enabled In-Vehicle Air Quality Sensors

Chung, Jae-joon; Kim, Hyun-Jung

doi:10.3390/su12062475

Cited by 31 publications

(20 citation statements)

References 41 publications

(72 reference statements)

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“…Using SVM, several coincidence detection algorithms were created and tested on actual limited-access highway data. For the same data set, SVM-based strategies outperformed neural community-based techniques in terms of overall performance in detecting injuries [16].…”

Section: Literature Workmentioning

confidence: 98%

Accident Detection in Autonomous Vehicles Using Modified Restricted Boltzmann Machine

Roohullah

Wahid

Ali

et al. 2022

Security and Communication Networks

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Accident detection in autonomous vehicles could save lives by reducing the time it takes for information to reach emergency responder. One of the most common reason for the death of humans is accident. Indeed, it was determined throughout the survey that road accidents are indeed the second greatest cause of death in the United States for people aged 30 to 44 years, representing for 1/3 among all deaths. The transportation industry is increasingly relying on mathematical methods and new data assets to detect injuries. Many machine learning and deep learning models have already been proposed for accident detection but still there is much space for further improvement to be done to save human lives in case of accident detection, if accidents are not identified well. In our present study, we proposed modified restricted Boltzmann machine for accident detection. Our proposed methodology consists of the following steps. In the first step, we took different accidental and nonaccidental images as an input. In the second step, we applied our proposed deep learning technique modified restricted Boltzmann machine. In the third step, when weight acceleration and coefficient adjustments are run as a generalization mechanism, then we check our model performance after applying through multiple procedures. As a result, multiple images are classified as accidental and nonaccidental images of vehicles. Proposed methodology has been applied for data set, and data have been divided into different training and testing ratios. The proposed MRBM model has an accuracy of 98% in classification of both accidental and nonaccidental images of vehicles. The proposed model outperforms the competition significantly than other in which they are compared like artificial neural network, support vector machine, and restricted Boltzmann machine techniques.

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Section: Literature Workmentioning

confidence: 98%

Accident Detection in Autonomous Vehicles Using Modified Restricted Boltzmann Machine

Roohullah

Wahid

Ali

et al. 2022

Security and Communication Networks

View full text Add to dashboard Cite

show abstract

“…Modern cars are increasingly being equipped with climate control features including in-cabin air quality control systems. Sensors and technologies are being deployed in vehicles to measure CO, CO 2 and other pollutants within the vehicles [ 157 ]. It was reported that low doses of far-UVC light inactivate airborne coronaviruses without harming human tissues [ 158 ].…”

Section: Impact Of Covid-19 On Iot and New Initiativesmentioning

confidence: 99%

Impact of COVID-19 on IoT Adoption in Healthcare, Smart Homes, Smart Buildings, Smart Cities, Transportation and Industrial IoT

Umair

Cheema

Cheema³

et al. 2021

Sensors

146

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COVID-19 has disrupted normal life and has enforced a substantial change in the policies, priorities and activities of individuals, organisations and governments. These changes are proving to be a catalyst for technology and innovation. In this paper, we discuss the pandemic’s potential impact on the adoption of the Internet of Things (IoT) in various broad sectors, namely healthcare, smart homes, smart buildings, smart cities, transportation and industrial IoT. Our perspective and forecast of this impact on IoT adoption is based on a thorough research literature review, a careful examination of reports from leading consulting firms and interactions with several industry experts. For each of these sectors, we also provide the details of notable IoT initiatives taken in the wake of COVID-19. We also highlight the challenges that need to be addressed and important research directions that will facilitate accelerated IoT adoption.

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“…As a result, AV internal issues concern in-vehicle detection and warning to provide alerts or stimulation if the driver appears to be impaired. The in-vehicle systems can monitor driver vigilance, drowsiness, and fatigue [112,113]. External issues are concerned with the safety of other vehicles and pedestrians.…”

Section: Security and Privacy Implicationsmentioning

confidence: 99%

The Role of 5G Technologies in a Smart City: The Case for Intelligent Transportation System

2021

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A smart city is an urban area that collects data using various electronic methods and sensors. Smart cities rely on Information and Communication Technologies (ICT) and aim to improve the quality of services by managing public resources and focusing on comfort, maintenance, and sustainability. The fifth generation (5G) of wireless mobile communication enables a new kind of communication network to connect everyone and everything. 5G will profoundly impact economies and societies as it will provide the necessary communication infrastructure required by various smart city applications. Intelligent Transporting System (ITS) is one of the many smart city applications that can be realized via 5G technology. The paper aims to discuss the impact and implications of 5G on ITS from various dimensions. Before this, the paper presents an overview of the technological context and the economic benefits of the 5G and how key vertical industries will be affected in a smart city, i.e., energy, healthcare, manufacturing, entertainment, and automotive and public transport. Afterward, 5G for ITS is introduced in more detail.

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An Automobile Environment Detection System Based on Deep Neural Network and its Implementation Using IoT-Enabled In-Vehicle Air Quality Sensors

Cited by 31 publications

References 41 publications

Accident Detection in Autonomous Vehicles Using Modified Restricted Boltzmann Machine

Accident Detection in Autonomous Vehicles Using Modified Restricted Boltzmann Machine

Impact of COVID-19 on IoT Adoption in Healthcare, Smart Homes, Smart Buildings, Smart Cities, Transportation and Industrial IoT

The Role of 5G Technologies in a Smart City: The Case for Intelligent Transportation System

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