Research on Customer Marketing Acceptance for Future Automatic Driving—A Case Study in China City

Wang, Haiwei; Feng, Yuanjing; Chu, Xinnan; Li, Xiaolong; Sun, Xiaojuan

doi:10.1109/access.2019.2898936

Cited by 19 publications

(11 citation statements)

References 59 publications

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“…To test the construct validity, the Kaiser-Meyer-Olkin (KMO) and Bartlett tests were applied following varimax orthogonal rotation and factor analysis. Generally, the data is appropriate for factor analysis when the KMO value is above 0.7, and the p value of Bartlett's test is significant at the 0.05 level, and the correlation matrix of the sample has a shared factor ( Wang et al., 2019 ; Zhang et al., 2018 ).…”

Section: Resultsmentioning

confidence: 99%

Use of social networking in the Middle East: student perspectives in higher education

Issa

Alqahtani

Al‐Oqily

et al. 2021

Heliyon

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This study aims to determine the benefits, risks, awareness, cultural factors, and sustainability, allied to social networking (SN) use in the higher education (HE) sector in Middle Eastern countries, namely Jordan, Saudi Arabia, and Turkey. Using an online survey, 1180 complete responses were collected and analyzed using the statistical confirmatory factor analysis method. The use of SN in the Middle Eastern HE sector has the capacity to promote and motivate students to acquire professional and personal skills for their studies and future workplace; however, the use of SN by tertiary students is also associated with several risks: isolation, depression, privacy, and security. Furthermore, culture is influenced by using SN use, since some countries shifted from one dimension to another based on Hofstede's cultural framework. The study new findings are based on a sample at a specific point in time within a culture. The study findings encourage academics to include SN in unit activities and assessments to reap the benefits of SN, while taking steps to mitigate any risks that SN poses to students. Although other studies in the Middle East examined the use of Learning Management System and Facebook in, HE as a means of engaging students in discussions and communications, however, this study contributes a better understanding of the benefits and risks, awareness, culture, and sustainability, associated with the use of SN in the HE sector in the Middle East. Finally, the paper concludes with an acknowledgment of the study limitations and suggestions for future research.

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Section: Resultsmentioning

confidence: 99%

Use of social networking in the Middle East: student perspectives in higher education

Issa

Alqahtani

Al‐Oqily

et al. 2021

Heliyon

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“…Fatigue driving endangers the driver and other traffic participants, as it declines the driving cognitive and driving skills, therefore resulting in misperception, misjudge, and misoperation. To ensure driving security and traffic safety, the driver must have a clear understanding of the driving condition and surrounding road environments all the time [35]. It requires the driver to continually adjust the head orientation and the fixation point of the eye.…”

Section: Motion State Parameter Extractionmentioning

confidence: 99%

A Fatigue Driving Detection Algorithm Based on Facial Motion Information Entropy

Feng

Gong

et al. 2020

Journal of Advanced Transportation

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Research studies on machine vision-based driver fatigue detection algorithm have improved traffic safety significantly. Generally, many algorithms asses the driving state according to limited video frames, thus resulting in some inaccuracy. We propose a real-time detection algorithm involved in information entropy. Particularly, this algorithm relies on the analysis of sufficient consecutive video frames. First, we introduce an improved YOLOv3-tiny convolutional neural network to capture the facial regions under complex driving conditions, eliminating the inaccuracy and affections caused by artificial feature extraction. Second, we construct a geometric area called Face Feature Triangle (FFT) based on the application of the Dlib toolkit as well as the landmarks and the coordinates of the facial regions; then we create a Face Feature Vector (FFV), which contains all the information of the area and centroid of each FFT. We use FFV as an indicator to determine whether the driver is in fatigue state. Finally, we design a sliding window to get the facial information entropy. Comparative experiments show that our algorithm performs better than the current ones on both accuracy and real-time performance. In simulated driving applications, the proposed algorithm detects the fatigue state at a speed of over 20 fps with an accuracy of 94.32%.

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“…To ensure the safety of autonomous vehicles, a holistic fleet deployment scheme from interdisciplinary perspectives should be established as proposed by Koopman and Wagner [22]. Wang et al investigated the acceptance of intelligent driving vehicles in Guangzhou and indicated that consumers focused on not only the developmental prospects but also the technological safety of intelligent driving vehicles [23]. With the rapid progress of IoV and VANET, there is a great deal of technologies involved in data analysis for automatic vehicles, which are described as follows.…”

Section: Data Analysis For Automaticmentioning

confidence: 99%

Vehicle Movement Analyses Considering Altitude Based on Modified Digital Elevation Model and Spherical Bilinear Interpolation Model: Evidence from GPS-Equipped Taxi Data in Sanya, Zhengzhou, and Liaoyang

Gui

2020

Journal of Advanced Transportation

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Aggravating energy shortages and increasing labor costs have become global problems and have garnered special importance in recent years in the transportation sector, especially in taxi markets. Automatic vehicles have a bright future, however, there is an equal amount of skepticism and concern about safety for all the optimism. To unlock the potential of automatic vehicles in intelligent transportation systems, a lot more data and testing are required to promote safety level as far as possible and achieve the organizational aim of reducing accidents to zero tolerance. And it is vital to provide accurate models for vehicle movement analyses. In this study, Modified Digital Elevation (MDE) model and Spherical Bilinear Interpolation (SBI) model were proposed for vehicle movement analyses considering altitude. And the experimental data of 9,990 GPS-enabled taxis in Sanya, Zhengzhou, and Liaoyang were adopted to support comparisons. Measurement results showed that MDE model had over 99% less disparity with direct solution than original model and SBI model could further improve the effects. It indicated that the application of MDE model and SBI model could improve both accuracy and efficiency of vehicle movement analyses and it had a bright future in the field of automatic vehicles. Future directions could be improving models and expanding data.

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Research on Customer Marketing Acceptance for Future Automatic Driving—A Case Study in China City

Cited by 19 publications

References 59 publications

Use of social networking in the Middle East: student perspectives in higher education

Use of social networking in the Middle East: student perspectives in higher education

A Fatigue Driving Detection Algorithm Based on Facial Motion Information Entropy

Vehicle Movement Analyses Considering Altitude Based on Modified Digital Elevation Model and Spherical Bilinear Interpolation Model: Evidence from GPS-Equipped Taxi Data in Sanya, Zhengzhou, and Liaoyang

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