Ford Motor Company E-Commerce Development on Social Networks based on HED Feature Extraction, Machine Learning and Alan Mulally’s Strategy

Barshooi, Amir Hossein

doi:10.1109/icwr57742.2023.10139027

Cited by 5 publications

(1 citation statement)

References 20 publications

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“…Ford Motor Company, a leading global automaker, has been implementing blockchain technology in the automotive sector for a number of years, in collaboration with three other companies: BMW, General Motors, and Renault. The consortium formed the Mobility Open Blockchain Initiative (MOBI) to focus on securing, controlling, and managing information related to the automotive industry with blockchain technology [18]. In this regard, Rathee et al [19], presented a security mechanism for AVs using blockchain technology.…”

Section: Internet Of Things and Blockchainsmentioning

confidence: 99%

Nighttime Driver Behavior Prediction Using Taillight Signal Recognition via CNN-SVM Classifier

Bagheri,

Barshooi

2023

Preprint

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This paper aims to enhance the ability to predict nighttime driving behavior by identifying the taillights of both human-driven and autonomous vehicles. The proposed model incorporates a customized detector designed to accurately detect front-vehicle taillights on the road. At the beginning of the detector, a learnable pre-processing block is implemented, which extracts deep features from input images and calculates the data rarity for each feature. In the next step, drawing inspiration from soft attention, a weighted binary mask is designed that guides the model to focus more on predetermined regions. This research utilizes Convolutional Neural Networks (CNNs) to extract distinguishing characteristics from these areas, then reduces dimensions using Principal Component Analysis (PCA). Finally, the Support Vector Machine (SVM) is used to predict the behavior of the vehicles. To train and evaluate the model, a large-scale dataset is collected from two types of dash-cams and Insta360 cameras from the rear view of Ford Motor Company vehicles. This dataset includes over 12K frames captured during both daytime and nighttime hours. To address the limited nighttime data, a unique pixel-wise image processing technique is implemented to convert daytime images into realistic night images. The findings from the experiments demonstrate that the proposed methodology can accurately categorize vehicle behavior with 92.14% accuracy, 97.38% specificity, 92.09% sensitivity, 92.10% F1-measure, and 0.895 Cohen’s Kappa Statistic. Further details are available at https://github.com/DeepCar/Taillight_Recognition.

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Section: Internet Of Things and Blockchainsmentioning

confidence: 99%