A Study for Application in Vehicle Networking and Driverless Driving

Zhongmei, Lu; Huang, Yu-Che; Bangjun, Cui

doi:10.1145/3374587.3374647

Cited by 2 publications

(2 citation statements)

References 2 publications

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“…Three papers [52][53][54] reported on the evolving automotive cyber security, which can use 5G to be faster and more efficient. Presented a peer-to-peer IoT infrastructure, built to OpenFog standards, which moves control out to "things" without involving devices or cloud computing.…”

Section: Review and Survey Articlesmentioning

confidence: 99%

From 5G to 6G Technology: Meets Energy, Internet-of-Things and Machine Learning: A Survey

Mahdi

Ahmad

Qassim³

et al. 2021

Applied Sciences

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Due to the rapid development of the fifth-generation (5G) applications, and increased demand for even faster communication networks, we expected to witness the birth of a new 6G technology within the next ten years. Many references suggested that the 6G wireless network standard may arrive around 2030. Therefore, this paper presents a critical analysis of 5G wireless networks’, significant technological limitations and reviews the anticipated challenges of the 6G communication networks. In this work, we have considered the applications of three of the highly demanding domains, namely: energy, Internet-of-Things (IoT) and machine learning. To this end, we present our vision on how the 6G communication networks should look like to support the applications of these domains. This work presents a thorough review of 370 papers on the application of energy, IoT and machine learning in 5G and 6G from three major libraries: Web of Science, ACM Digital Library, and IEEE Explore. The main contribution of this work is to provide a more comprehensive perspective, challenges, requirements, and context for potential work in the 6G communication standard.

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Section: Review and Survey Articlesmentioning

confidence: 99%

From 5G to 6G Technology: Meets Energy, Internet-of-Things and Machine Learning: A Survey

Mahdi

Ahmad

Qassim³

et al. 2021

Applied Sciences

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“…5G technologies present a new paradigm to provide connectivity to vehicles, in support of high data rate services, complementing existing AMP communication standards [1]. The 5G network has low latency, high throughput, and high reliability, which greatly enhances the richness and timeliness of the information transmitted by the car network, and also improves the technical value of the car network sensor [2]. It provides the network foundation for cloud computing and edge computing.…”

Section: Introductionmentioning

confidence: 99%

Unsupervised Learning of Depth from Monocular Videos Using 3D-2D Corresponding Constraints

et al. 2021

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Depth estimation can provide tremendous help for object detection, localization, path planning, etc. However, the existing methods based on deep learning have high requirements on computing power and often cannot be directly applied to autonomous moving platforms (AMP). Fifth-generation (5G) mobile and wireless communication systems have attracted the attention of researchers because it provides the network foundation for cloud computing and edge computing, which makes it possible to utilize deep learning method on AMP. This paper proposes a depth prediction method for AMP based on unsupervised learning, which can learn from video sequences and simultaneously estimate the depth structure of the scene and the ego-motion. Compared with the existing unsupervised learning methods, our method makes the spatial correspondence among pixel points consistent with the image area by smoothing the 3D corresponding vector field based on 2D image, which effectively improves the depth prediction ability of the neural network. Our experiments on the KITTI driving dataset demonstrated that our method outperformed other previous learning-based methods. The results on the Apolloscape and Cityscapes datasets show that our proposed method has a strong universality.

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A Study for Application in Vehicle Networking and Driverless Driving

Cited by 2 publications

References 2 publications

From 5G to 6G Technology: Meets Energy, Internet-of-Things and Machine Learning: A Survey

From 5G to 6G Technology: Meets Energy, Internet-of-Things and Machine Learning: A Survey

Unsupervised Learning of Depth from Monocular Videos Using 3D-2D Corresponding Constraints

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