Enhancing the Performance of Vehicle-to-Vehicle Realtime Video Streaming for Platoons

Kuk, Seungho; Kim, Hyogon; Park, Yongtae

doi:10.1109/ivs.2018.8500514

Cited by 6 publications

(3 citation statements)

References 8 publications

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“…Some of them contribute to improving video delivery performance, reducing frame loss and increasing packet delivery ratio, enhancing peak signal-to-noise ratio and video quality. In addition to these, It also work for reducing the impact of interference, maximizing the effecting transmission rates, improving stress as well as traffic flow, achieving continuous video delivery and enhancing the performance of multimedia services [17].…”

Section: Privacy and Security Issues In Vanetmentioning

confidence: 99%

Deep Learnıng Based Enhanced Secure Emergency Vıdeo Streamıng Approach by Leveraging Blockchain Technology for Vehicular AdHoc 5G Networks

Awais,

Ali,

saeed

et al. 2024

Preprint

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VANET is a category of MANET that aims to provide wireless communication. It increases the safety of roads and passengers. Millions of people lose their precious lives in accidents yearly, millions are injured, and others incur disability daily. Emergency vehicles need clear roads to reach their destination faster to save lives. Video streaming can be more effective as compared to textual messages and warnings. To address this issue, we proposed a methodology to use visual sensors, cameras, and OBU to record emergency videos. Initially, the frames are detected. After re-recording, the frames detection algorithm detects the specific event from the video frames. Blockchain encrypts an emergency or specific event using hashing algorithms in the second layer of our proposed framework. In the third layer of the proposed methodology, encrypted video is broadcast with the help of 5G wireless technology to the connected nodes in the VANET. The dataset used in this research comprises up to 72 video sequences averaging about 120 seconds per video. All videos have different traffic conditions and vehicles. The ResNet-50 model is used for the feature extraction process of extracted frames. The model is trained using Tensorflow and Keras deep learning models. The Elbow method finds the optimal K number for the K Means model. This data is split into training and testing. 70% is reserved for training the support vector machine (SVM) model and test datasets, while 30%. 98% accuracy is achieved with 98% precision and 99% recall as results for the proposed methodology.

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Section: Privacy and Security Issues In Vanetmentioning

confidence: 99%

Deep Learnıng Based Enhanced Secure Emergency Vıdeo Streamıng Approach by Leveraging Blockchain Technology for Vehicular AdHoc 5G Networks

Awais,

Ali,

saeed

et al. 2024

Preprint

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show abstract

“…Along with these advantages, the blocking of the vision of the drivers that follows the leading vehicle is a big issue in vehicle platoons. Although the Advanced Driver-Assistance Systems (ADAS) and V2V can help prevent vehicle collisions, vision blocking can cause anxiety in the following drivers due to the blocked view [117]. Some research works have addressed this problem.…”

Section: H Platoon Of Vehiclesmentioning

confidence: 99%

“…The member vehicles will play this stream in front of the drivers, but the performance of the one-hop broadcasting can be affected by the varying gap between the vehicles, the surrounding environment, and channel utilization. AI is a promising approach planning, scheduling and optimization to address these problems of video streaming performance degradation in the IEEE 802.11p [117].…”

Section: H Platoon Of Vehiclesmentioning

confidence: 99%

Artificial Intelligence for Vehicle-to-Everything: A Survey

et al. 2019

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Recently, the advancement in communications, intelligent transportation systems, and computational systems has opened up new opportunities for intelligent traffic safety, comfort, and efficiency solutions. Artificial intelligence (AI) has been widely used to optimize traditional data-driven approaches in different areas of the scientific research. Vehicle-to-everything (V2X) system together with AI can acquire the information from diverse sources, can expand the driver's perception, and can predict to avoid potential accidents, thus enhancing the comfort, safety, and efficiency of the driving. This paper presents a comprehensive survey of the research works that have utilized AI to address various research challenges in V2X systems. We have summarized the contribution of these research works and categorized them according to the application domains. Finally, we present open problems and research challenges that need to be addressed for realizing the full potential of AI to advance V2X systems.

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E-Mobility: Transportation Sector in Transition

Shaukat

Khan

2021

Handbook of Climate Change Mitigation and Adaptation

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Enhancing the Performance of Vehicle-to-Vehicle Realtime Video Streaming for Platoons

Cited by 6 publications

References 8 publications

Deep Learnıng Based Enhanced Secure Emergency Vıdeo Streamıng Approach by Leveraging Blockchain Technology for Vehicular AdHoc 5G Networks

Deep Learnıng Based Enhanced Secure Emergency Vıdeo Streamıng Approach by Leveraging Blockchain Technology for Vehicular AdHoc 5G Networks

Artificial Intelligence for Vehicle-to-Everything: A Survey

E-Mobility: Transportation Sector in Transition

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