A New Vehicular Fog Computing Architecture for Cooperative Sensing of Autonomous Driving
The sensing coverage and accuracy of vehicles are vital for autonomous driving. However, the current sensing capability of a single autonomous vehicle is quite limited in the complicated road traffic environment, which leads to many sensing dead zones or frequent misdetection. In this paper, we prop...
| Main Authors: | , , , , |
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| Format: | Article |
| Language: | English |
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IEEE
2020-01-01
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| Series: | IEEE Access |
| Subjects: | |
| Online Access: | https://ieeexplore.ieee.org/document/8950168/ |
| _version_ | 1830300681303490560 |
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| author | Hao Du Supeng Leng Fan Wu Xiaosha Chen Sun Mao |
| author_facet | Hao Du Supeng Leng Fan Wu Xiaosha Chen Sun Mao |
| author_sort | Hao Du |
| collection | DOAJ |
| description | The sensing coverage and accuracy of vehicles are vital for autonomous driving. However, the current sensing capability of a single autonomous vehicle is quite limited in the complicated road traffic environment, which leads to many sensing dead zones or frequent misdetection. In this paper, we propose to develop a Vehicular Fog Computing (VFC) architecture to implement cooperative sensing among multiple adjacent vehicles driving in the form of a platoon. Based on our VFC architecture greedy and Support Vector Machine (SVM) algorithms are adopted respectively to enhance the sensing coverage and accuracy in the platoon. Furthermore, the distributed deep learning is processed for trajectory prediction by applying the Light Gated Recurrent Unit (Li-GRU) neural network algorithm. Simulation results based on real-world traffic datasets indicate the sensing coverage and accuracy by the proposed algorithms can be significantly improved with low computational complexity. |
| first_indexed | 2024-12-19T08:32:33Z |
| format | Article |
| id | doaj.art-263228862318436386b7977b1fe59000 |
| institution | Directory Open Access Journal |
| issn | 2169-3536 |
| language | English |
| last_indexed | 2024-12-19T08:32:33Z |
| publishDate | 2020-01-01 |
| publisher | IEEE |
| record_format | Article |
| series | IEEE Access |
| spelling | doaj.art-263228862318436386b7977b1fe590002022-12-21T20:29:08ZengIEEEIEEE Access2169-35362020-01-018109971100610.1109/ACCESS.2020.29640298950168A New Vehicular Fog Computing Architecture for Cooperative Sensing of Autonomous DrivingHao Du0https://orcid.org/0000-0002-6912-2749Supeng Leng1https://orcid.org/0000-0003-0049-5982Fan Wu2Xiaosha Chen3Sun Mao4https://orcid.org/0000-0002-9911-8484School of Information and Communication Engineering, University of Electronic Science and Technology of China, Chengdu, ChinaSchool of Information and Communication Engineering, University of Electronic Science and Technology of China, Chengdu, ChinaSchool of Information and Communication Engineering, University of Electronic Science and Technology of China, Chengdu, ChinaSchool of Information and Communication Engineering, University of Electronic Science and Technology of China, Chengdu, ChinaSchool of Information and Communication Engineering, University of Electronic Science and Technology of China, Chengdu, ChinaThe sensing coverage and accuracy of vehicles are vital for autonomous driving. However, the current sensing capability of a single autonomous vehicle is quite limited in the complicated road traffic environment, which leads to many sensing dead zones or frequent misdetection. In this paper, we propose to develop a Vehicular Fog Computing (VFC) architecture to implement cooperative sensing among multiple adjacent vehicles driving in the form of a platoon. Based on our VFC architecture greedy and Support Vector Machine (SVM) algorithms are adopted respectively to enhance the sensing coverage and accuracy in the platoon. Furthermore, the distributed deep learning is processed for trajectory prediction by applying the Light Gated Recurrent Unit (Li-GRU) neural network algorithm. Simulation results based on real-world traffic datasets indicate the sensing coverage and accuracy by the proposed algorithms can be significantly improved with low computational complexity.https://ieeexplore.ieee.org/document/8950168/Intelligent vehiclesvehicular fog computingcooperative sensingautonomous driving |
| spellingShingle | Hao Du Supeng Leng Fan Wu Xiaosha Chen Sun Mao A New Vehicular Fog Computing Architecture for Cooperative Sensing of Autonomous Driving IEEE Access Intelligent vehicles vehicular fog computing cooperative sensing autonomous driving |
| title | A New Vehicular Fog Computing Architecture for Cooperative Sensing of Autonomous Driving |
| title_full | A New Vehicular Fog Computing Architecture for Cooperative Sensing of Autonomous Driving |
| title_fullStr | A New Vehicular Fog Computing Architecture for Cooperative Sensing of Autonomous Driving |
| title_full_unstemmed | A New Vehicular Fog Computing Architecture for Cooperative Sensing of Autonomous Driving |
| title_short | A New Vehicular Fog Computing Architecture for Cooperative Sensing of Autonomous Driving |
| title_sort | new vehicular fog computing architecture for cooperative sensing of autonomous driving |
| topic | Intelligent vehicles vehicular fog computing cooperative sensing autonomous driving |
| url | https://ieeexplore.ieee.org/document/8950168/ |
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