Abstract
The most important thing for Connected and Automated Vehicles (CAVs) is to ensure driving safety and prevent the loss of life and property due to danger. The existence of vehicle blind spots can lead to incomplete or ineffective access to information, which will bring risks. At the same time, the transmission of a large amount of duplicate data will lead to information redundancy and bandwidth waste. In this paper, we design BP-CODS, which uses blind-spot prediction assistance to schedule image data between vehicles with the support of the Edge Server. We model the data scheduling transmission as two processes of uploading and downloading, form the set coverage problem, and propose a heuristic algorithm to solve it. We conduct extensive simulation experiments in CARLA to verify the effectiveness of BP-CODS in reducing a large number of redundant data.
This work is supported in part by National Key R &D Program of China under Grant 2019YFB2102400, in part by National Natural Science Foundation of China under Grant No. 62072330, in part by China Postdoctoral Science Foundation 2020M680906, in part by Hebei Province High-level Talent Funding Project B202003027, in part by Tianjin Research Innovation Project for Postgraduate Students 2021YJSO2S04.
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Li, T., Zhang, C., Zhou, X. (2022). BP-CODS: Blind-Spot-Prediction-Assisted Multi-Vehicle Collaborative Data Scheduling. In: Wang, L., Segal, M., Chen, J., Qiu, T. (eds) Wireless Algorithms, Systems, and Applications. WASA 2022. Lecture Notes in Computer Science, vol 13473. Springer, Cham. https://doi.org/10.1007/978-3-031-19211-1_25
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