BP-CODS: Blind-Spot-Prediction-Assisted Multi-Vehicle Collaborative Data Scheduling

Tailai Li, Chaokun Zhang, Xiaobo Zhou

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.