Edge-Assisted Camera Selection in Vehicular Networks

Camera sensors have been widely used to perceive the vehicle surrounding environments, understand the traffic condition, and then help avoid traffic accidents. Since most sensors are limited by line of sight, the perception data collected through individual vehicle can be uploaded and shared through the edge server. To reduce the bandwidth, storage and processing cost, we propose an edge-assisted camera selection system that only selects the necessary camera images to upload to the server. The selection is based on the camera metadata which describes the coverage of the cameras represented with GPS locations, orientations, and field of views. Different from existing work, our metadata based approach can detect and locate camera occlusions by leveraging LiDAR sensors, and then precisely and quickly calculate the real camera coverage and identify the coverage overlap. Based on the camera metadata, we study two camera selection problems, the Max-Coverage problem and the Min-Selection problem, and solve them with efficient algorithms. Moreover, we propose similarity based redundancy suppression techniques to further reduce the bandwidth consumption which becomes significant due to vehicle movements. Extensive evaluations demonstrate that the proposed algorithms can effectively select cameras to maximize coverage or minimize bandwidth consumption based on the application requirements.