Priority-Aware Perception Data Preprocessing and Offloading in Vehicle-Road Collaboration

Vehicle-road collaboration is an effective means of improving perception capacities and enhancing safety of intelligent connected vehicles (ICVs). A larger volume of perception data increases the accuracy and robustness of environmental understanding, but it also introduces heavier computation loads. Aiming to reduce data size while meeting perception requirements, this paper studies joint data preprocessing and offloading in vehicle-road collaboration. In the preprocessing stage, we assign different priorities to the detected objects based on their types and distances from the perceiving vehicles. We allow discarding some low-priority objects that may not need immediate attention to reduce computation loads in subsequent data processing. After object selection and downsampling on video frames, the downsized perception data is offloaded and processed collectively by ICVs and roadside units (RSUs). A nonconvex mixed-integer problem is formulated, maximizing the sum of priorities of the selected objects while satisfying constraints of time delay, bandwidth, and computing resources. A fast heuristic based on the penalty alternating direction method (PADM) and modified annealed feasibility pump (MAFP) is developed to solve the problem. Results show that the proposed method is more computationally efficient than the commercial solver in solving the priority maximization problem. Also, it can significantly reduce perception data size, enabling efficient use of the limited communication and computing resources to timely complete more high-priority tasks.