Low-Latency Perception Sharing Services for Connected Autonomous Vehicles

Connected autonomous vehicles (CAVs) are promising to improve road safety, thanks to various on-board sensors, such as LiDAR, radars, and stereo cameras. However, perception view could be significantly limited due to occlusions, extreme weather, and far objects. To address these challenges, in this paper, we propose an efficient edge-assisted perception sharing scheme, which enables vehicles to exchange the information about their sensed environment to improve road safety. We formulate perception sharing as an online optimization problem, with the objective of maximizing the total weighted utility, where utility indicates the quality of collected sensor data while weight means the intensity of the vehicle's demand for information in a certain area. To solve this problem, we propose an efficient online heuristic algorithm, which decouples the original problem into multiple sub-problems and solves them alternatively to find the optimal solution. Extensive simulations demonstrate that our proposed method can significantly improve the perception sharing performance.