Toward Collaborative Occlusion-Free Perception in Connected Autonomous Vehicles

In connected autonomous vehicles (CAVs), the driving safety can be greatly deteriorated, in the presence of occlusions which are adverse to CAVs' perception of region-of-interest (RoI). Collaborative perception on the basis the information sharing of occlusions among CAVs, in a real-time and accurate manner, provides a means of the occlusion-free RoI perception for safe driving. In this paper, we propose a novel framework of Collaborative Occlusion-free Perception (COFP) in CAVs, to regain the real-time and accurate occlusion awareness. The innovative COFP targets two goals: well-balanced computation resource allocation, as well as fast and high-quality RoI information fusion. Specifically, the resource allocation problem, with the objective of minimizing CAVs' completion delay, is formulated as a multi-player continuous potential game and solved by a better response dynamics (BRD) algorithm. The RoI information fusion, with the objective of maximizing the overall object depiction quality, is formulated as a combinatorial optimization problem, and solved by a modified discrete salp swarm (MDSSA) algorithm. Experimental results show that the proposed COFP with 5 GHz computing power can achieve full occlusion awareness for CAVs with 69.61% completion time reduction and 19.03% fusion quality improvement, compared to the existing methods.