Enhancing Cooperative LiDAR-Based Perception Accuracy in Vehicular Edge Networks

In this paper, we investigate the problem of enhancing cooperative LiDAR-based perception accuracy in vehicular edge networks. The key to solving this problem is the selection of connected and autonomous vehicles (CAVs) that can collectively provide maximum perception performance. In specific, extensive motivating experiments on an open benchmark dataset are conducted, which reveal that the cooperative perception accuracy is a submodular combination of selected CAVs, and such selection is non-trivial due to high vehicular mobility as well as unstable vehicular network conditions. Then, we develop an Edge coordinated COoperative Perception (ECOP) framework, taking into account both cooperative vehicle selection and adaptive bandwidth allocation. The novelty of the ECOP design is threefold. First, a new metric named perceptual gain is designed, which properly quantifies the individual perception contributions of each CAV without incurring additional computational overhead. Secondly, an online vehicle selection strategy, which utilizes continual learning to assess the perceptual gain of each CAV, is devised. Theoretical analysis indicates that the proposed vehicle selection strategy can achieve asymptotically diminishing learning regret, highlighting its effectiveness in adapting to vehicular mobility. Finally, an optimal bandwidth allocation method is proposed, which can adapt to heterogeneous and unstable vehicular network conditions. Simulation results demonstrate that, compared with other benchmarks, ECOP can select vehicle sets with the highest cooperative perception accuracy and ensure real-time perception in the presence of fluctuating bandwidth. Furthermore, a case study is presented to visualize the effectiveness of the proposed ECOP framework.