Autonomous vehicles rely on cooperative perception to extend environmental awareness and enhance safety. However, contemporary methods typically select cooperative vehicles based solely on distance or link quality, without accounting for occlusions or redundant coverage, thereby incurring higher costs. In this paper, we propose an edge-assisted low-overhead cooperative perception system that achieves a predetermined coverage with a minimum number of vehicles. Specifically, we first introduce a lightweight LiDAR-based occlusion detection method to generate Perception Metadata (PMD), which accurately describes each vehicle’s coverage in terms of locations, orientations, and fields of view. On this basis, we formulate the vehicle selection problem as a Min-Selection problem and solve it using the proposed Q-learning-based Adaptive Vehicle Selection (QAVS) algorithm. The QAVS algorithm is able to select the vehicles with the minimum perception coverage redundancy depending on PMD. Finally, based on the KITTI dataset and the simulation dataset, our LiDAR-based occlusion detection method achieves a competitive recall rate compared to deep-learning methods, while significantly reducing latency. Meanwhile, QAVS selects fewer vehicles than greedy or random selection methods for equal coverage targets.