Cooperative perception, offering a wider field of view than standalone perception, is becoming increasingly crucial in autonomous driving. This advanced perception is enabled through vehicle-to-vehicle (V2V) communication, allowing connected automated vehicles (CAVs) to exchange sensor data, such as LiDAR point clouds, thereby enhancing the collective understanding of the environment. In this paper, we leverage an importance map to distill critical semantic information, introducing a cooperative perception semantic communication framework that employs intermediate fusion. Furthermore, recognizing the necessity for reliable transmission, especially in low SNR scenarios, we introduce a novel semantic error detection method that is integrated within our semantic communication framework in the spirit of hybrid automatic repeated request (HARQ). Simulation results show that our model surpasses traditional separate source-channel coding methods in perception performance. Additionally, in terms of throughput, our proposed HARQ schemes demonstrate superior efficiency compared to conventional coding approaches.