In autonomous driving, LiDAR plays an important role for detecting and identifying various objects (e.g., vehicles, buildings) and people (e.g., pedestrians). However, it is vulnerable to attacks using laser-absorbing material to disrupt inter-vehicle distance estimation. On the other hand, camera systems are resilient to these attacks but face challenges in accurately identifying the target under adversarial attacks inducing object misclassification. Therefore, this study proposes a collaboration method integrating both camera and LiDAR sensors to enable reliable operation and defend against spoofing attacks. In the proposed method, vehicles share the results of object recognition (e.g., position, class, confidence values) from their sensors via a mesh network. And then, attack detection is performed by identifying the dissimilar parts of the recognition results among the sensors and nearby vehicles. Experimental evaluation demonstrates that this cooperative strategy significantly enhances attack detection accuracy against simulated spoofing attacks compared to the conventional standalone system on a vehicle with a single sensor.