InterCoop: Spatio-Temporal Interaction Aware Cooperative Perception for Networked Vehicles

In autonomous driving, cooperative perception through vehicle-to-vehicle (V2V) communication is considered crucial for enhancing traffic safety and efficiency. However, existing methods often simplify the handling of perception data from multiple vehicles. In these approaches, the egovehicle aggregates observations from all neighboring connected cooperative vehicles (CCV), without considering the interactions between the vehicles or making differentiated use of the acquired sensing data. This approach can result in suboptimal performance due to the increase of noise and large transmission delay. In this paper, we introduce a novel approach to cooperative perception. By fusing both the road topology and trajectory histories of neighboring CCVs, our model learns an interaction score for each CCV. These scores prioritize vehicles that are most relevant to the current driving scenario, offering valuable guidance for selective fusion of sensor data, thereby enhancing driving decision-making. The proposed method is validated through experiments conducted on the CARLA simulator. Results demonstrate that our approach surpasses existing methods in terms of performance and robustness.