LFF-V2V: A Late Fusion Cooperative Framework in V2V Scenarios

Traditional perception systems in automated driving have different constraints that do not allow for complete environmental awareness. Cooperative Perception (CP) addresses these limitations by sharing information between vehicles and/or infrastructure through Vehicle-to-Everything (V2X) communications. This collaborative approach mitigates occlusions and extends sensor coverage, proving essential for Cooperative Driving Automation (CDA). However, there are remaining challenges about its application in online real-world scenarios, such as CP information transmission and communication degradations. In this cooperative context, Motion Prediction (MP) proves to be crucial, since it provides a scene representation of all the agents with their positions, velocities and future trajectories. Thus, shared information between agents can improve each agent understanding of the overall scene. This paper introduces LFF - V2V, A Late Fusion Cooperative Framework in V2V Scenarios. It combines two state-of-the-art late fusion methods, Non-Maximum Suppression (NMS) and Weighted Box Fusion (WBF), with a mapless Hierarchical Vector Transformer (HiVT) motion prediction model. We have conducted an extensive evaluation in two environments: CARLA simulator and the real-world V2X-Real dataset, analyzing different communication strategies. Our results demonstrate the effectiveness of CP in improving object detection and motion prediction, even with degraded communications.