Vehicle-to-infrastructure cooperative 3D object detection (VIC3D) is a task that leverages both vehicle and roadside sensors to jointly perceive the surrounding environment. However, considering the high speed of vehicles, the real-time requirements, and the limitations of communication bandwidth, roadside devices transmit the results of perception rather than raw sensor data or feature maps in our real-world scenarios. And affected by various environmental factors, the transmission delay is dynamic. To meet the needs of practical applications, we present OTVIC, which is the first multi-modality and multi-view dataset with online transmission from real scenes for vehicle-to-infrastructure cooperative 3D object detection. The ego-vehicle receives the results of infrastructure perception in real-time, collected from a section of highway in Chengdu, China. Moreover, we propose LfFormer, which is a novel end-to-end multi-modality late fusion framework with transformer for VIC3D task as a baseline based on OTVIC. Experiments prove our fusion framework's effectiveness and robustness.