  Current research on trajectory prediction primarily relies on data collected
by onboard sensors of an ego vehicle. With the rapid advancement in connected
technologies, such as vehicle-to-vehicle (V2V) and vehicle-to-infrastructure
(V2I) communication, valuable information from alternate views becomes
accessible via wireless networks. The integration of information from
alternative views has the potential to overcome the inherent limitations
associated with a single viewpoint, such as occlusions and limited field of
view. In this work, we introduce V2INet, a novel trajectory prediction
framework designed to model multi-view data by extending existing single-view
models. Unlike previous approaches where the multi-view data is manually fused
or formulated as a separate training stage, our model supports end-to-end
training, enhancing both flexibility and performance. Moreover, the predicted
multimodal trajectories are calibrated by a post-hoc conformal prediction
module to get valid and efficient confidence regions. We evaluated the entire
framework using the real-world V2I dataset V2X-Seq. Our results demonstrate
superior performance in terms of Final Displacement Error (FDE) and Miss Rate
(MR) using a single GPU. The code is publicly available at:
\url{https://github.com/xichennn/V2I_trajectory_prediction}.
