  The prediction of surrounding vehicle trajectories is crucial for
collision-free path planning. In this study, we focus on a scenario where a
connected and autonomous vehicle (CAV) serves as the central agent, utilizing
both sensors and communication technologies to perceive its surrounding
traffics consisting of autonomous vehicles (AVs), connected vehicles (CVs), and
human-driven vehicles (HDVs). Our trajectory prediction task is aimed at all
the detected surrounding vehicles. To effectively integrate the multi-source
data from both sensor and communication technologies, we propose a deep
learning framework called MSMA utilizing a cross-attention module for
multi-source data fusion. Vector map data is utilized to provide contextual
information. The trajectory dataset is collected in CARLA simulator with
synthesized data errors introduced. Numerical experiments demonstrate that in a
mixed traffic flow scenario, the integration of data from different sources
enhances our understanding of the environment. This notably improves trajectory
prediction accuracy, particularly in situations with a high CV market
penetration rate. The code is available at: https://github.com/xichennn/MSMA.
