A critical component for advancing driving automation is trajectory prediction, which enables vehicles to navigate complex driving scenarios and make informed decisions in unfamiliar environments. However, training models exclusively on trajectory supervision presents significant challenges for trajectory prediction in autonomous systems. We propose a Velocity-Angle Trajectory Network (VATNet), an integrated approach that enhances trajectory prediction tasks with an additional velocity prediction and angle prediction task in a multi-task learning framework. This assists the model in cross-validating and correcting errors in trajectory prediction, leveraging temporal relationships and directional context to achieve more reliable outcomes. To address the challenges posed by the periodic nature of angles, we use a classification approach. This method discretizes angles into bins and focuses on probability distributions rather than precise values, thereby reducing numerical instability associated with regression. To mitigate the issue of overfitting to any single task, we introduce an uncertainty-aware weighted loss that dynamically assigns higher priority to tasks identified as challenging or uncertain. The VATNet model has been evaluated on Argoverse 2 dataset and outperforms state-of-the-art models by a margin of 3.76%-33.83%, 5.68%-42.97%, 11.84%-90.78% and 0.37%-29.73% on the minADE6, minFDE6, MR6 and b-FDE6 respectively.