With the world’s population recently surpassing the 8 billion mark, population growth poses significant challenges on the planet. This growth is particularly evident in urban areas, and as a consequence, cities must find innovative ways to accommodate the increasing pressure on the current road infrastructure. In Amsterdam, a city with an extensive network of urban canals, Autonomous Surface Vessels (ASV) could play an important role in alleviating the pressure on the road by transporting goods and people through the canals. However, autonomous navigation in narrow and unstructured canals amongst human-operated vessels is still a great challenge. Recently, a sampling-based Interaction-Aware Model Predictive Path Integral (IA-MPPI) control framework was proposed for this purpose. The method pro- vides trajectories for all agents in a local multi-agent system based on their current state and estimated local goals. This thesis builds upon this framework by integrating a learning-based trajectory prediction model to provide better estimates of the local goals of interacting agents. We adapt a state-of-the-art trajectory prediction model and train it on simulated vessel data to predict vessel trajectories. We then provide heuristics to extract local goals from these predictions and integrate them into the IA-MPPI framework. With extensive experiments in simulated environments of Amsterdam’s canals, we show that our proposed method outperforms the baseline in high-interaction scenarios and achieves similar performance to the method with communication capabilities. Furthermore, we provide insights into the benefits of interaction-aware planning over planning with fixed trajectory predictions for other agents’ motion. In additional experiments, we provide heuristics to use the trajectory predictions to improve the sampling distribution of the IA-MPPI. Preliminary results of this method provide insights into the potential benefits of this approach and we suggest directions for future research in this area.