  Autonomous vehicles that operate in urban environments shall comply with
existing rules and reason about the interactions with other decision-making
agents. In this paper, we introduce a decentralized and communication-free
interaction-aware motion planner and apply it to Autonomous Surface Vessels
(ASVs) in urban canals. We build upon a sampling-based method, namely Model
Predictive Path Integral control (MPPI), and employ it to, in each time
instance, compute both a collision-free trajectory for the vehicle and a
prediction of other agents' trajectories, thus modeling interactions. To
improve the method's efficiency in multi-agent scenarios, we introduce a
two-stage sample evaluation strategy and define an appropriate cost function to
achieve rule compliance. We evaluate this decentralized approach in simulations
with multiple vessels in real scenarios extracted from Amsterdam's canals,
showing superior performance than a state-of-the-art trajectory optimization
framework and robustness when encountering different types of agents.
