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.

翻译：在城市环境中运行的自动驾驶车辆必须遵守现有规则，并考虑与其他决策智能体的交互。本文提出了一种无需通信的分布式交互感知运动规划器，并将其应用于城市河道中的自主水面船舶（ASV）。我们基于采样方法——模型预测路径积分控制（MPPI），在每个时间步中同时计算车辆的无碰撞轨迹与其他智能体轨迹的预测，从而建模交互行为。为提升该方法在多人场景中的效率，我们引入两阶段样本评估策略，并定义合适的代价函数以实现规则遵守。我们在从阿姆斯特丹河道提取的真实场景中对多艘船舶进行仿真评估，结果表明该方法在性能上优于现有最先进的轨迹优化框架，并且在遇到不同类型智能体时具有鲁棒性。