Artificial intelligence is an enabling technology for autonomous surface vehicles, with methods such as evolutionary algorithms, artificial potential fields, fast marching methods, and many others becoming increasingly popular for solving problems such as path planning and collision avoidance. However, there currently is no unified way to evaluate the performance of different algorithms, for example with regard to safety or risk. This paper is a step in that direction and offers a comparative study of current state-of-the art path planning and collision avoidance algorithms for autonomous surface vehicles. Across 45 selected papers, we compare important performance properties of the proposed algorithms related to the vessel and the environment it is operating in. We also analyse how safety is incorporated, and what components constitute the objective function in these algorithms. Finally, we focus on comparing advantages and limitations of the 45 analysed papers. A key finding is the need for a unified platform for evaluating and comparing the performance of algorithms under a large set of possible real-world scenarios.

人工智能是一种用于自动水面飞行器的使能技术，其具有诸如进化算法，人工势场，快速行进方法之类的方法，而许多其他方法则在解决诸如路径规划和避免碰撞等问题上越来越受欢迎。但是，目前尚没有统一的方法来评估不同算法的性能，例如安全性或风险。本文是朝这个方向迈出的一步，并提供了对当前最先进的自动驾驶车辆路径规划和防撞算法的比较研究。在45篇精选论文中，我们比较了与船舶及其运行环境有关的拟议算法的重要性能。我们还分析了如何整合安全性，在这些算法中什么组成了目标函数。最后，我们专注于比较45篇分析论文的优缺点。一个关键发现是需要一个统一的平台来评估和比较在大量可能的实际场景下算法的性能。