arrow
返回

Explainable AI for ship collision avoidance: Decoding decision-making processes and behavioral intentions

delete2025-03-01
delete0
delete
OA
AI
H
Hitoshi Yoshioka *
H
Hirotada Hashimoto
DOI:10.1016/j.apor.2025.104471delete
delete原文链接
delete分享
delete收藏
查看原文
摘要

摘要

En 中文
Most ship collision accidents are attributed to human errors. Autonomous navigation technology is heralded as a potential solution to mitigate human error-related collisions. Recent advancements have enabled the application of deep reinforcement learning (DRL) in developing autonomous navigation artificial intelligence (AI). However, the decision-making process of AI is not transparent, and its potential for misjudgment could lead to severe accidents. Consequently, the explainability of DRL-based AI emerges as a critical hurdle in deploying autonomous collision avoidance systems. This study developed an explainable AI for ship collision avoidance. Initially, a critic network composed of sub-task critic networks was proposed to individually evaluate each sub-task to clarify the AI decision-making processes in collision avoidance. Additionally, an attempt was made to discern behavioral intentions through a Q-value analysis and an Attention mechanism. The former focused on interpreting intentions by examining the increment of the Q-value resulting from AI actions, while the latter incorporated the significance of other ships in the decision-making process for collision avoidance into the learning objective. AI's behavioral intentions in collision avoidance were visualized by combining the perceived increment of Q-value with the degree of attention to other ships. The proposed method was evaluated through a numerical experiment. The developed AI was confirmed to be able to safely avoid collisions under various congestion levels, and the decision-making process and behavioral intention of AI for collision avoidance were rendered comprehensible to humans. It is comprehensible to seafarers onboard and could contribute to the future practical implementation of autonomous navigation AI systems.
Keyword:
Collision avoidance
Explainable AI
Deep reinforcement learning
Maritime autonomous surfece ships
AI总结

AI总结

对已上传原文的论文进行重点信息的提取,主要内容包括:简要概述、研究摘要、背景介绍、关键亮点、图文解析、展望与总结。

期刊

Applied Ocean Research 封面图
Applied Ocean Research
IF:
4.4
论文数:
4.2K
被引数:
1.3W

机构

O
Osaka Metropolitan University
学者数:
1.2W
论文数: 9.7K
被引数: 1.6K
引用论文

引用论文

Clinical Effectiveness of Direct Oral Anticoagulants vs Warfarin in Older Patients With Atrial Fibrillation and Ischemic Stroke
err2019-10-01
err0
errOAAI
errYing Xian; Haolin Xu; Emily C. O’Brien; Shreyansh Shah; Laine Thomas; Michael J. Pencina; Gregg C. Fonarow; DaiWai M. Olson; Lee H. Schwamm; Deepak L. Bhatt; Eric E. Smith; Deidre Hannah; Lesley Maisch; Barbara L. Lytle; Eric D. Peterson; Adrian F. Hernandez
err分享
err收藏
err分享
err收藏
err
IF0
err
err0
PREAI
err
err分享
err收藏
err分享
err收藏
err分享
err收藏
学者 查看更多内容