利用图模型进行复杂性分析,以解决复杂情况下自动驾驶船舶的冲突

Azzeddine Bakdi, Erik Vanem
{"title":"利用图模型进行复杂性分析,以解决复杂情况下自动驾驶船舶的冲突","authors":"Azzeddine Bakdi, Erik Vanem","doi":"10.1115/1.4066198","DOIUrl":null,"url":null,"abstract":"\n Maritime Autonomous Surface Ships (MASSs) will reshape the fast-evolving ecosystem for their attractive socio-economic benefits and potential to improve safety. However, their new systems and technology need thorough verification to identify unintended components of risk. The interaction between MASS cyber-physical systems and the existing regulatory framework is currently unpredictable; AI-powered intelligent situation awareness and autonomous navigation algorithms must safely and efficiently adhere to the regulations which are only designed for human interpretation without MASSs consideration. This paper contributes to algorithmic regulations and particularly algorithmic COLREGs in real-world MASS applications. It focuses on codifying COLREGs into a machine-executable system applicable to MASSs, then analyzing their performance in dynamic and mixed interactions between multiple vessels in complex scenarios. Based on fullest pairwise COLREGs criteria, this paper considers Decision-Making (DM) and complexity analysis in multi-collision-conflict scenarios. Complexity influential factors are an interplay between the characteristics of COLREGs, traffic scenarios, MASS interactions, and the environment. Participant vessels are the decision-makers forming a decentralized uncertain DM process, casted into a multi-participant multi-conflict multi-criteria DM problem. This is tackled through the technique of graph models for conflict resolution, using risk graph models and fuzzy preferences over alternative collision-avoidance states. The presented work is validated on a database of historical scenarios extracted from multiple data sources.","PeriodicalId":509714,"journal":{"name":"Journal of Offshore Mechanics and Arctic Engineering","volume":null,"pages":null},"PeriodicalIF":0.0000,"publicationDate":"2024-08-09","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":"0","resultStr":"{\"title\":\"Complexity analysis using graph models for conflict resolution for autonomous ships in complex situations\",\"authors\":\"Azzeddine Bakdi, Erik Vanem\",\"doi\":\"10.1115/1.4066198\",\"DOIUrl\":null,\"url\":null,\"abstract\":\"\\n Maritime Autonomous Surface Ships (MASSs) will reshape the fast-evolving ecosystem for their attractive socio-economic benefits and potential to improve safety. However, their new systems and technology need thorough verification to identify unintended components of risk. The interaction between MASS cyber-physical systems and the existing regulatory framework is currently unpredictable; AI-powered intelligent situation awareness and autonomous navigation algorithms must safely and efficiently adhere to the regulations which are only designed for human interpretation without MASSs consideration. This paper contributes to algorithmic regulations and particularly algorithmic COLREGs in real-world MASS applications. It focuses on codifying COLREGs into a machine-executable system applicable to MASSs, then analyzing their performance in dynamic and mixed interactions between multiple vessels in complex scenarios. Based on fullest pairwise COLREGs criteria, this paper considers Decision-Making (DM) and complexity analysis in multi-collision-conflict scenarios. Complexity influential factors are an interplay between the characteristics of COLREGs, traffic scenarios, MASS interactions, and the environment. Participant vessels are the decision-makers forming a decentralized uncertain DM process, casted into a multi-participant multi-conflict multi-criteria DM problem. This is tackled through the technique of graph models for conflict resolution, using risk graph models and fuzzy preferences over alternative collision-avoidance states. The presented work is validated on a database of historical scenarios extracted from multiple data sources.\",\"PeriodicalId\":509714,\"journal\":{\"name\":\"Journal of Offshore Mechanics and Arctic Engineering\",\"volume\":null,\"pages\":null},\"PeriodicalIF\":0.0000,\"publicationDate\":\"2024-08-09\",\"publicationTypes\":\"Journal Article\",\"fieldsOfStudy\":null,\"isOpenAccess\":false,\"openAccessPdf\":\"\",\"citationCount\":\"0\",\"resultStr\":null,\"platform\":\"Semanticscholar\",\"paperid\":null,\"PeriodicalName\":\"Journal of Offshore Mechanics and Arctic Engineering\",\"FirstCategoryId\":\"1085\",\"ListUrlMain\":\"https://doi.org/10.1115/1.4066198\",\"RegionNum\":0,\"RegionCategory\":null,\"ArticlePicture\":[],\"TitleCN\":null,\"AbstractTextCN\":null,\"PMCID\":null,\"EPubDate\":\"\",\"PubModel\":\"\",\"JCR\":\"\",\"JCRName\":\"\",\"Score\":null,\"Total\":0}","platform":"Semanticscholar","paperid":null,"PeriodicalName":"Journal of Offshore Mechanics and Arctic Engineering","FirstCategoryId":"1085","ListUrlMain":"https://doi.org/10.1115/1.4066198","RegionNum":0,"RegionCategory":null,"ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":null,"EPubDate":"","PubModel":"","JCR":"","JCRName":"","Score":null,"Total":0}
引用次数: 0

摘要

海上自主水面舰艇(MASSs)将重塑快速发展的生态系统,带来诱人的社会经济效益和提高安全性的潜力。然而,它们的新系统和新技术需要彻底验证,以识别意外风险因素。目前,MASS 网络物理系统与现有监管框架之间的互动是不可预测的;人工智能驱动的智能态势感知和自主导航算法必须安全、高效地遵守仅为人类解释而设计、不考虑 MASS 的法规。本文将对算法法规,特别是真实世界 MASS 应用中的算法 COLREGs 做出贡献。本文的重点是将 COLREGs 编纂成适用于 MASS 的机器可执行系统,然后分析其在复杂场景下多船动态混合互动中的性能。基于最完整的成对 COLREGs 标准,本文考虑了多碰撞冲突场景中的决策(DM)和复杂性分析。复杂性影响因素是 COLREGs 特性、交通场景、MASS 相互作用和环境之间的相互作用。参与船只是决策者,形成一个分散的不确定 DM 流程,并将其转化为一个多参与者多冲突多标准 DM 问题。该问题通过图模型技术解决冲突,使用风险图模型和对备选避碰状态的模糊偏好。所介绍的工作在从多个数据源提取的历史情景数据库中得到了验证。
本文章由计算机程序翻译,如有差异,请以英文原文为准。
Complexity analysis using graph models for conflict resolution for autonomous ships in complex situations
Maritime Autonomous Surface Ships (MASSs) will reshape the fast-evolving ecosystem for their attractive socio-economic benefits and potential to improve safety. However, their new systems and technology need thorough verification to identify unintended components of risk. The interaction between MASS cyber-physical systems and the existing regulatory framework is currently unpredictable; AI-powered intelligent situation awareness and autonomous navigation algorithms must safely and efficiently adhere to the regulations which are only designed for human interpretation without MASSs consideration. This paper contributes to algorithmic regulations and particularly algorithmic COLREGs in real-world MASS applications. It focuses on codifying COLREGs into a machine-executable system applicable to MASSs, then analyzing their performance in dynamic and mixed interactions between multiple vessels in complex scenarios. Based on fullest pairwise COLREGs criteria, this paper considers Decision-Making (DM) and complexity analysis in multi-collision-conflict scenarios. Complexity influential factors are an interplay between the characteristics of COLREGs, traffic scenarios, MASS interactions, and the environment. Participant vessels are the decision-makers forming a decentralized uncertain DM process, casted into a multi-participant multi-conflict multi-criteria DM problem. This is tackled through the technique of graph models for conflict resolution, using risk graph models and fuzzy preferences over alternative collision-avoidance states. The presented work is validated on a database of historical scenarios extracted from multiple data sources.
求助全文
通过发布文献求助,成功后即可免费获取论文全文。 去求助
来源期刊
自引率
0.00%
发文量
0
×
引用
GB/T 7714-2015
复制
MLA
复制
APA
复制
导出至
BibTeX EndNote RefMan NoteFirst NoteExpress
×
提示
您的信息不完整,为了账户安全,请先补充。
现在去补充
×
提示
您因"违规操作"
具体请查看互助需知
我知道了
×
提示
确定
请完成安全验证×
copy
已复制链接
快去分享给好友吧!
我知道了
右上角分享
点击右上角分享
0
联系我们:info@booksci.cn Book学术提供免费学术资源搜索服务,方便国内外学者检索中英文文献。致力于提供最便捷和优质的服务体验。 Copyright © 2023 布克学术 All rights reserved.
京ICP备2023020795号-1
ghs 京公网安备 11010802042870号
Book学术文献互助
Book学术文献互助群
群 号:481959085
Book学术官方微信