Jiongjiong Liu , Jinfen Zhang , Zaili Yang , Mingyang Zhang , Wuliu Tian
{"title":"A game-based decision-making method for multi-ship collaborative collision avoidance reflecting risk attitudes in open waters","authors":"Jiongjiong Liu , Jinfen Zhang , Zaili Yang , Mingyang Zhang , Wuliu Tian","doi":"10.1016/j.ocecoaman.2024.107450","DOIUrl":null,"url":null,"abstract":"<div><div>To accurately reflect risk attitudes towards ship intentions in multi-ship encounters, this paper develops a novel two-stage collaborative collision avoidance decision-making (CADM) model by incorporating intention prediction and real-time decision-making. We acquire prior knowledge of risk attitudes by analyzing Automatic Identification System (AIS) data and further estimate the probability distributions of encountering ship's risk attitude using Bayesian reasoning. By treating collision avoidance procedure as a static game with incomplete information, a predictive model for collision avoidance intentions is developed by taking account into risk attitude probabilities. Real-time decisions are then implemented according to different stages, and a collaborative CADM model is established by a game-decision cycle. Finally, a multi-ship encounter scenario is simulated under all combinations of risk attitudes, and the results are compared with those obtained under complete information. The results demonstrate that the proposed model can formulate avoidance actions that meet safety requirements under all combinations of risk attitudes. Further comparison with complete information proves the effectiveness of the risk attitude probability model, which is conducive to improving the decision-making flexibility and reducing complexity. The research findings enhance the collaborative decision-making, contributing to the development of autonomous navigation in open waters.</div></div>","PeriodicalId":54698,"journal":{"name":"Ocean & Coastal Management","volume":"259 ","pages":"Article 107450"},"PeriodicalIF":4.8000,"publicationDate":"2024-10-23","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":"0","resultStr":null,"platform":"Semanticscholar","paperid":null,"PeriodicalName":"Ocean & Coastal Management","FirstCategoryId":"93","ListUrlMain":"https://www.sciencedirect.com/science/article/pii/S0964569124004356","RegionNum":2,"RegionCategory":"环境科学与生态学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":null,"EPubDate":"","PubModel":"","JCR":"Q1","JCRName":"OCEANOGRAPHY","Score":null,"Total":0}
引用次数: 0
Abstract
To accurately reflect risk attitudes towards ship intentions in multi-ship encounters, this paper develops a novel two-stage collaborative collision avoidance decision-making (CADM) model by incorporating intention prediction and real-time decision-making. We acquire prior knowledge of risk attitudes by analyzing Automatic Identification System (AIS) data and further estimate the probability distributions of encountering ship's risk attitude using Bayesian reasoning. By treating collision avoidance procedure as a static game with incomplete information, a predictive model for collision avoidance intentions is developed by taking account into risk attitude probabilities. Real-time decisions are then implemented according to different stages, and a collaborative CADM model is established by a game-decision cycle. Finally, a multi-ship encounter scenario is simulated under all combinations of risk attitudes, and the results are compared with those obtained under complete information. The results demonstrate that the proposed model can formulate avoidance actions that meet safety requirements under all combinations of risk attitudes. Further comparison with complete information proves the effectiveness of the risk attitude probability model, which is conducive to improving the decision-making flexibility and reducing complexity. The research findings enhance the collaborative decision-making, contributing to the development of autonomous navigation in open waters.
期刊介绍:
Ocean & Coastal Management is the leading international journal dedicated to the study of all aspects of ocean and coastal management from the global to local levels.
We publish rigorously peer-reviewed manuscripts from all disciplines, and inter-/trans-disciplinary and co-designed research, but all submissions must make clear the relevance to management and/or governance issues relevant to the sustainable development and conservation of oceans and coasts.
Comparative studies (from sub-national to trans-national cases, and other management / policy arenas) are encouraged, as are studies that critically assess current management practices and governance approaches. Submissions involving robust analysis, development of theory, and improvement of management practice are especially welcome.