Jinat Ara , Mohammad Badhruddouza Khan , Shamsunnahar Yasmin , Hanif Bhuiyan
{"title":"自动驾驶车辆道路使用者脆弱行为估计研究进展","authors":"Jinat Ara , Mohammad Badhruddouza Khan , Shamsunnahar Yasmin , Hanif Bhuiyan","doi":"10.1080/15568318.2025.2510413","DOIUrl":null,"url":null,"abstract":"<div><div>In recent years, the development of automated support systems for vehicles, focusing on enhancing digital traffic facilities through new methods and techniques has gained immense popularity. However, in real-life scenarios, the developed automated support systems are not completely compatible in terms of ensuring safety, effectiveness, and reliability. A promising number of research have emphasized the importance of integrating vulnerable road users’ (VRUs) behavioral aspects into automated vehicle systems which might help to improve safety, effectiveness, reliability, and community acceptance. Addressing this research focus, this paper presents a comprehensive review focusing on VRU behavior analysis for automated vehicles, specifically examining studies related to VRUs’ behavior aspects. We reviewed 73 studies addressing one research question: What aspects of automated vehicles need attention to improve their safety for VRUs? This review highlights and represents the findings focusing on three major groups: <strong>(I)</strong> VRUs crossing intention prediction, <strong>(II)</strong> VRUs motion prediction, and <strong>(III)</strong> VRUs path/trajectory prediction, concentrating on ten key aspects: (i) Improvement of VRUs context understanding, (ii) Early phase detection and prediction, <em>(iii) Computational/Processing time</em>, <em>(iv) Consistent/Continuous prediction</em>, <em>(v) Interactive navigation</em>, <em>(vi) Semanticity</em>, <em>(vii) Low-scale dataset</em>, <em>(viii) Long-term prediction</em>, <em>(ix) Real-time prediction</em>, and <em>(x) Explainability improvement</em>. Besides, it also addresses several challenges, such as difficulties in observing multiple scenarios, understanding contextual information, and detecting group behavior, which is crucial for enhancing the reliability and acceptability of automated vehicles in future research. The findings of this review are significant to provide new insights and directions for developing driving support systems for automated vehicles by integrating VRUs behavior.</div></div>","PeriodicalId":47824,"journal":{"name":"International Journal of Sustainable Transportation","volume":"19 6","pages":"Pages 547-575"},"PeriodicalIF":3.9000,"publicationDate":"2025-06-03","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":"0","resultStr":"{\"title\":\"A review on the estimation of vulnerable road user behavior for automated vehicles\",\"authors\":\"Jinat Ara , Mohammad Badhruddouza Khan , Shamsunnahar Yasmin , Hanif Bhuiyan\",\"doi\":\"10.1080/15568318.2025.2510413\",\"DOIUrl\":null,\"url\":null,\"abstract\":\"<div><div>In recent years, the development of automated support systems for vehicles, focusing on enhancing digital traffic facilities through new methods and techniques has gained immense popularity. However, in real-life scenarios, the developed automated support systems are not completely compatible in terms of ensuring safety, effectiveness, and reliability. A promising number of research have emphasized the importance of integrating vulnerable road users’ (VRUs) behavioral aspects into automated vehicle systems which might help to improve safety, effectiveness, reliability, and community acceptance. Addressing this research focus, this paper presents a comprehensive review focusing on VRU behavior analysis for automated vehicles, specifically examining studies related to VRUs’ behavior aspects. We reviewed 73 studies addressing one research question: What aspects of automated vehicles need attention to improve their safety for VRUs? This review highlights and represents the findings focusing on three major groups: <strong>(I)</strong> VRUs crossing intention prediction, <strong>(II)</strong> VRUs motion prediction, and <strong>(III)</strong> VRUs path/trajectory prediction, concentrating on ten key aspects: (i) Improvement of VRUs context understanding, (ii) Early phase detection and prediction, <em>(iii) Computational/Processing time</em>, <em>(iv) Consistent/Continuous prediction</em>, <em>(v) Interactive navigation</em>, <em>(vi) Semanticity</em>, <em>(vii) Low-scale dataset</em>, <em>(viii) Long-term prediction</em>, <em>(ix) Real-time prediction</em>, and <em>(x) Explainability improvement</em>. Besides, it also addresses several challenges, such as difficulties in observing multiple scenarios, understanding contextual information, and detecting group behavior, which is crucial for enhancing the reliability and acceptability of automated vehicles in future research. The findings of this review are significant to provide new insights and directions for developing driving support systems for automated vehicles by integrating VRUs behavior.</div></div>\",\"PeriodicalId\":47824,\"journal\":{\"name\":\"International Journal of Sustainable Transportation\",\"volume\":\"19 6\",\"pages\":\"Pages 547-575\"},\"PeriodicalIF\":3.9000,\"publicationDate\":\"2025-06-03\",\"publicationTypes\":\"Journal Article\",\"fieldsOfStudy\":null,\"isOpenAccess\":false,\"openAccessPdf\":\"\",\"citationCount\":\"0\",\"resultStr\":null,\"platform\":\"Semanticscholar\",\"paperid\":null,\"PeriodicalName\":\"International Journal of Sustainable Transportation\",\"FirstCategoryId\":\"5\",\"ListUrlMain\":\"https://www.sciencedirect.com/org/science/article/pii/S1556831825000267\",\"RegionNum\":3,\"RegionCategory\":\"工程技术\",\"ArticlePicture\":[],\"TitleCN\":null,\"AbstractTextCN\":null,\"PMCID\":null,\"EPubDate\":\"\",\"PubModel\":\"\",\"JCR\":\"Q2\",\"JCRName\":\"ENVIRONMENTAL STUDIES\",\"Score\":null,\"Total\":0}","platform":"Semanticscholar","paperid":null,"PeriodicalName":"International Journal of Sustainable Transportation","FirstCategoryId":"5","ListUrlMain":"https://www.sciencedirect.com/org/science/article/pii/S1556831825000267","RegionNum":3,"RegionCategory":"工程技术","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":null,"EPubDate":"","PubModel":"","JCR":"Q2","JCRName":"ENVIRONMENTAL STUDIES","Score":null,"Total":0}
A review on the estimation of vulnerable road user behavior for automated vehicles
In recent years, the development of automated support systems for vehicles, focusing on enhancing digital traffic facilities through new methods and techniques has gained immense popularity. However, in real-life scenarios, the developed automated support systems are not completely compatible in terms of ensuring safety, effectiveness, and reliability. A promising number of research have emphasized the importance of integrating vulnerable road users’ (VRUs) behavioral aspects into automated vehicle systems which might help to improve safety, effectiveness, reliability, and community acceptance. Addressing this research focus, this paper presents a comprehensive review focusing on VRU behavior analysis for automated vehicles, specifically examining studies related to VRUs’ behavior aspects. We reviewed 73 studies addressing one research question: What aspects of automated vehicles need attention to improve their safety for VRUs? This review highlights and represents the findings focusing on three major groups: (I) VRUs crossing intention prediction, (II) VRUs motion prediction, and (III) VRUs path/trajectory prediction, concentrating on ten key aspects: (i) Improvement of VRUs context understanding, (ii) Early phase detection and prediction, (iii) Computational/Processing time, (iv) Consistent/Continuous prediction, (v) Interactive navigation, (vi) Semanticity, (vii) Low-scale dataset, (viii) Long-term prediction, (ix) Real-time prediction, and (x) Explainability improvement. Besides, it also addresses several challenges, such as difficulties in observing multiple scenarios, understanding contextual information, and detecting group behavior, which is crucial for enhancing the reliability and acceptability of automated vehicles in future research. The findings of this review are significant to provide new insights and directions for developing driving support systems for automated vehicles by integrating VRUs behavior.
期刊介绍:
The International Journal of Sustainable Transportation provides a discussion forum for the exchange of new and innovative ideas on sustainable transportation research in the context of environmental, economical, social, and engineering aspects, as well as current and future interactions of transportation systems and other urban subsystems. The scope includes the examination of overall sustainability of any transportation system, including its infrastructure, vehicle, operation, and maintenance; the integration of social science disciplines, engineering, and information technology with transportation; the understanding of the comparative aspects of different transportation systems from a global perspective; qualitative and quantitative transportation studies; and case studies, surveys, and expository papers in an international or local context. Equal emphasis is placed on the problems of sustainable transportation that are associated with passenger and freight transportation modes in both industrialized and non-industrialized areas. All submitted manuscripts are subject to initial evaluation by the Editors and, if found suitable for further consideration, to peer review by independent, anonymous expert reviewers. All peer review is single-blind. Submissions are made online via ScholarOne Manuscripts.