{"title":"UAV Operations Safety Assessment: A Systematic Literature Review","authors":"Omid Asghari, Naghmeh Ivaki, Henrique Madeira","doi":"10.1145/3723871","DOIUrl":null,"url":null,"abstract":"The significant increase in urban UAVs, due to their benefits and commercial potential, will increase drone density and collision risks. To manage this, Unmanned Aircraft Systems Traffic Management (UTM), European implementation of UTM (U-space), and Air Traffic Management (ATM) are being developed for safe integration with other air traffic. Nonetheless, thorough safety assessments remain essential for ensuring UAV operation safety. In this study, we conducted a two-phase systematic literature review. First, we analyzed existing reviews on UAV operation safety assessments. Second, we examined primary studies with the goal of identifying i) safety assessment approaches, ii) employed methods/techniques, iii) defined and utilized safety metrics, iv) common tools/simulators, and v) stages of safety assessment addressed by each technique in the reviewed studies. As a result, we categorized safety assessment approaches into five groups: 1) Model-based, 2) Analytical-based, 3) Data-driven, 4) Experimental-based, and 5) Hybrid approaches. We found that Monte Carlo simulation and Specific Operations Risk Assessment (SORA) are the most commonly used methods for safety assessment. We identified 42 metrics and classified them into four groups: 1) Collision, 2) Performance, 3) Communication, and 4) Reliability Metrics. Additionally, we identified ten tools/simulators used for safety assessment. Finally, we observed that Stage 5 (safety risk evaluation) of the safety assessment process is the most frequently covered in the studies reviewed.","PeriodicalId":50926,"journal":{"name":"ACM Computing Surveys","volume":"1 1","pages":""},"PeriodicalIF":23.8000,"publicationDate":"2025-03-16","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":"0","resultStr":null,"platform":"Semanticscholar","paperid":null,"PeriodicalName":"ACM Computing Surveys","FirstCategoryId":"94","ListUrlMain":"https://doi.org/10.1145/3723871","RegionNum":1,"RegionCategory":"计算机科学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":null,"EPubDate":"","PubModel":"","JCR":"Q1","JCRName":"COMPUTER SCIENCE, THEORY & METHODS","Score":null,"Total":0}
引用次数: 0
Abstract
The significant increase in urban UAVs, due to their benefits and commercial potential, will increase drone density and collision risks. To manage this, Unmanned Aircraft Systems Traffic Management (UTM), European implementation of UTM (U-space), and Air Traffic Management (ATM) are being developed for safe integration with other air traffic. Nonetheless, thorough safety assessments remain essential for ensuring UAV operation safety. In this study, we conducted a two-phase systematic literature review. First, we analyzed existing reviews on UAV operation safety assessments. Second, we examined primary studies with the goal of identifying i) safety assessment approaches, ii) employed methods/techniques, iii) defined and utilized safety metrics, iv) common tools/simulators, and v) stages of safety assessment addressed by each technique in the reviewed studies. As a result, we categorized safety assessment approaches into five groups: 1) Model-based, 2) Analytical-based, 3) Data-driven, 4) Experimental-based, and 5) Hybrid approaches. We found that Monte Carlo simulation and Specific Operations Risk Assessment (SORA) are the most commonly used methods for safety assessment. We identified 42 metrics and classified them into four groups: 1) Collision, 2) Performance, 3) Communication, and 4) Reliability Metrics. Additionally, we identified ten tools/simulators used for safety assessment. Finally, we observed that Stage 5 (safety risk evaluation) of the safety assessment process is the most frequently covered in the studies reviewed.
期刊介绍:
ACM Computing Surveys is an academic journal that focuses on publishing surveys and tutorials on various areas of computing research and practice. The journal aims to provide comprehensive and easily understandable articles that guide readers through the literature and help them understand topics outside their specialties. In terms of impact, CSUR has a high reputation with a 2022 Impact Factor of 16.6. It is ranked 3rd out of 111 journals in the field of Computer Science Theory & Methods.
ACM Computing Surveys is indexed and abstracted in various services, including AI2 Semantic Scholar, Baidu, Clarivate/ISI: JCR, CNKI, DeepDyve, DTU, EBSCO: EDS/HOST, and IET Inspec, among others.