{"title":"Quantifying the Value of Preview Information for Safety Control","authors":"Zexiang Liu;Necmiye Ozay","doi":"10.1109/TAC.2024.3524462","DOIUrl":null,"url":null,"abstract":"Safety-critical systems, such as autonomous vehicles, often incorporate perception modules that can anticipate upcoming disturbances to system dynamics, expecting that such preview information can improve the performance and safety of the system in complex and uncertain environments. However, there is a lack of formal analysis of the impact of preview information on safety. In this work, we introduce a notion of safety regret, a properly defined difference between the maximal invariant set of a system with finite preview and that of a system with infinite preview, and show that for linear systems, this quantity corresponding to finite-step preview decays exponentially with the preview horizon. Furthermore, algorithms are developed to numerically evaluate the safety regret of a system for different preview horizons. Finally, we demonstrate the established theory and algorithms via multiple examples from different application domains.","PeriodicalId":13201,"journal":{"name":"IEEE Transactions on Automatic Control","volume":"70 7","pages":"4484-4499"},"PeriodicalIF":7.0000,"publicationDate":"2024-12-30","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":"0","resultStr":null,"platform":"Semanticscholar","paperid":null,"PeriodicalName":"IEEE Transactions on Automatic Control","FirstCategoryId":"94","ListUrlMain":"https://ieeexplore.ieee.org/document/10818640/","RegionNum":1,"RegionCategory":"计算机科学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":null,"EPubDate":"","PubModel":"","JCR":"Q1","JCRName":"AUTOMATION & CONTROL SYSTEMS","Score":null,"Total":0}
引用次数: 0
Abstract
Safety-critical systems, such as autonomous vehicles, often incorporate perception modules that can anticipate upcoming disturbances to system dynamics, expecting that such preview information can improve the performance and safety of the system in complex and uncertain environments. However, there is a lack of formal analysis of the impact of preview information on safety. In this work, we introduce a notion of safety regret, a properly defined difference between the maximal invariant set of a system with finite preview and that of a system with infinite preview, and show that for linear systems, this quantity corresponding to finite-step preview decays exponentially with the preview horizon. Furthermore, algorithms are developed to numerically evaluate the safety regret of a system for different preview horizons. Finally, we demonstrate the established theory and algorithms via multiple examples from different application domains.
期刊介绍：
In the IEEE Transactions on Automatic Control, the IEEE Control Systems Society publishes high-quality papers on the theory, design, and applications of control engineering. Two types of contributions are regularly considered:
1) Papers: Presentation of significant research, development, or application of control concepts.
2) Technical Notes and Correspondence: Brief technical notes, comments on published areas or established control topics, corrections to papers and notes published in the Transactions.
In addition, special papers (tutorials, surveys, and perspectives on the theory and applications of control systems topics) are solicited.
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