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| DC 欄位 | 值 | 語言 |
|---|---|---|
| dc.contributor.advisor | 詹魁元 | zh_TW |
| dc.contributor.advisor | Kuei-Yuan Chan | en |
| dc.contributor.author | 劉怡葶 | zh_TW |
| dc.contributor.author | YI-TING LIU | en |
| dc.date.accessioned | 2024-08-14T16:36:19Z | - |
| dc.date.available | 2024-08-15 | - |
| dc.date.copyright | 2024-08-13 | - |
| dc.date.issued | 2024 | - |
| dc.date.submitted | 2024-08-09 | - |
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| dc.identifier.uri | http://tdr.lib.ntu.edu.tw/jspui/handle/123456789/94083 | - |
| dc.description.abstract | 本研究提出一種使用故障注入和傷害等級模型來評估自動煞車系統(AEB)風險的方法。研究的主要目標是建立一套針對先進駕駛輔助系統/自動駕駛系統的量化風險評估方法。研究方法包括構建AEB系統和故障模型，其中包括感測器、控制器和通訊網絡的故障注入模型；採用基於系統理論過程分析(STPA)的策略性故障注入方法，以提高危害事件觸發效率；在歐盟新車安全評鑑協會之測試場景下進行模擬，包括行人縱向行走和後車追尾兩種情況；使用擴展Delta-V估算速度變化量，並結合過往研究和新開發的傷害等級模型，評估危害事件的嚴重性和可控性。
主要研究結果顯示，基於STPA的故障注入方法能觸發更多危害事件，且形成更危險的情況。通訊網絡延遲、控制器運算位元翻轉和運算延遲是導致最多危害事件的三種故障類型。使用機器學習方法(如隨機森林、自適應增強和堆疊模型)開發的新傷害等級模型表現較好。大多數單一故障導致的危害事件嚴重程度為無傷害(AIS 0)。在有人為操作的情況下，大多數危害事件被評估為完全可控(C0)。 本研究為AEB系統的風險評估提供了一種新的量化方法，可幫助提高系統安全性。未來研究可進一步改進傷害等級模型和擴展到更多場景，以更全面地評估自動駕駛系統的安全性。 | zh_TW |
| dc.description.abstract | This study proposes a method for evaluating the risks of Automated Emergency Braking (AEB) systems using fault injection and injury severity models. The main objective is to establish a quantitative risk assessment approach for advanced driver assistance systems and autonomous driving systems. The research methodology includes constructing AEB system and fault models, encompassing fault injection models for sensors, controllers, and communication networks; adopting a strategic fault injection method based on System-Theoretic Process Analysis (STPA) to enhance the efficiency of hazard event triggering; conducting simulations in Euro NCAP test scenarios, including pedestrian walking and rear-end collision situations; and using extended Delta-V to estimate velocity changes, combined with existing research and newly developed injury severity models to assess the severity and controllability of hazard events.
The main findings indicate that the STPA-based fault injection method triggers more hazard events and creates more dangerous situations. Communication network delays, controller bit-flip errors, and computational delays are the three fault types causing the most hazard events. New injury severity models developed using machine learning methods (such as Random Forest, AdaBoost, and Stacking Classifier) show better performance. Most single faults lead to hazard events with no injuries (AIS 0). In scenarios with human operation, most hazard events are assessed as controllable in general (C0). This study provides a new quantitative method for risk assessment of AEB systems, which can help improve system safety. Future research could further improve the injury severity models and extend the approach to more scenarios, enabling a more comprehensive safety assessment of autonomous driving systems. | en |
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| dc.description.provenance | Made available in DSpace on 2024-08-14T16:36:19Z (GMT). No. of bitstreams: 0 | en |
| dc.description.tableofcontents | 口試委員會審定書 i
誌謝 ii 中文摘要 iv Abstract v 目次 vii 圖次 ix 表次 xi 第一章 緒論 1 1.1 研究背景 1 1.2 研究動機與目的 2 1.3 論文架構 3 第二章 文獻回顧 4 2.1 自動駕駛組件常見之故障現象 4 2.2 ISO 26262 道路車輛功能安全國際標準概述 7 2.2.1 簡介 7 2.2.2 汽車安全完整性等級 8 2.3 危害分析與風險評估 10 2.3.1 危害分析方法 10 2.3.2 風險評估方法 12 2.4 故障注入方法 19 2.5 小結 20 第三章 研究方法 22 3.1 系統模型建構 23 3.1.1 系統架構 23 3.1.2 車輛動態與道路環境模型 23 3.1.3 系統控制策略 28 3.1.4 故障注入模型 31 3.2 基於系統理論過程分析的故障注入方法 34 3.2.1 系統理論過程危害分析 34 3.2.2 基於STPA的故障注入方法 35 3.3 模擬流程說明 39 3.4 危害事件風險評估 42 3.4.1 速度變化量之估計方法 42 3.4.2 傷害等級模型建構 43 3.4.3 危害事件嚴重型和可控性評估 58 3.5 小結 60 第四章 模擬結果與討論 61 4.1 故障注入方法之比較 61 4.2 碰撞速度變化量估計結果 70 4.3 傷害等級模型效能評估 70 4.4 危害事件風險評估 75 4.4.1 危害事件的嚴重性評估 75 4.4.2 危害事件之可控性評估 81 4.5 失效對策方法探討 86 第五章 結論與未來工作 87 5.1 研究成果與貢獻 87 5.2 未來工作 88 參考文獻 89 附錄A 附錄 100 A.1 NASS/CDS 資料資訊 100 | - |
| dc.language.iso | zh_TW | - |
| dc.subject | 自動煞車系統 | zh_TW |
| dc.subject | 故障注入 | zh_TW |
| dc.subject | 基於機器學習之傷害等級模型 | zh_TW |
| dc.subject | Delta-V | zh_TW |
| dc.subject | 危害事件嚴重性與可控性量化評估方法 | zh_TW |
| dc.subject | machine learning-based injury severity model | en |
| dc.subject | quantitative assessment of hazard event severity and controllability | en |
| dc.subject | Automated Emergency Braking (AEB) system | en |
| dc.subject | fault injection | en |
| dc.subject | Delta-V | en |
| dc.title | 以故障注入與傷害等級模型評估自動煞車系統之風險 | zh_TW |
| dc.title | Risk Assessment of Automated Braking Systems Using Fault Injection and Injury Severity Models | en |
| dc.type | Thesis | - |
| dc.date.schoolyear | 112-2 | - |
| dc.description.degree | 碩士 | - |
| dc.contributor.oralexamcommittee | 吳文方;蘇偉儁 | zh_TW |
| dc.contributor.oralexamcommittee | Wen-Fang Wu;Wei-Jiun Su | en |
| dc.subject.keyword | 自動煞車系統,故障注入,基於機器學習之傷害等級模型,Delta-V,危害事件嚴重性與可控性量化評估方法, | zh_TW |
| dc.subject.keyword | Automated Emergency Braking (AEB) system,fault injection,machine learning-based injury severity model,Delta-V,quantitative assessment of hazard event severity and controllability, | en |
| dc.relation.page | 101 | - |
| dc.identifier.doi | 10.6342/NTU202403590 | - |
| dc.rights.note | 同意授權(限校園內公開) | - |
| dc.date.accepted | 2024-08-12 | - |
| dc.contributor.author-college | 工學院 | - |
| dc.contributor.author-dept | 工業工程學研究所 | - |
| 顯示於系所單位： | 工業工程學研究所 | |
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|---|---|---|---|
| ntu-112-2.pdf 授權僅限NTU校內IP使用（校園外請利用VPN校外連線服務） | 20.24 MB | Adobe PDF |
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