Research on Quantitative Safety Risk Assessment Methods at Vehicle Controllability Level
In recent years, advancements in electrification, intelligent connectivity, and autonomous driving have made functional safety, particularly the vehicle-level controllability, a critical research focus. Although the ISO 26262 standard provides a systematic framework for automotive functional safety,...
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| Format: | Article |
| Language: | English |
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IEEE
2025-01-01
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| Series: | IEEE Access |
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| Online Access: | https://ieeexplore.ieee.org/document/10994430/ |
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| _version_ | 1849320958621384704 |
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| author | Lei He Zichong Li Xuezhu Yang Yuanle Zhou Zikun Qu Zhiqiang Ren |
| author_facet | Lei He Zichong Li Xuezhu Yang Yuanle Zhou Zikun Qu Zhiqiang Ren |
| author_sort | Lei He |
| collection | DOAJ |
| description | In recent years, advancements in electrification, intelligent connectivity, and autonomous driving have made functional safety, particularly the vehicle-level controllability, a critical research focus. Although the ISO 26262 standard provides a systematic framework for automotive functional safety, challenges remain in accurately defining and quantifying controllability levels. In this paper, we analyze the influence of potential faults—such as unintended torque and loss of regenerative braking force—on vehicle controllability under various operating conditions. A database of vehicle attitude data and corresponding controllability levels is established through real-vehicle tests. Feature screening is then performed to identify the most relevant attributes that significantly affect controllability. Based on this database, a controllability model is developed using the Support Vector Machine (SVM) method, which can effectively predict controllability levels. The model is applied in multiple simulation scenarios to assess vehicle controllability under different conditions. Additionally, the safety boundaries of motor torque are determined at varying speeds, providing insights into critical thresholds beyond which the vehicle’s controllability may be compromised. This work presents a novel approach for the quantitative analysis of vehicle controllability and safety risks, offering a reliable tool for evaluating functional safety in modern vehicles. |
| format | Article |
| id | doaj-art-7cb2806545bf46aaa5ecd02d85f85694 |
| institution | Kabale University |
| issn | 2169-3536 |
| language | English |
| publishDate | 2025-01-01 |
| publisher | IEEE |
| record_format | Article |
| series | IEEE Access |
| spelling | doaj-art-7cb2806545bf46aaa5ecd02d85f856942025-08-20T03:49:55ZengIEEEIEEE Access2169-35362025-01-0113825278253910.1109/ACCESS.2025.356850910994430Research on Quantitative Safety Risk Assessment Methods at Vehicle Controllability LevelLei He0https://orcid.org/0000-0003-3020-2984Zichong Li1https://orcid.org/0009-0003-2019-9598Xuezhu Yang2Yuanle Zhou3Zikun Qu4https://orcid.org/0009-0004-4278-0567Zhiqiang Ren5State Key Laboratory of Automotive Simulation and Control, Jilin University, Changchun, Jilin, ChinaState Key Laboratory of Automotive Simulation and Control, Jilin University, Changchun, Jilin, ChinaChina First Automobile Works Group Corporation Ltd., Changchun, Jilin, ChinaState Key Laboratory of Automotive Simulation and Control, Jilin University, Changchun, Jilin, ChinaState Key Laboratory of Automotive Simulation and Control, Jilin University, Changchun, Jilin, ChinaState Key Laboratory of Automotive Simulation and Control, Jilin University, Changchun, Jilin, ChinaIn recent years, advancements in electrification, intelligent connectivity, and autonomous driving have made functional safety, particularly the vehicle-level controllability, a critical research focus. Although the ISO 26262 standard provides a systematic framework for automotive functional safety, challenges remain in accurately defining and quantifying controllability levels. In this paper, we analyze the influence of potential faults—such as unintended torque and loss of regenerative braking force—on vehicle controllability under various operating conditions. A database of vehicle attitude data and corresponding controllability levels is established through real-vehicle tests. Feature screening is then performed to identify the most relevant attributes that significantly affect controllability. Based on this database, a controllability model is developed using the Support Vector Machine (SVM) method, which can effectively predict controllability levels. The model is applied in multiple simulation scenarios to assess vehicle controllability under different conditions. Additionally, the safety boundaries of motor torque are determined at varying speeds, providing insights into critical thresholds beyond which the vehicle’s controllability may be compromised. This work presents a novel approach for the quantitative analysis of vehicle controllability and safety risks, offering a reliable tool for evaluating functional safety in modern vehicles.https://ieeexplore.ieee.org/document/10994430/Automotivecontrollabilityfunctional safetyrisk assessmentsupport vector machines |
| spellingShingle | Lei He Zichong Li Xuezhu Yang Yuanle Zhou Zikun Qu Zhiqiang Ren Research on Quantitative Safety Risk Assessment Methods at Vehicle Controllability Level IEEE Access Automotive controllability functional safety risk assessment support vector machines |
| title | Research on Quantitative Safety Risk Assessment Methods at Vehicle Controllability Level |
| title_full | Research on Quantitative Safety Risk Assessment Methods at Vehicle Controllability Level |
| title_fullStr | Research on Quantitative Safety Risk Assessment Methods at Vehicle Controllability Level |
| title_full_unstemmed | Research on Quantitative Safety Risk Assessment Methods at Vehicle Controllability Level |
| title_short | Research on Quantitative Safety Risk Assessment Methods at Vehicle Controllability Level |
| title_sort | research on quantitative safety risk assessment methods at vehicle controllability level |
| topic | Automotive controllability functional safety risk assessment support vector machines |
| url | https://ieeexplore.ieee.org/document/10994430/ |
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