Spectrum quantification-based safety efficiency evaluation of autonomous vehicle under random cut-in scenarios
Continuous-scale trusted safety efficiency evaluation is crucial for the agile development and robust validation of autonomous vehicle intelligence. While the UN R157 Regulation evaluates automated lane-keeping system (ALKS) performance baselines through safe collision plots (SCPs) in various scenar...
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| Format: | Article |
| Language: | English |
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Tsinghua University Press
2024-09-01
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| Series: | Journal of Intelligent and Connected Vehicles |
| Subjects: | |
| Online Access: | https://www.sciopen.com/article/10.26599/JICV.2023.9210035 |
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| _version_ | 1850066075352301568 |
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| author | Jiang Chen Weiwei Zhang Miao Liu Xiaolan Wang Jun Gong Jun Li Boqi Li Jiejie Xu |
| author_facet | Jiang Chen Weiwei Zhang Miao Liu Xiaolan Wang Jun Gong Jun Li Boqi Li Jiejie Xu |
| author_sort | Jiang Chen |
| collection | DOAJ |
| description | Continuous-scale trusted safety efficiency evaluation is crucial for the agile development and robust validation of autonomous vehicle intelligence. While the UN R157 Regulation evaluates automated lane-keeping system (ALKS) performance baselines through safe collision plots (SCPs) in various scenario clusters, quantifying the specific ALKS safety efficiency remains challenging. We propose a spectrum quantification approach to evaluate the safety efficiency of autonomous vehicles in cut-in scenarios. First, we collected speed-distance data under different cut-in scenarios and extracted essential spectral features to indicate the vehicle motion parameters during the cut-in process. Second, by utilizing Fourier analysis, a spectral analysis model was built to quantify and analyze the vehicle motion characteristics, providing insights into scenario safety. Finally, we created approximate analytical equations for the normalized disturbance frequencies in the nonlinear response scenarios of autonomous driving systems by combining the SCP with a frequency spectrum analysis model. The results showed that the normalized disturbance frequency in the cut-in scenario was approximately 0.2. When the relative longitudinal distance and speed of the vehicle are the same, if the cut-in speed of the cut-in vehicle is larger, the normalized disturbance frequency is higher, indicating that the cut-in process of the autonomous vehicle is more dangerous and may trigger a collision. |
| format | Article |
| id | doaj-art-9cb7848e51e445ca8d9f4f2cd57e11fd |
| institution | DOAJ |
| issn | 2399-9802 |
| language | English |
| publishDate | 2024-09-01 |
| publisher | Tsinghua University Press |
| record_format | Article |
| series | Journal of Intelligent and Connected Vehicles |
| spelling | doaj-art-9cb7848e51e445ca8d9f4f2cd57e11fd2025-08-20T02:48:51ZengTsinghua University PressJournal of Intelligent and Connected Vehicles2399-98022024-09-017320521810.26599/JICV.2023.9210035Spectrum quantification-based safety efficiency evaluation of autonomous vehicle under random cut-in scenariosJiang Chen0Weiwei Zhang1Miao Liu2Xiaolan Wang3Jun Gong4Jun Li5Boqi Li6Jiejie Xu7School of Mechanical and Automotive Engineering, Shanghai University of Engineering Science, Shanghai 201620, ChinaShanghai Smart Vehicle Cooperating Innovation Center Co., Ltd., Shanghai 201805, ChinaSchool of Mechanical and Automotive Engineering, Shanghai University of Engineering Science, Shanghai 201620, ChinaSchool of Mechanical and Automotive Engineering, Shanghai University of Engineering Science, Shanghai 201620, ChinaShanghai Smart Vehicle Cooperating Innovation Center Co., Ltd., Shanghai 201805, ChinaShanghai Smart Vehicle Cooperating Innovation Center Co., Ltd., Shanghai 201805, ChinaDepartment of Mechanical Engineering, University of Michigan, Ann Arbor, MI 48109, USAShanghai Smart Vehicle Cooperating Innovation Center Co., Ltd., Shanghai 201805, ChinaContinuous-scale trusted safety efficiency evaluation is crucial for the agile development and robust validation of autonomous vehicle intelligence. While the UN R157 Regulation evaluates automated lane-keeping system (ALKS) performance baselines through safe collision plots (SCPs) in various scenario clusters, quantifying the specific ALKS safety efficiency remains challenging. We propose a spectrum quantification approach to evaluate the safety efficiency of autonomous vehicles in cut-in scenarios. First, we collected speed-distance data under different cut-in scenarios and extracted essential spectral features to indicate the vehicle motion parameters during the cut-in process. Second, by utilizing Fourier analysis, a spectral analysis model was built to quantify and analyze the vehicle motion characteristics, providing insights into scenario safety. Finally, we created approximate analytical equations for the normalized disturbance frequencies in the nonlinear response scenarios of autonomous driving systems by combining the SCP with a frequency spectrum analysis model. The results showed that the normalized disturbance frequency in the cut-in scenario was approximately 0.2. When the relative longitudinal distance and speed of the vehicle are the same, if the cut-in speed of the cut-in vehicle is larger, the normalized disturbance frequency is higher, indicating that the cut-in process of the autonomous vehicle is more dangerous and may trigger a collision.https://www.sciopen.com/article/10.26599/JICV.2023.9210035safety efficiency evaluationautomated lane-keeping system (alks)cut-in scenariofrequency quantify |
| spellingShingle | Jiang Chen Weiwei Zhang Miao Liu Xiaolan Wang Jun Gong Jun Li Boqi Li Jiejie Xu Spectrum quantification-based safety efficiency evaluation of autonomous vehicle under random cut-in scenarios Journal of Intelligent and Connected Vehicles safety efficiency evaluation automated lane-keeping system (alks) cut-in scenario frequency quantify |
| title | Spectrum quantification-based safety efficiency evaluation of autonomous vehicle under random cut-in scenarios |
| title_full | Spectrum quantification-based safety efficiency evaluation of autonomous vehicle under random cut-in scenarios |
| title_fullStr | Spectrum quantification-based safety efficiency evaluation of autonomous vehicle under random cut-in scenarios |
| title_full_unstemmed | Spectrum quantification-based safety efficiency evaluation of autonomous vehicle under random cut-in scenarios |
| title_short | Spectrum quantification-based safety efficiency evaluation of autonomous vehicle under random cut-in scenarios |
| title_sort | spectrum quantification based safety efficiency evaluation of autonomous vehicle under random cut in scenarios |
| topic | safety efficiency evaluation automated lane-keeping system (alks) cut-in scenario frequency quantify |
| url | https://www.sciopen.com/article/10.26599/JICV.2023.9210035 |
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