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| Main Authors: | , , , , |
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| Format: | Preprint |
| Published: |
2024
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| Subjects: | |
| Online Access: | https://arxiv.org/abs/2411.05835 |
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| _version_ | 1866915038808244224 |
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| author | Yi, Haozhe Liu, Junyi Yang, Maolin Chen, Zewei Jiang, Xu |
| author_facet | Yi, Haozhe Liu, Junyi Yang, Maolin Chen, Zewei Jiang, Xu |
| contents | Controller Area Networks (CANs) are widely adopted in real-time automotive control and are increasingly standard in factory automation. Considering their critical application in safety-critical systems, The error rate of the system must be accurately predicted and guaranteed. Through simulation, it is possible to obtain a low-precision overview of the system's behavior. However, for low-probability events, the required number of samples in simulation increases rapidly, making it difficult to conduct a sufficient number of simulations in practical applications, and the statistical results may deviate from the actual outcomes. Therefore, a formal analysis is needed to evaluate the error rate of the system. This paper improves the worst-case probability response time analysis by using convolution-based busy-window and backlog techniques under the error retransmission protocol of CANs. Empirical analysis shows that the proposed method improves upon existing methods in terms of accuracy and efficiency. |
| format | Preprint |
| id |
arxiv_https___arxiv_org_abs_2411_05835 |
| institution | arXiv |
| publishDate | 2024 |
| record_format | arxiv |
| spellingShingle | Improved Convolution-Based Analysis for Worst-Case Probability Response Time of CAN Yi, Haozhe Liu, Junyi Yang, Maolin Chen, Zewei Jiang, Xu Systems and Control Controller Area Networks (CANs) are widely adopted in real-time automotive control and are increasingly standard in factory automation. Considering their critical application in safety-critical systems, The error rate of the system must be accurately predicted and guaranteed. Through simulation, it is possible to obtain a low-precision overview of the system's behavior. However, for low-probability events, the required number of samples in simulation increases rapidly, making it difficult to conduct a sufficient number of simulations in practical applications, and the statistical results may deviate from the actual outcomes. Therefore, a formal analysis is needed to evaluate the error rate of the system. This paper improves the worst-case probability response time analysis by using convolution-based busy-window and backlog techniques under the error retransmission protocol of CANs. Empirical analysis shows that the proposed method improves upon existing methods in terms of accuracy and efficiency. |
| title | Improved Convolution-Based Analysis for Worst-Case Probability Response Time of CAN |
| topic | Systems and Control |
| url | https://arxiv.org/abs/2411.05835 |