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arXiv · 2411.05835

Improved Convolution-Based Analysis for Worst-Case Probability Response Time of CAN

Haozhe Yi·Junyi Liu·Maolin Yang·Zewei Chen·Xu Jiang
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Abstract

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.

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Haozhe Yi, Junyi Liu, Maolin Yang, Zewei Chen, Xu Jiang. 2024-11-28. Improved Convolution-Based Analysis for Worst-Case Probability Response Time of CAN. https://arxiv.org/abs/2411.05835

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