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

TL;DR

This paper presents an improved formal analysis method for CAN systems that enhances accuracy and efficiency in predicting worst-case probability response times, crucial for safety-critical automotive and automation applications.

Contribution

It introduces a convolution-based approach with busy-window and backlog techniques for more precise worst-case probability response time analysis under CAN error retransmission protocols.

Findings

Improves accuracy over existing methods

Enhances efficiency in analysis process

Effective for safety-critical CAN applications

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.