GPU-Accelerated Guided Heuristic Sampling for Residual Error Probability Analysis in CAN FD Communication

Abstract

The deployment of automated driving features demands stringent compliance with ISO 26262, particularly concerning data integrity over in-vehicle networks. This study effectively addresses the computational challenges of quantifying the residual error probability of Classical CAN and CAN FD communication under high-order fault profiles. By introducing a GPU-accelerated residual error analysis framework utilizing OpenCL, it overcomes the mathematical barriers of traditional brute-force simulation, elevating execution speeds from 150,000 to 4.2 million iterations per second. Empirical evaluations demonstrate that native data-link layer CRC protection may be bypassed under specific multi-bit physical-layer corruption patterns involving stuff-bit cascading effects. Conversely, for the evaluated application-layer End-to-End (E2E) protected configurations, no residual errors were observed. However, from a functional safety management perspective, these observations must be interpreted within the scope of the investigated configurations: the localized payload regions between Bytes 12 and 16 exhibiting an elevated susceptibility to masking failures apply exclusively to the investigated radar payload structures and selected real-world BLF traces. Because these masking failures are highly dependent on the evaluated frame structures and specific fault-injection scenarios, the conclusions should be limited accordingly to the evaluated configurations.

The authors' abstract, as published at the source. Future Transportation, 2026 · DOI ↗