Early fault detection in automotive systems is critical for ensuring user safety and initiating corrective actions. This work proposes an Artificial Intelligence (AI) framework for automotive fault detection. The system monitors Hardware Performance Monitor (HPM) counters to identify anomalous behavior and trigger alerts. By evaluating various fault detection models, the study identifies a neural network-based approach achieving a high detection accuracy of 96%. The impact of inference latency on overall system performance is also assessed.
Data-Driven Simulation Based Fault Detection in Automotive RISC-V Applications
Vinciguerra E.;Russo E.;Palesi M.;Ascia G.
2024-01-01
Abstract
Early fault detection in automotive systems is critical for ensuring user safety and initiating corrective actions. This work proposes an Artificial Intelligence (AI) framework for automotive fault detection. The system monitors Hardware Performance Monitor (HPM) counters to identify anomalous behavior and trigger alerts. By evaluating various fault detection models, the study identifies a neural network-based approach achieving a high detection accuracy of 96%. The impact of inference latency on overall system performance is also assessed.File in questo prodotto:
Non ci sono file associati a questo prodotto.
I documenti in IRIS sono protetti da copyright e tutti i diritti sono riservati, salvo diversa indicazione.
