Andreas Rausch

Achieving 90% accuracy in fault localization while providing interpretable explanations could revolutionize root cause analysis in automotive systems.

Coordinated multi-LLM reasoning boosts fault classification accuracy in automotive systems, achieving a remarkable 0.917 Top-1 accuracy while enhancing interpretability.

Generating realistic fault data through HIL simulation could revolutionize how we validate automotive software systems in real time.

Small LLMs can achieve the same fault diagnosis accuracy as larger models, challenging the assumption that bigger is always better in automotive software validation.

Stop blindly trusting your fault detection models: this hybrid CNN-GRU approach uses explainable AI to reveal the reasoning behind its predictions, enabling adaptation and root cause analysis in automotive software validation.