5 papers
Multi-Agent Causal Reasoning System for Error Pattern Rule Automation in Vehicles
Hugo Math, Julian Lorenz, Stefan Oelsner +1
Modern vehicles generate thousands of different discrete events known as Diagnostic Trouble Codes (DTCs). Automotive manufacturers use Boolean combinations of these codes, called e…
Transforming Vehicle Diagnostics: A Multimodal Approach to Error Patterns Prediction
Hugo Math, Rainer Lienhart
Accurately diagnosing and predicting vehicle malfunctions is crucial for maintenance and safety in the automotive industry. While modern diagnostic systems primarily rely on sequen…
One-Shot Multi-Label Causal Discovery in High-Dimensional Event Sequences
Hugo Math, Robin Schön, Rainer Lienhart
Understanding causality in event sequences with thousands of sparse event types is critical in domains such as healthcare, cybersecurity, or vehicle diagnostics, yet current method…
Towards Practical Multi-label Causal Discovery in High-Dimensional Event Sequences via One-Shot Graph Aggregation
Hugo Math, Rainer Lienhart
Understanding causality in event sequences where outcome labels such as diseases or system failures arise from preceding events like symptoms or error codes is critical. Yet remain…
Harnessing Event Sensory Data for Error Pattern Prediction in Vehicles: A Language Model Approach
Hugo Math, Rainer Lienhart, Robin Schön
In this paper, we draw an analogy between processing natural languages and processing multivariate event streams from vehicles in order to predict and $\textit{what…