SDVDiag: Multimodal Causal Discovery for Online Diagnosis in Software-defined Vehicles

2024-01-29 · Source: cs.SE updates on arXiv.org · Field: Technology & Digital — Artificial Intelligence & Machine Learning, Robotics & Autonomous Systems, Software Development & Engineering · Depth: Expert, extended

Summary

SDVDiag is a multimodal causal-discovery pipeline designed for online diagnosis in software-defined vehicles (SDVs). It addresses limitations of existing systems by fusing log-based and metric-based service representations into a shared embedding space before causal graph construction. The platform integrates an anomaly-driven trigger, converting it from a manual batch tool into a continuously running online system. A log-processing pipeline compresses raw log streams by 60-62% using Run-Length Encoding and Drain3, then encodes them into 384-dimensional event vectors via a LogBERT-based model. These log embeddings are fused with metric embeddings using an autoencoder. Evaluation on an Autonomous Valet Parking testbed demonstrated that SDVDiag produces sparser causal graphs (134 vs. 182 edges on average) and achieves a 2.4-fold improvement in edge-weighted reward over a metrics-only baseline after 60 human feedback queries. An end-to-end fault injection scenario confirmed its ability to recover a true root cause two causal hops upstream of the observable symptom.

Key takeaway

For MLOps Engineers managing complex software-defined vehicle systems, SDVDiag offers a robust solution for online root cause analysis. You should consider integrating multimodal data sources like logs and metrics to build more accurate causal graphs. This approach significantly reduces diagnostic time by automating incident triggers and precisely pinpointing root causes, even when symptoms are far removed from the origin. Implement continuous human feedback to further refine your diagnostic models.

Key insights

SDVDiag fuses multimodal data and anomaly detection for online, accurate root cause analysis in complex software-defined vehicles.

Principles

• Multimodal data fusion improves causal graph accuracy.
• Anomaly-driven triggers enable continuous online diagnosis.
• Human feedback refines causal graphs effectively.

Method

SDVDiag processes logs via RLE and LogBERT, fuses them with GNN-encoded metrics using an autoencoder, then constructs and refines causal graphs, triggered by anomaly detection.

In practice

• Implement RLE for log compression before encoding.
• Use autoencoders for fusing diverse data modalities.
• Integrate anomaly detection to automate diagnostic workflows.

Topics

• Software-Defined Vehicles
• Causal Root Cause Analysis
• Multimodal Data Fusion
• LogBERT
• Online Diagnosis
• Autonomous Valet Parking

Best for: Research Scientist, AI Scientist, Machine Learning Engineer, MLOps Engineer

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Editorial summary, takeaway, and curation by AIssential. Original article published by cs.SE updates on arXiv.org.