# SDVDiag: Using Context-Aware Causality Mining for the Diagnosis of Connected Vehicle Functions

arXiv: https://arxiv.org/abs/2604.03391 · HTML: https://mlsanity.com/paper/2604.03391/

## Key facts

- Cited by 1 papers in the map (data through 14 Oct 2026).
- Builds on 9 of its 22 references that are in the map.
- First posted on arXiv 3 Apr 2026.

## Builds on (most cited first, 9 of 9)

- [Root Cause Analysis for Microservice System based on Causal Inference: How Far Are We?](https://mlsanity.com/paper/2408.13729/) (2024)
- [Failure Diagnosis in Microservice Systems: A Comprehensive Survey and Analysis](https://mlsanity.com/paper/2407.01710/) (2024)
- [A Comprehensive Survey on Root Cause Analysis in (Micro) Services: Methodologies, Challenges, and Trends](https://mlsanity.com/paper/2408.00803/) (2024)
- [SDVDiag: A Modular Platform for the Diagnosis of Connected Vehicle Functions](https://mlsanity.com/paper/2507.19403/) (2025)
- [Revealing Multimodal Causality with Large Language Models](https://mlsanity.com/paper/2509.17784/) (2025)
- [A Survey on Performance, Current and Future Usage of Vehicle-To-Everything Communication Standards](https://mlsanity.com/paper/2410.10264/) (2024)
- [Directives for Function Offloading in 5G Networks Based on a Performance Characteristics Analysis](https://mlsanity.com/paper/2508.03287/) (2025)
- [When Computing follows Vehicles: Decentralized Mobility-Aware Resource Allocation for Edge-to-Cloud Continuum](https://mlsanity.com/paper/2404.13179/) (2024)
- [An OpenSource CI/CD Pipeline for Variant-Rich Software-Defined Vehicles](https://mlsanity.com/paper/2507.19446/) (2025)

## Built on by (most cited first, 1 of 1)

- [SDVDiag: Multimodal Causal Discovery for Online Diagnosis in Software-defined Vehicles](https://mlsanity.com/paper/2606.15559/) (2026)

## Abstract (arXiv)

Real-world implementations of connected vehicle functions are spreading steadily, yet operating these functions reliably remains challenging due to their distributed nature and the complexity of the underlying cloud, edge, and networking infrastructure. Quick diagnosis of problems and understanding the error chains that lead to failures is essential for reducing downtime. However, diagnosing these systems is still largely performed manually, as automated analysis techniques are predominantly data-driven and struggle with hidden relationships and the integration of context information. This paper addresses this gap by introducing a multimodal approach that integrates human feedback and system-specific information into the causal analysis process. Reinforcement Learning from Human Feedback is employed to continuously train a causality mining model while incorporating expert knowledge. Additional modules leverage distributed tracing data to prune false-positive causal links and enable the injection of domain-specific relationships to further refine the causal graph.Evaluation is performed using an automated valet parking application operated in a connected vehicle test field. Results demonstrate a significant increase in precision from 14% to 100% for the detection of causal edges and improved system interpretability compared to purely data-driven approaches, highlighting the potential for system operators in the connected vehicle domain.