SDVDiag: A Modular Platform for the Diagnosis of Connected Vehicle Functions

• URL: http://arxiv.org/abs/2507.19403v1
• Date: Fri, 25 Jul 2025 16:09:27 GMT
• Title: SDVDiag: A Modular Platform for the Diagnosis of Connected Vehicle Functions
• Authors: Matthias Weiß, Falk Dettinger, Michael Weyrich,
• Abstract summary: This paper presents SDVDiag, an automated platform for the diagnosis of connected vehicle functions.<n>The platform enables the creation of pipelines that cover all steps from initial data collection to the tracing of potential root causes.<n>It is deployed inside an 5G test fleet environment for connected vehicle functions.
• Score: 0.0
• License: http://creativecommons.org/licenses/by-sa/4.0/
• Abstract: Connected and software-defined vehicles promise to offer a broad range of services and advanced functions to customers, aiming to increase passenger comfort and support autonomous driving capabilities. Due to the high reliability and availability requirements of connected vehicles, it is crucial to resolve any occurring failures quickly. To achieve this however, a complex cloud/edge architecture with a mesh of dependencies must be navigated to diagnose the responsible root cause. As such, manual analyses become unfeasible since they would significantly delay the troubleshooting. To address this challenge, this paper presents SDVDiag, an extensible platform for the automated diagnosis of connected vehicle functions. The platform enables the creation of pipelines that cover all steps from initial data collection to the tracing of potential root causes. In addition, SDVDiag supports self-adaptive behavior by the ability to exchange modules at runtime. Dependencies between functions are detected and continuously updated, resulting in a dynamic graph view of the system. In addition, vital system metrics are monitored for anomalies. Whenever an incident is investigated, a snapshot of the graph is taken and augmented by relevant anomalies. Finally, the analysis is performed by traversing the graph and creating a ranking of the most likely causes. To evaluate the platform, it is deployed inside an 5G test fleet environment for connected vehicle functions. The results show that injected faults can be detected reliably. As such, the platform offers the potential to gain new insights and reduce downtime by identifying problems and their causes at an early stage.

Related papers

• Natural Reflection Backdoor Attack on Vision Language Model for Autonomous Driving [55.96227460521096]
  Vision-Language Models (VLMs) have been integrated into autonomous driving systems to enhance reasoning capabilities.<n>We propose a natural reflection-based backdoor attack targeting VLM systems in autonomous driving scenarios.<n>Our findings uncover a new class of attacks that exploit the stringent real-time requirements of autonomous driving.
  arXiv  Detail & Related papers  (2025-05-09T20:28:17Z)
• Automated Root Cause Analysis System for Complex Data Products [1.7458548956314806]
  We present ARCAS (Automated Root Cause Analysis System), a diagnostic platform built for fast diagnostic implementation and low learning curve.<n>Arcas is composed of a constellation of automated troubleshooting guides (Auto-TSGs) that can execute in parallel to detect issues using product telemetry and apply mitigation in near-real-time.
  arXiv  Detail & Related papers  (2024-12-19T20:10:54Z)
• FaultExplainer: Leveraging Large Language Models for Interpretable Fault Detection and Diagnosis [7.161558367924948]
  This paper presents FaultExplainer, an interactive tool designed to improve fault detection, diagnosis, and explanation in the Tennessee Eastman Process (TEP)<n>FaultExplainer integrates real-time sensor data visualization, Principal Component Analysis (PCA)-based fault detection, and identification of top contributing variables within an interactive user interface powered by large language models (LLMs)<n>We evaluate the LLMs' reasoning capabilities in two scenarios: one where historical root causes are provided, and one where they are not to mimic the challenge of previously unseen faults.
  arXiv  Detail & Related papers  (2024-12-19T03:35:06Z)
• Exploring the Causality of End-to-End Autonomous Driving [57.631400236930375]
  We propose a comprehensive approach to explore and analyze the causality of end-to-end autonomous driving. Our work is the first to unveil the mystery of end-to-end autonomous driving and turn the black box into a white one.
  arXiv  Detail & Related papers  (2024-07-09T04:56:11Z)
• Application of a Dense Fusion Attention Network in Fault Diagnosis of Centrifugal Fan [15.414035116487037]
  This paper discusses embedding distributed attention modules into dense connections instead of traditional dense cascading operations. The proposed dense fusion focuses on the visualization of the network diagnosis process, which increases the interpretability of model diagnosis. Experimental results show that the network has stronger diagnostic performance than other advanced fault diagnostic models.
  arXiv  Detail & Related papers  (2023-11-12T07:09:11Z)
• RCAgent: Cloud Root Cause Analysis by Autonomous Agents with Tool-Augmented Large Language Models [46.476439550746136]
  Large language model (LLM) applications in cloud root cause analysis (RCA) have been actively explored recently. We present RCAgent, a tool-augmented LLM autonomous agent framework for practical and privacy-aware industrial RCA usage. Running on an internally deployed model rather than GPT families, RCAgent is capable of free-form data collection and comprehensive analysis with tools.
  arXiv  Detail & Related papers  (2023-10-25T03:53:31Z)
• PyRCA: A Library for Metric-based Root Cause Analysis [66.72542200701807]
  PyRCA is an open-source machine learning library of Root Cause Analysis (RCA) for Artificial Intelligence for IT Operations (AIOps) It provides a holistic framework to uncover the complicated metric causal dependencies and automatically locate root causes of incidents.
  arXiv  Detail & Related papers  (2023-06-20T09:55:10Z)
• Disentangled Causal Graph Learning for Online Unsupervised Root Cause Analysis [49.910053255238566]
  Root cause analysis (RCA) can identify the root causes of system faults/failures by analyzing system monitoring data. Previous research has mostly focused on developing offline RCA algorithms, which often require manually initiating the RCA process. We propose CORAL, a novel online RCA framework that can automatically trigger the RCA process and incrementally update the RCA model.
  arXiv  Detail & Related papers  (2023-05-18T01:27:48Z)
• Monitoring and Adapting ML Models on Mobile Devices [17.28565076128893]
  We design the first end-to-end system for continuously monitoring and adapting models on mobile devices without requiring feedback from users. Our key observation is that often model degradation is due to a specific root cause, which may affect a large group of devices. We evaluate the system on two computer vision datasets, and show it consistently boosts accuracy compared to existing approaches.
  arXiv  Detail & Related papers  (2023-05-12T21:33:26Z)
• DiffStack: A Differentiable and Modular Control Stack for Autonomous Vehicles [75.43355868143209]
  We present DiffStack, a differentiable and modular stack for prediction, planning, and control. Our results on the nuScenes dataset indicate that end-to-end training with DiffStack yields substantial improvements in open-loop and closed-loop planning metrics.
  arXiv  Detail & Related papers  (2022-12-13T09:05:21Z)
• Causal Scene BERT: Improving object detection by searching for challenging groups of data [125.40669814080047]
  Computer vision applications rely on learning-based perception modules parameterized with neural networks for tasks like object detection. These modules frequently have low expected error overall but high error on atypical groups of data due to biases inherent in the training process. Our main contribution is a pseudo-automatic method to discover such groups in foresight by performing causal interventions on simulated scenes.
  arXiv  Detail & Related papers  (2022-02-08T05:14:16Z)
• Monitoring and Diagnosability of Perception Systems [21.25149064251918]
  We propose a mathematical model for runtime monitoring and fault detection and identification in perception systems. We demonstrate our monitoring system, dubbed PerSyS, in realistic simulations using the LGSVL self-driving simulator and the Apollo Auto autonomy software stack.
  arXiv  Detail & Related papers  (2020-11-11T23:03:14Z)

This list is automatically generated from the titles and abstracts of the papers in this site.

This site does not guarantee the quality of this site (including all information) and is not responsible for any consequences.