Reasoning-supported Robustness Validation of Automotive E/E Components
- URL: http://arxiv.org/abs/2608.16421v1
- Date: Mon, 17 Aug 2026 11:19:21 GMT
- Title: Reasoning-supported Robustness Validation of Automotive E/E Components
- Abstract summary: This paper presents an ontology-supported approach to tackle the complexity of the Robustness Validation process of automotive electrical/electronic (E/E) components.<n>The approach uses formalized knowledge from the RV process and stress, operating, and load profiles, so-called Mission Profiles (MPs)<n>We show how component characteristics are formalized in OWL in order to form the foundation of an efficient automated analysis selection and decision support during the RV process.
- Score: 39.82496881457754
- License: http://arxiv.org/licenses/nonexclusive-distrib/1.0/
- Abstract: This paper presents an ontology-supported approach to tackle the complexity of the Robustness Validation (RV) process of automotive electrical/electronic (E/E) components. The approach uses formalized knowledge from the RV process and stress, operating, and load profiles, so-called Mission Profiles (MPs). In contrast to the error-prone industrially established manual procedure, we show how component characteristics are formalized in OWL in order to form the foundation of an efficient automated analysis selection and decision support during the RV process. The proposed approach is based on the idea of mapping MPs to an OWL representation so to allow to perform semantic queries against MP data to improve their integration into the RV process. The resulting ontology-supported application framework has been applied to an industrial use-case from automotive power electronics. We present experimental results showing that the RV process can be significantly improved in terms of reduced design time and increased exhaustiveness by automating the analyses selection step and the provisioning of all the relevant data to be used.
Related papers
- AutoREC: A software platform for developing reinforcement learning agents for equivalent circuit model generation from electrochemical impedance spectroscopy data [5.498964035230184]
AutoREC is a Python package for developing reinforcement learning (RL) agents to automatically generate equivalent circuit models (ECMs) from electrochemical impedance spectroscopy (EIS) data.<n> ECMs are a standard framework for interpreting EIS data, but traditional identification is typically based on manual trial-and-error.
arXiv Detail & Related papers (2026-04-29T23:42:27Z) - AutoMIA: Improved Baselines for Membership Inference Attack via Agentic Self-Exploration [49.050688065974605]
Membership Inference Attacks (MIAs) serve as a fundamental auditing tool for evaluating training data leakage in machine learning models.<n>We propose AutoMIA, an agentic framework that reformulates membership inference as an automated process of self-exploration and strategy evolution.
arXiv Detail & Related papers (2026-04-01T15:17:45Z) - DUCTILE: Agentic LLM Orchestration of Engineering Analysis in Product Development Practice [41.99844472131922]
Engineering analysis automation in product development relies on rigid interfaces between tools, data formats and documented processes.<n>This paper presents a DUCTILE (Delegated, User-supervised Coordination of Tool- and document-Integrated LLM-Enabled) agentic orchestration.<n>The agent interprets documented design practices, inspects input data and adapts the processing path, while the engineer supervises and exercises final judgment.
arXiv Detail & Related papers (2026-03-10T22:00:47Z) - EmboCoach-Bench: Benchmarking AI Agents on Developing Embodied Robots [68.29056647487519]
Embodied AI is fueled by high-fidelity simulation and large-scale data collection.<n>However, this scaling capability remains bottlenecked by a reliance on labor-intensive manual oversight.<n>We introduce textscEmboCoach-Bench, a benchmark evaluating the capacity of LLM agents to autonomously engineer embodied policies.
arXiv Detail & Related papers (2026-01-29T11:33:49Z) - Feedback-Driven Tool-Use Improvements in Large Language Models via Automated Build Environments [70.42705564227548]
We propose an automated environment construction pipeline for large language models (LLMs)<n>This enables the creation of high-quality training environments that provide detailed and measurable feedback without relying on external tools.<n>We also introduce a verifiable reward mechanism that evaluates both the precision of tool use and the completeness of task execution.
arXiv Detail & Related papers (2025-08-12T09:45:19Z) - ASIL-Decomposition Based Resource Allocation Optimization for Automotive E/E Architectures [0.4143603294943439]
We present an approach to automatically map software components to available hardware resources.<n>Compared to existing frameworks, our method provides a wider range of safety analyses in compliance with the ISO 26262 standard.<n>We formulate a multi-objective optimization problem to minimize both the development cost and the maximum execution times of critical function chains.
arXiv Detail & Related papers (2025-05-10T15:48:29Z) - AI-Enhanced Business Process Automation: A Case Study in the Insurance Domain Using Object-Centric Process Mining [0.7124736158080938]
This paper presents a case study from the insurance sector, where an LLM was deployed to automate the identification of claim parts.<n>We apply Object-Centric Process Mining (OCPM) to assess the impact of AI-driven automation on process scalability.<n>Our findings indicate that while LLMs significantly enhance operational capacity, they also introduce new process dynamics that require further refinement.
arXiv Detail & Related papers (2025-04-24T06:43:29Z) - Scenario-based Thermal Management Parametrization Through Deep Reinforcement Learning [0.4218593777811082]
This paper introduces a learning-based tuning approach for thermal management functions.
Our deep reinforcement learning agent processes the tuning task context and incorporates an image-based interpretation of embedded parameter sets.
We demonstrate its applicability to a valve controller parametrization task and verify it in real-world vehicle testing.
arXiv Detail & Related papers (2024-08-04T13:19:45Z) - End-to-End Meta-Bayesian Optimisation with Transformer Neural Processes [52.818579746354665]
This paper proposes the first end-to-end differentiable meta-BO framework that generalises neural processes to learn acquisition functions via transformer architectures.
We enable this end-to-end framework with reinforcement learning (RL) to tackle the lack of labelled acquisition data.
arXiv Detail & Related papers (2023-05-25T10:58:46Z) - Benchmarking Automated Machine Learning Methods for Price Forecasting
Applications [58.720142291102135]
We show the possibility of substituting manually created ML pipelines with automated machine learning (AutoML) solutions.
Based on the CRISP-DM process, we split the manual ML pipeline into a machine learning and non-machine learning part.
We show in a case study for the industrial use case of price forecasting, that domain knowledge combined with AutoML can weaken the dependence on ML experts.
arXiv Detail & Related papers (2023-04-28T10:27:38Z)
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.