1 citations · 1 across the 19 of their papers we have counts for
21 papers
Think Inside the Chunk: RegulaRAG for Regulation-Compliant Scenario Generation using LLMs: A Case Study of UN Regulation No. 152
Vahid Zolfaghari, Nenad Petrovic, AndrÉ Schamschurko +1
Generating regulation-compliant test scenarios is essential for validating safety-critical automotive systems, yet Large Language Models (LLMs) struggle to ground outputs in long,…
CyberLLM: A Multi-Agent LLM Framework for Autonomous Detection and Guarded Response in Automotive Cybersecurity
Nenad Petrovic, Oussama Jeddou, Feres Ben Fraj +4
Software-Defined Vehicles (SDVs) expand the automotive attack surface across source code, runtime logs, and deployment topologies, while safety constraints forbid autonomous agents…
Scaling LLM-Driven Multi-Agent Systems: Design Principles and Architectural Scalability Analysis
Linus Sander, Fengjunjie Pan, Vahid Zolfaghari +3
LLM-based multi-agent systems have the potential to enable collective intelligence and scale toward solving highly complex tasks through coordinated ensembles of specialized agents…
LLM-Driven Approach to Modeling Tool Interoperability in Automotive Domain
Nenad Petrovic, Jiajie Zhang, Vahid Zolfaghari +1
Interoperability between heterogeneous modeling tools remains a significant challenge in Model-Driven Engineering (MDE), particularly in the automotive domain where multiple modeli…
Linear Separability of Activation Representations after Supervised Fine-Tuning on Incorrect Responses: A Study of Synthetic Dishonesty in Large Language Models
Vahideh Zolfaghari
When a language model is fine-tuned to produce systematically incorrect responses, does this training leave a structured, linearly recoverable trace in its internal activations? We…
LLM-Empowered Functional Safety and Security by Design in Automotive Systems
Nenad Petrovic, Vahid Zolfaghari, Fengjunjie Pan +1
This paper presents LLM-empowered workflow to support Software Defined Vehicle (SDV) software development, covering the aspects of security-aware system topology design, as well as…
Safety boundary maintenance in consumer AI systems responding to pediatric health queries: a cross-platform benchmark evaluation under naturalistic and adversarially pressured conditions
Vahideh Zolfaghari, Leila Mashhadi, Mitra Ahadi +2
Consumer artificial intelligence chatbots are now accessed by hundreds of millions of users seeking health information, yet systematic evaluation of their safety boundary maintenan…
PediatricAnxietyBench: Evaluating Large Language Model Safety Under Parental Anxiety and Pressure in Pediatric Consultations
Vahideh Zolfaghari
Large language models (LLMs) are increasingly consulted by parents for pediatric guidance, yet their safety under real-world adversarial pressures is poorly understood. Anxious par…
LLM-Empowered Event-Chain Driven Code Generation for ADAS in SDV systems
Nenad Petrovic, Norbert Kroth, Axel Torschmied +9
This paper presents an event-chain-driven, LLM-empowered workflow for generating validated, automotive code from natural-language requirements. A Retrieval-Augmented Generation (RA…
Knowledge-Graph Based RAG System Evaluation Framework
Sicheng Dong, Vahid Zolfaghari, Nenad Petrovic +1
Large language models (LLMs) has become a significant research focus and is utilized in various fields, such as text generation and dialog systems. One of the most essential applic…
Hallucination in LLM-Based Code Generation: An Automotive Case Study
Marc Pavel, Nenad Petrovic, Lukasz Mazur +3
Large Language Models (LLMs) have shown significant potential in automating code generation tasks offering new opportunities across software engineering domains. However, their pra…
GenAI for Automotive Software Development: From Requirements to Wheels
Nenad Petrovic, Fengjunjie Pan, Vahid Zolfaghari +3
This paper introduces a GenAI-empowered approach to automated development of automotive software, with emphasis on autonomous and Advanced Driver Assistance Systems (ADAS) capabili…
Survey of GenAI for Automotive Software Development: From Requirements to Executable Code
Nenad Petrovic, Vahid Zolfaghari, Andre Schamschurko +10
Adoption of state-of-art Generative Artificial Intelligence (GenAI) aims to revolutionize many industrial areas by reducing the amount of human intervention needed and effort for h…
Are requirements really all you need? A case study of LLM-driven configuration code generation for automotive simulations
Krzysztof Lebioda, Nenad Petrovic, Fengjunjie Pan +3
Large Language Models (LLMs) are taking many industries by storm. They possess impressive reasoning capabilities and are capable of handling complex problems, as shown by their ste…
Optimizing Retrieval Augmented Generation for Object Constraint Language
Kevin Chenhao Li, Vahid Zolfaghari, Nenad Petrovic +2
The Object Constraint Language (OCL) is essential for defining precise constraints within Model-Based Systems Engineering (MBSE). However, manually writing OCL rules is complex and…
LLM-enabled Instance Model Generation
Fengjunjie Pan, Nenad Petrovic, Vahid Zolfaghari +2
In the domain of model-based engineering, models are essential components that enable system design and analysis. Traditionally, the creation of these models has been a manual proc…
LLM-based Iterative Approach to Metamodeling in Automotive
Nenad Petrovic, Fengjunjie Pan, Vahid Zolfaghari +1
In this paper, we introduce an automated approach to domain-specific metamodel construction relying on Large Language Model (LLM). The main focus is adoption in automotive domain.…
Multi-modal Summarization in Model-Based Engineering: Automotive Software Development Case Study
Nenad Petrovic, Yurui Zhang, Moaad Maaroufi +5
Multimodal summarization integrating information from diverse data modalities presents a promising solution to aid the understanding of information within various processes. Howeve…
Adopting RAG for LLM-Aided Future Vehicle Design
Vahid Zolfaghari, Nenad Petrovic, Fengjunjie Pan +2
In this paper, we explore the integration of Large Language Models (LLMs) with Retrieval-Augmented Generation (RAG) to enhance automated design and software development in the auto…
Synergy of Large Language Model and Model Driven Engineering for Automated Development of Centralized Vehicular Systems
Nenad Petrovic, Fengjunjie Pan, Krzysztof Lebioda +6
We present a prototype of a tool leveraging the synergy of model driven engineering (MDE) and Large Language Models (LLM) for the purpose of software development process automation…
Towards Single-System Illusion in Software-Defined Vehicles -- Automated, AI-Powered Workflow
Krzysztof Lebioda, Viktor Vorobev, Nenad Petrovic +3
We propose a novel model- and feature-based approach to development of vehicle software systems, where the end architecture is not explicitly defined. Instead, it emerges from an i…