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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…
LLM-Based Approach for Enhancing Maintainability of Automotive Architectures
Nenad Petrovic, Lukasz Mazur, Alois Knoll
There are many bottlenecks that decrease the flexibility of automotive systems, making their long-term maintenance, as well as updates and extensions in later lifecycle phases incr…
GenAI-based test case generation and execution in SDV platform
Denesa Zyberaj, Lukasz Mazur, Nenad Petrovic +5
This paper introduces a GenAI-driven approach for automated test case generation, leveraging Large Language Models and Vision-Language Models to translate natural language requirem…
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…
A Unified Perception-Language-Action Framework for Adaptive Autonomous Driving
Yi Zhang, Erik Leo Haß, Kuo-Yi Chao +4
Autonomous driving systems face significant challenges in achieving human-like adaptability, robustness, and interpretability in complex, open-world environments. These challenges…
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…
Querying Large Automotive Software Models: Agentic vs. Direct LLM Approaches
Lukasz Mazur, Nenad Petrovic, James Pontes Miranda +3
Large language models (LLMs) offer new opportunities for interacting with complex software artifacts, such as software models, through natural language. They present especially pro…
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…
Automating Automotive Software Development: A Synergy of Generative AI and Model-Based Methods
Fengjunjie Pan, Yinglei Song, Long Wen +3
As the automotive industry shifts its focus toward software-defined vehicles, the need for faster and reliable software development continues to grow. However, traditional methods…
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…
RECSIP: REpeated Clustering of Scores Improving the Precision
André Schamschurko, Nenad Petrovic, Alois Christian Knoll
The latest research on Large Language Models (LLMs) has demonstrated significant advancement in the field of Natural Language Processing (NLP). However, despite this progress, ther…
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…