The paper investigates how Artificial Intelligence can support end‑to‑end automotive design and software development for Software‑Defined Vehicles. It presents an AI‑driven toolchain that combines Large Language Models with Model‑Driven Engineering to convert natural‑language requirements into system architectures, executable software, and validation artifacts, while maintaining traceability, consistency, and safety compliance. Validation was performed on a centralized vehicle computing platform for adaptive cruise control and emergency braking using Hardware‑in‑the‑Loop and Software‑in‑the‑Loop tests, and the authors discuss current capabilities, limitations, and future directions toward more automated, reliable, and quality‑assured automotive software development.
arXiv:2606. 14327v1 Announce Type: cross Abstract: This paper appraises recent frameworks within AI development to integrate LLMs into control tasks in automotive contexts from the perspective of safety assurance.
arXiv:2609.25945v1 Announce Type: new Abstract: The field of Artificial Intelligence has been adopted for many application domains. Vision Language Models are one of the recently advanced AI techniqu...
The paper introduces a coding assistant tailored for safety‑critical automotive software, offering generate, edit, and verify tools via the Model Context Protocol. It customizes a large language model through prompt engineering and fine‑tuning on a dataset of automotive production code linked to requirements and UML behavior. In a two‑week pilot, developers favored the verify tool and the prompt‑engineered model, noting fewer MISRA violations in C and better architectural conformance in C++.
arXiv:2604. 02478v2 Announce Type: replace Abstract: Deep learning models excel at detecting anomaly patterns in normal data.
arXiv:2505. 18334v2 Announce Type: replace-cross Abstract: Past work has demonstrated that autonomous vehicles can drive more safely if they communicate with each other.
SysML v2's textual syntax enables compiler-based validation of model structure and language conformance. However, semantic mistakes that preserve syntactic validity but violate domain rules cannot be detected through compilers.
arXiv:2607. 14387v1 Announce Type: new Abstract: Validating autonomous driving systems requires diverse, regulation-compliant test scenarios.
arXiv:2509.21143v3 Announce Type: replace-cross Abstract: Multimodal models have demonstrated strong capabilities in web, desktop, and mobile GUI environments, but their performance in automotive coc...
arXiv:2607. 14659v1 Announce Type: cross Abstract: Interoperability between heterogeneous modeling tools remains a significant challenge in Model-Driven Engineering (MDE), particularly in the automotive domain where multiple modeling languages, as well as defacto standard proprietary and open-source tools coexist.
AI Control Scientist (AICS) is a large language model–driven agent that automatically generates optimized controllers from language design requirements. It comprises a Task Modeling Agent that translates user needs into engineering constraints, a Controller Design Agent that produces candidate controller structures and code, and a Parameter Tuning Agent that refines parameters to meet closed‑loop performance criteria. Experiments show AICS outperforms existing automated baselines in design success rate and optimization efficiency, enabling the creation of multiple representative control systems.
arXiv:2608. 08281v1 Announce Type: new Abstract: Recently, Large Language Models (LLMs) have shown considerable capability for situational understanding, reasoning, and decision making in different domains, most notable in the automotive sector.
arXiv:2606. 12774v1 Announce Type: cross Abstract: While MPC effectively handles structured, diverse, and low-level specifications, it lacks the capability to dynamically incorporate high-level contextual information such as social norms, user intent, or natural language instructions.