用大模型提升汽车架构可维护性，自动化处理更新与兼容性问题。
LLM-Based Approach for Enhancing Maintainability of Automotive Architectures
- 用大模型自动完成硬件抽象、合规检查等任务
- 实现接口兼容性验证与架构修改建议，减少人工干预
- 适合汽车软件开发与系统集成团队参考
当前汽车系统因重构周期长、标准不一、设备与软件组件繁多，导致后期维护、更新和扩展困难。本文探索大语言模型（LLMs）在提升汽车系统灵活性方面的潜力，聚焦三项早期研究案例：1）更新、硬件抽象与合规性处理；2）接口兼容性检查；3）架构修改建议。以OpenAI的GPT-4o模型为原型，验证了大模型在自动化这些关键任务中的可行性，为后续系统维护智能化提供初步实践路径。
原文摘要 · Abstract (English)
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 increasingly difficult, mainly due to long re-engineering, standardization, and compliance procedures, as well as heterogeneity and numerosity of devices and underlying software components involved. In this paper, we explore the potential of Large Language Models (LLMs) when it comes to the automation of tasks and processes that aim to increase the flexibility of automotive systems. Three case studies towards achieving this goal are considered as outcomes of early-stage research: 1) updates, hardware abstraction, and compliance, 2) interface compatibility checking, and 3) architecture modification suggestions. For proof-of-concept implementation, we rely on OpenAI's GPT-4o model.
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