虚拟副驾驶：基于多模态大语言模型的单驾驶员运营快速访问程序
Virtual Co-Pilot: Multimodal Large Language Model-enabled Quick-access Procedures for Single Pilot Operations
AI总结：
本研究提出基于多模态大语言模型的虚拟副驾驶（V-CoP）概念，通过整合飞行员指令与实时驾驶舱数据实现自动化快速程序搜索，在情境分析和程序信息检索中分别达到90.5%和86.5%的准确率，为单驾驶员运营提供安全保障。
摘要：
技术进步、飞行员短缺和成本压力正推动航空业向单驾驶员甚至远程运营发展。考虑到单驾驶员运营带来的巨大工作量和风险，开发虚拟副驾驶（V-CoP）有望成为保障航空安全的潜在途径。本研究提出V-CoP概念，并探讨人类与虚拟助手如何有效协作。通过初步案例研究，探索了V-CoP的关键作用，即利用多模态大语言模型（LLM）实现自动化快速程序搜索。基于LLM的V-CoP整合飞行员指令与实时驾驶舱仪表数据，以提示适用的航空手册和操作程序。结果表明，基于LLM的V-CoP在情境分析中实现了高准确率（90.5%），并有效检索了程序信息（86.5%）。所提出的V-CoP有望为未来虚拟智能助手的开发奠定基础，提升单驾驶员表现并降低航空中的人为错误风险。
英文摘要：
Advancements in technology, pilot shortages, and cost pressures are driving a trend towards single-pilot and even remote operations in aviation. Considering the extensive workload and huge risks associated with single-pilot operations, the development of a Virtual Co-Pilot (V-CoP) is expected to be a potential way to ensure aviation safety. This study proposes a V-CoP concept and explores how humans and virtual assistants can effectively collaborate. A preliminary case study is conducted to explore a critical role of V-CoP, namely automated quick procedures searching, using the multimodal large language model (LLM). The LLM-enabled V-CoP integrates the pilot instruction and real-time cockpit instrumental data to prompt applicable aviation manuals and operation procedures. The results showed that the LLM-enabled V-CoP achieved high accuracy in situational analysis and effective retrieval of procedure information. The results showed that the LLM-enabled V-CoP achieved high accuracy in situational analysis (90.5%) and effective retrieval of procedure information (86.5%). The proposed V-CoP is expected to provide a foundation for future virtual intelligent assistant development, improve the performance of single pilots, and reduce the risk of human errors in aviation.
机构、总结、中文摘要均由AI分析而来，可能存在错误，请以论文正文为准。
