TY - GEN
T1 - Virtual Co-Pilot: Multimodal Large Language Model-enabled Quick-access Procedures for Single Pilot Operations
AU - Li, Fan
AU - Feng, Shanshan
AU - Yan, Yuqi
AU - Lee, Ching Hung
AU - Ong, Yew Soon
N1 - Publisher Copyright:
© 2024 IEEE.
PY - 2024
Y1 - 2024
N2 - 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's 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 (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.
AB - 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's 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 (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.
KW - Aviation
KW - human-AI collaboration
KW - large language model
KW - virtual assistant
UR - https://www.scopus.com/pages/publications/85201240767
U2 - 10.1109/CAI59869.2024.00270
DO - 10.1109/CAI59869.2024.00270
M3 - Conference article published in proceeding or book
AN - SCOPUS:85201240767
T3 - Proceedings - 2024 IEEE Conference on Artificial Intelligence, CAI 2024
SP - 1501
EP - 1506
BT - Proceedings - 2024 IEEE Conference on Artificial Intelligence, CAI 2024
PB - Institute of Electrical and Electronics Engineers Inc.
T2 - 2nd IEEE Conference on Artificial Intelligence, CAI 2024
Y2 - 25 June 2024 through 27 June 2024
ER -