# AI-Driven Safety and Security for UAVs: From Machine Learning to Large Language Models
## 3 AI for UAV Safety
### Lessons Learned  
Section 3 provides an investigation of the significant progress of AI-driven UAV safety applications and discusses the limitations of traditional methods. Table 3 presents the research progress of traditional AI methods in different safety fields, categorizing different AI methods. ML and DL techniques have been widely applied in UAV safety, particularly in relatively stable and well-defined scenarios. Traditional AI models can optimize material design, structural stability, and battery management strategies, improve GPS spoofing detection accuracy, obstacle avoidance ability, and path planning efficiency, and achieve safe flight control. These papers not only highlight the potential of AI technology to improve the safety performance of UAVs but also highlight the limitations of current methods in terms of dataset size, model generalizability, accuracy, and adaptability. \begin{table}
\begin{tabular}{c c c c c c} \hline \hline
**Field** & **Ref.** & **Contributor** & **Year** & **Methods** & **Main Point** \\ \hline \multirow{7}{*}{
\begin{tabular}{c} **Physical** \\ **Safety** \\ **Design** \\ \end{tabular} } & \multirow{7}{*}{[96]} & State Key Laboratory of Nonferrous Metals and Processes & & & & An AdaBoost Regressor-based model predicts AI-Li alloys’ specific modulus and achieves a 4\% increase in maximum specific modulus over the dataset, with Alloy 3 exceeding widely-used 2195-T8 by 12.6\% while maintaining similar specific strength. \\ \cline{2-6}  & \multirow{7}{*}{[99]} & Northwestern Polytechnical University & & & & A flutter test signal feature extraction method combining EMD and CNN-achieves 100\% accuracy on test datasets with fewer training iterations and lower computational complexity. \\ \cline{2-6}  & \multirow{7}{*}{[162]} & Northwestern Polytechnical University & & & & A BPNN-based system identification technology; achieves less error for various wing models, improving design efficiency and safety in the preliminary stage. \\ \cline{2-6}  & & Lawrence Berkeley National Laboratory & 2025 & LLM & A multimodal LLM for materials science; achieves 0.93 accuracy in materials property prediction and 0.09 RMSE in energy above hull tasks. \\ \hline \multirow{2}{*}{[107]} & Harbin Institute of Technology & & & & A collaborative optimization method combining gradient descent and LLMs for prompt tuning; improves performance in NLP and vision-language tasks over traditional methods. \\ \cline{2-6}  & \multirow{2}{*}{[106]} & University of Alicante & & & & A sensor data retrieval method using LLMs converts data to FAIR-compliant formats and creates word embeddings; achieves 0.90 precision and 0.94 MRR. \\ \hline \hline \end{tabular}
\end{table} 
Table 3: Summary of different AI methods in UAV safety. Table 4 outlines the limitations of the traditional AI models in UAV safety. With respect to physical safety design, traditional models simplify nonlinear interactions and cannot fully capture the key factors influencing UAV flight safety. In the field of battery safety management, the difficulty of data acquisition leads to unstable model performance under  
\begin{table}
\begin{tabular}{c c c c c c} \hline \hline
different conditions. Scalability is more prominent in sensor spoofing. In the fields of collision avoidance and path planning, traditional AI models have difficulty dealing with sudden obstacles in dynamic environments and lack a hardware fault response mechanism. With respect to flight control, traditional methods lack sufficient adaptability in complex electromagnetic environments or under malicious attacks. These limitations indicate that although traditional AI models have made some progress in the field of UAV safety, they still need to be improved to meet the increasingly complex UAV safety requirements. To mitigate the limitations of traditional ML and DL models, as shown in Figure 12, LLMs offer more promising application potential in enhancing UAV safety, particularly in more complex and dynamic scenarios, than traditional ML and DL models do.