Unmanned aerial vehicle cruise risk identification technology based on multi-source data and large models

MA Tao; WU Jun; TANG Fan-long; FAN Jian-wei; WANG Ning

doi:10.19818/j.cnki.1671-1637.2026.036

Volume 26 Issue 3

Mar. 2026

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Article Navigation > Journal of Traffic and Transportation Engineering > 2026 > 26(3): 75-88

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MA Tao, WU Jun, TANG Fan-long, FAN Jian-wei, WANG Ning. Unmanned aerial vehicle cruise risk identification technology based on multi-source data and large models[J]. Journal of Traffic and Transportation Engineering, 2026, 26(3): 75-88. doi: 10.19818/j.cnki.1671-1637.2026.036

Citation:

MA Tao, WU Jun, TANG Fan-long, FAN Jian-wei, WANG Ning. Unmanned aerial vehicle cruise risk identification technology based on multi-source data and large models[J]. Journal of Traffic and Transportation Engineering, 2026, 26(3): 75-88. doi: 10.19818/j.cnki.1671-1637.2026.036

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Unmanned aerial vehicle cruise risk identification technology based on multi-source data and large models

doi: 10.19818/j.cnki.1671-1637.2026.036

1.
School of Transportation, Southeast University, Nanjing 211189, Jiangsu, China
2.
School of Network and Communication Engineering, Jinling Institute of Technology, Nanjing 211169, Jiangsu, China
3.
College of Civil Engineering, Nanjing Forestry University, Nanjing 210037, Jiangsu, China

Funds:

National Key R&D Program of China 2020YFB1600102

National Natural Science Foundation of China 52378445

Xizang Autonomous Region Science and Technology Funding XZ202501JX0006

Foundation of Jinling Institute of Technology jit-b-202401

More Information

Corresponding author: TANG Fan-long, lecturer, PhD, E-mail: tangfanlong@jit.edu.cn
Received Date: 2025-07-03
Accepted Date: 2025-09-26
Rev Recd Date: 2025-08-19
Publish Date: 2026-03-28

Abstract

Abstract

To identify complex risk events during the cruise of unmanned aerial vehicles (UAVs), the basic elements of UAV cruise risks were explored, and characteristic parameters required for prompt were specified. The implementation methods, architectures, and typical models of multimodal large models were analyzed, and a scheme for integrating multi-source data in the prompt generation model was proposed. By combining environmental perception, detection, identification and tracking methods, a prompt generation model integrating with macroscopic scene description, dynamic scene supplementation, and sudden risk detection was established. The extracted feature parameters were then integrated into the prompt. The UAV cruise risk identification and judgment were completed through DeepSeek's comprehensive analysis. Research results show that the three modules can quickly complete the identification of UAV cruise risks and obtain complete prompts. The static scene description based on the Owl-ViT model can effectively identify static obstacles during flight, with confidence exceeding 80%. The dynamic object capture based on the ByteTrack algorithm can quickly obtain dynamic information such as the distance, speed, and coordinates of flying birds and other UAVs. The sudden risk identification based on point clouds can capture point cloud obstacle information, including the distance, size, volume, and aspect ratio of the target, and can quickly detect obstacles that suddenly enter the safe area. The output results of DeepSeek generated by the prompt can detail the risk content and level during the cruise, and provide safety suggestions. The developed UAV cruise risk identification system can visualize the perception and identification data and determine the device and task information for the tasks, further assisting DeepSeek in risk judgment. The research results can provide effective technical support for risk identification during UAV cruise as well as safe and efficient flight.
- low-altitude traffic,
- UAV,
- large model,
- cruise risk identification technology,
- prompt word generation model,
- multi-source data

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References

[1]	HU Wen-xiao, MU Di, LI Zhi, et al. Research on counter-measures for promoting the development of low-altitude economy through innovation in key technical issues[J/OL]. Acta Aeronautica et Astronautica Sinica, 2024, https://doi.org/10.7527/S1000-6893.2024.31539.
[2]	JIA Yong-nan. A scheme for unmanned aerial system traffic management in low-altitude airspace[J]. Acta Aeronautica et Astronautica Sinica, 2025, 46(11): 114-140.
[3]	ZHANG Xue-jun, LI Cheng-long, ZHANG Zhi-yuan, et al. Constructing in-time risk management capabilities for low-altitude aviation systems: Concepts, technologies, and challenges[J]. Acta Aeronautica et Astronautica Sinica, 2025, 46(11): 8-34.
[4]	LIN Jia-mei-lin, HUANG Long-yang. Unmanned aerial system (UAS) operation risk assessment and risk mitigation research[J]. Ship Electronic Engineering, 2024, 44(11): 156-160.
[5]	YU Li-shen. On government regulation and legal protection in the orderly development of low-alti-tude economy[J]. Jiangxi Social Sciences, 2025, 45(3): 33-47, 206.
[6]	ASWINI N, KUMAR E K, UMA S V. UAV and obstacle sensing techniques—a perspective[J]. International Journal of Intelligent Unmanned Systems, 2018, 6(1): 32-46. doi: 10.1108/IJIUS-11-2017-0013
[7]	SKARKA W, ASHFAQ R. Hybrid machine learning and reinforcement learning framework for adaptive UAV obstacle avoidance[J]. Aerospace, 2024, 11(11): 870. doi: 10.3390/aerospace11110870
[8]	WANG Jia-liang, DONG Kai, GU Zhao-jun, et al. Review of obstacle avoidance methods for small UAVs using visual sensors[J]. Journal of Xidian University (Natural Science), 2025, 52(1): 60-79.
[9]	SUN S, HAN Y Q, LI Y, et al. Analysis and research of intelligent distribution UAV control system based on optical flow sensor[C]//Springer. 2023 International Conference on Innovative Computing. Munich: Springer, 2023: 128-137.
[10]	MAO Y M, CHEN M, WEI X L, et al. Obstacle recognition and avoidance for UAVs under resource-constrained environ-ments[J]. IEEE Access, 2020, 8: 169408-169422. doi: 10.1109/ACCESS.2020.3020632
[11]	WANG D S, LI W, LIU X G, et al. UAV environmental perception and autonomous obstacle avoidance: A deep learning and depth camera combined solution[J]. Computers and Electronics in Agriculture, 2020, 175: 105523. doi: 10.1016/j.compag.2020.105523
[12]	LIN H Y, PENG X Z. Autonomous quadrotor navigation with vision-based obstacle avoidance and path planning[J]. IEEE Access, 2021, 9: 102450-102459. doi: 10.1109/ACCESS.2021.3097945
[13]	CHEN P H, LEE C Y. UAVNet: An efficient obstacel detection model for UAV with autonomous flight[C]//IEEE. 2018 International Conference on Intelligent Autonomous Systems (ICoIAS). New York: IEEE, 2018: 217-220.
[14]	FU Q, YANG Y H, CHEN X Y, et al. Vision-based obstacle avoidance for flapping-wing aerial vehicles[J]. Science China Information Sciences, 2020, 63(7): 170208. doi: 10.1007/s11432-019-2750-y
[15]	ZHANG Wu-yang, ZHANG Wei, SONG Fang, et al. Monocular vision obstacle avoidance method for quadcopter based on deep learning[J]. Journal of Computer Applica-tions, 2019, 39(4): 1001-1005.
[16]	SINGLA A, PADAKANDLA S, BHATNAGAR S. Memory-based deep reinforcement learning for obstacle avoidance in UAV with limited environment knowledge[J]. IEEE Tran-sactions on Intelligent Transportation Systems, 2021, 22(1): 107-118. doi: 10.1109/TITS.2019.2954952
[17]	CHEN Yi-jun, YU Sha-sha, ZHANG Xue-jun. Ground risk quantitative assessment for UAV operations in urban low-altitude scenarios[J]. Journal of Beijing University of Aero-nautics and Astronautics, 2025, 51(3): 806-815.
[18]	HE Hong-bo, XU Chen-chen, YE Hu-ping. Environmental risk assessment of obstacles in low-altitude flight of unmanned aerial vehicle: Taking the Beijing-Tianjin New Town as an example[J]. Progress in Geography, 2021, 40(9): 1503-1515.
[19]	ZHANG Qin-tong, WANG Yu-chao, WANG He-xi, et al. Comprehensive review of large language model fine-tuning[J]. Computer Engineering and Applications, 2024, 60(17): 17-33.
[20]	LIU Xue-bo, HU Bao-tian, CHEN Ke-hai, et al. Key technologies and future development directions of large language models: Insights from ChatGPT[J]. Bulletin of National Natural Science Foundation of China, 2023, 37(5): 758-766.
[21]	LIU P F, YUAN W Z, FU J L, et al. Pre-train, prompt, and predict: A systematic survey of prompting methods in natural language processing[J]. ACM Computing Surveys, 2023, 55(9): 1-35.
[22]	VASWANI A, SHAZEER N, PARMAR N, et al. Attention is all you need[EB/OL]. arXiv, 2017, https://arxiv.org/abs/1706.03762.
[23]	XIAO Jian-li, QIU Xue, ZHANG Yang, et al. Review on large language models in transportation[J]. Journal of Traffic and Transportation Engineering, 2025, 25(1): 8-28. doi: 10.19818/j.cnki.1671-1637.2025.01.002
[24]	YIN S K, FU C Y, ZHAO S R, et al. A survey on multimodal large language models[J]. National Science Review, 2024, 11(12): nwae403. doi: 10.1093/nsr/nwae403
[25]	BROWN T B, MANN B, RYDER N, et al. Language models are few-shot learners[EB/OL]. arXiv, 2005, https://arxiv.org/abs/2005.14165.
[26]	YAN Hao, LIU Yu-liang, JIN Lian-wen, et al. The develop-ment, application, and future of LLM similar to ChatGPT[J]. Journal of Image and Graphics, 2023, 28(9): 2749-2762.
[27]	LIU Jing, GUO Long-teng. Inspiration of GPT-4 on multi-modal foundation models in multimodal understanding, generation, and interaction[J]. Bulletin of National Natural Science Foundation ofChina, 2023, 37(5): 793-802.
[28]	WU Wen-jun, LIAO Xing-chuang, ZHAO Jin-kun. Deep-Seek: Technological innovations and development trends toward artificial general intelligence[J]. Science & Techno-logy Review, 2025, 43(6): 14-20.
[29]	LI Geng, WANG Zi-shuo, HE Xiang-teng, et al. From ChatGPT to large multimodal model: Present and future[J]. Bulletin of National Natural Science Foundation of China, 2023, 37(5): 724-734.

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Unmanned aerial vehicle cruise risk identification technology based on multi-source data and large models

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