Volume 26 Issue 4
Apr.  2026
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CHEN De-qi, ZHANG Shu-hui, ZHANG Wen-hui, JIANG Xian-cai. Method for UAV adaptive cruising trajectory planning for high-risk road sections of forest roads[J]. Journal of Traffic and Transportation Engineering, 2026, 26(4): 121-133. doi: 10.19818/j.cnki.1671-1637.2026.168
Citation: CHEN De-qi, ZHANG Shu-hui, ZHANG Wen-hui, JIANG Xian-cai. Method for UAV adaptive cruising trajectory planning for high-risk road sections of forest roads[J]. Journal of Traffic and Transportation Engineering, 2026, 26(4): 121-133. doi: 10.19818/j.cnki.1671-1637.2026.168
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Method for UAV adaptive cruising trajectory planning for high-risk road sections of forest roads

doi: 10.19818/j.cnki.1671-1637.2026.168

  • School of Civil Engineering and Transportation, Northeast Forestry University, Harbin 150040, Heilongjiang, China

Funds:

Annual Philosophy and Social Science Foundation of Heilongjiang Province 23GLC022

National Natural Science Foundation of China 52572369

More Information
  • Corresponding author: ZHANG Wen-hui, professor, PhD, E-mail： zhangwenhui@nefu.edu.cn
  • Received Date: 2025-10-10
  • Accepted Date: 2026-01-23
  • Rev Recd Date: 2025-12-11
  • Publish Date: 2026-04-28
    Abstract
  • To mitigate forest road traffic accident risks caused by canopy obstruction, a deep reinforcement learning (DRL)-based adaptive cruising model for UAVs that accounts for state uncertainty was proposed. At the perception stage, an adaptive unscented Kalman filter (UKF) tailored to forest road scenarios was designed to address global positioning system (GPS) signal loss. At the decision-making stage, a state uncertainty-aware soft actor-critic (SUA-SAC) algorithm was developed, where UKF-derived state estimates and their covariance were used as network inputs, enabling SUA-SAC to learn control strategies that are more robust to state estimation. The results show that, in terms of training efficiency, the convergence speed of SUA-SAC is improved by approximately 50% and 60% compared with the baseline SAC algorithm and proximal policy optimization algorithm. In multi-scenario tests, compared with the SAC algorithm, SUA-SAC reduces the average tracking error by 67%, 61%, and 66% in scenarios without interference, dynamic occlusion, and strong wind interference, respectively. In tests involving positioning signal loss lasting up to 20 s, the tracking error of SUA-SAC is less affected. Overall, SUA-SAC improves UAV trajectory tracking accuracy, flight stability, and mission success rate under complex forest road conditions, contributing to the enhancement of traffic safety on forest roads.

     

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