Transfer Learning-Based Deep Reinforcement Learning Approach for Robust Route Guidance in Mixed Traffic Environment

Lee, Donghoun

doi:10.1109/access.2024.3395430

IEEE Access

2024

DOI: 10.1109/access.2024.3395430

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Transfer Learning-Based Deep Reinforcement Learning Approach for Robust Route Guidance in Mixed Traffic Environment

Donghoun Lee

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Cited by 1 publication

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Trajectory Planning for Unmanned Vehicles on Airport Apron Under Aircraft–Vehicle–Airfield Collaboration

Yuan,

Zhong,

Zhu

et al. 2024

Sensors

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To address the issue of safe, orderly, and efficient operation for unmanned vehicles within the apron area in the future, a hardware framework of aircraft–vehicle–airfield collaboration and a trajectory planning method for unmanned vehicles on the apron were proposed. As for the vehicle–airfield perspective, a collaboration mechanism between flight support tasks and unmanned vehicle departure movement was constructed. As for the latter, a control mechanism was established for the right-of-way control of the apron. With the goal of reducing waiting time downstream of the pre-selected path, a multi-agent reinforcement learning model with a collaborative graph was created to accomplish path selection among various origin–destination pairs. Then, we took Apron NO.2 in Ezhou Huahu Airport as an example for simulation verification. The results show that, compared with traditional methods, the proposed method improves the average vehicle speed and reduces average vehicle queue time by 11.60% and 32.34%, respectively. The right-of-way signal-switching actions are associated with the path selection behavior of the corresponding agent, fitting the created aircraft–vehicle collaboration. After 10 episodes of training, the Q-values can steadily converge, with the deviation rate decreasing from 40% to below 0.22%, making the balance between sociality and competitiveness. A single trajectory can be planned in just 0.78 s, and for each second of training, 7.54 s of future movement of vehicles can be planned in the simulation world. Future research could focus on online rolling trajectory planning for UGSVs in the apron area, and realistic verification under multi-sensor networks can further advance the application of unmanned vehicles in apron operations.

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Trajectory Planning for Unmanned Vehicles on Airport Apron Under Aircraft–Vehicle–Airfield Collaboration

Yuan,

Zhong,

Zhu

et al. 2024

Sensors

View full text Add to dashboard Cite

show abstract

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Transfer Learning-Based Deep Reinforcement Learning Approach for Robust Route Guidance in Mixed Traffic Environment

Cited by 1 publication

References 41 publications

Trajectory Planning for Unmanned Vehicles on Airport Apron Under Aircraft–Vehicle–Airfield Collaboration

Trajectory Planning for Unmanned Vehicles on Airport Apron Under Aircraft–Vehicle–Airfield Collaboration

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