| UAV navigation in high dynamic environments: A deep reinforcement learning approach GUO Tong, N Jiang, LI Biyue, ZHU Xi, W Ya, DU Wenbo Chinese Journal of Aeronautics 34 (2), 479-489, 2021 | 90 | 2021 |
| Cooperative pursuit of unauthorized UAVs in urban airspace via Multi-agent reinforcement learning W Du, T Guo, J Chen, B Li, G Zhu, X Cao Transportation Research Part C: Emerging Technologies 128, 103122, 2021 | 35 | 2021 |
| A deep unsupervised learning approach for airspace complexity evaluation B Li, W Du, Y Zhang, J Chen, K Tang, X Cao IEEE Transactions on Intelligent Transportation Systems 23 (8), 11739-11751, 2021 | 20 | 2021 |
| A spatiotemporal hybrid model for airspace complexity prediction W Du, B Li, J Chen, Y Lv, Y Li IEEE Intelligent Transportation Systems Magazine 15 (2), 217-224, 2022 | 6 | 2022 |
| A multi-objective memetic algorithm with adaptive local search for airspace complexity mitigation B Li, T Guo, Y Mei, Y Li, J Chen, Y Zhang, K Tang, W Du Swarm and Evolutionary Computation 83, 101400, 2023 | 3 | 2023 |
| MAST-GNN: A multimodal adaptive spatio-temporal graph neural network for airspace complexity prediction B Li, Z Li, J Chen, Y Yan, Y Lv, W Du Transportation Research Part C: Emerging Technologies 160, 104521, 2024 | 2 | 2024 |
| Flight conflict resolution method and apparatus based on ultimatum game theory X Cao, W Du, Y Li, B Li, L Zheng US Patent 11,138,893, 2021 | 1 | 2021 |
| Deep unsupervised learning approach, device and storage medium for airspace complexity evaluation W Du, X Cao, B Li, X Zhu, Y Li US Patent App. 17/102,984, 2021 | 1 | 2021 |
