RGB-NIR fusion is a promising method for low-light imaging. However, high-intensity noise in low-light images amplifies the effect of structure inconsistency between RGB-NIR images, which fails existing algorithms. To handle this, we propose a new RGB-NIR fusion algorithm called Dark Vision Net (DVN) with two technical novelties: Deep Structure and Deep Inconsistency Prior (DIP). The Deep Structure extracts clear structure details in deep multiscale feature space rather than raw input space, which is more robust to noisy inputs. Based on the deep structures from both RGB and NIR domains, we introduce the DIP to leverage the structure inconsistency to guide the fusion of RGB-NIR. Benefits from this, the proposed DVN obtains high-quality low-light images without the visual artifacts. We also propose a new dataset called Dark Vision Dataset (DVD), consisting of aligned RGB-NIR image pairs, as the first public RGB-NIR fusion benchmark. Quantitative and qualitative results on the proposed benchmark show that DVN significantly outperforms other comparison algorithms in PSNR and SSIM, especially in extremely low light conditions.

Free Route Airspace (FRA) permits users to freely plan routes between defined entry and exit waypoints with the possibility of routing via intermediate waypoints, which is beneficial to improve flight efficiency. Dynamic management of sectors is essential for the future promotion of full-time FRA applications. In this paper, considering the demand uncertainty at the pre-tactical level, we construct an FRA complexity indicator system and use the XGBoost algorithm to predict the ATC workload. A two-stage sector boundary optimization method is proposed, using Binary Space Partition (BSP) to generate sector boundaries and an A*-based heuristic algorithm to automatically tune them to conform to the operational structure and “direct to” characteristics of FRA. Finally, this paper verifies the effectiveness of the proposed method for balancing ATC workload in a pre-designed Lanzhou FRA in China.

We address the issue of multi-aircraft cooperative strategic trajectory planning in free-route airspace (FRA) in this study, taking into consideration the impact of time-varying and altitude-varying wind forecast uncertainty. A bi-level planning model was established in response to the properties of the wind. The upper level focused on minimizing the flight time, while the lower level aimed to reduce potential conflicts. Meanwhile, a heuristic approach based on conflict severity (CS) within the framework of a cooperative co-evolution evolutionary algorithm (CCEA) was proposed to accelerate the convergence speed in view of the complexity of this optimization issue. In order to conduct the experiments, historical data of 1479 flights over western Chinese airspace were retrieved. The number of conflicts, total flight time, total flight time variance, and deviation were used as indicators to evaluate the safety, efficiency, and predictability of the trajectory. When compared to a trajectory in the structured airspace, the optimal solution was conflict-free and reduced the total flight time by about 17.7%, the variance by 11.7%, and the deviation by 37.5%. Additionally, the contrast with the two-stage model demonstrated that the proposed method was entirely meaningful. The outcome of this survey can provide an effective trajectory-planning method, which is crucial for the sustainable development of future air traffic management (ATM).

To accommodate growing air traffic demand, the traffic complexity management plays a crucial role in the capacity improvement of future automated air traffic management. In this paper, an autonomous and collaborative trajectory planning (ACTP) method is presented to facilitate collaborative decision‐making and enhance flexibility of the traffic complexity management in the en route airspace. A nonlinear integer model is formulated for the conflict‐free autonomous trajectory planning by reorganizing the trajectories with the combination of speed adjustment, lateral rerouting and flight level allocation maneuvers. Within the framework of the air‐ground situation awareness sharing, the spatio‐temporal reachable space is defined to describe the feasible solution space under the constraints of aircraft performance and required time of arrival. In addition, both the system‐level and the individual‐level objectives, traffic complexity and flight efficiency, are considered in the model, which are measured through the metric based on linear dynamical systems and the deviation from the user‐preferred trajectory. To balance the optimality and computational efficiency in the multi‐aircraft trajectory planning, a multi‐aircraft clustered and collaborative optimization algorithm is proposed based on the hybrid distributed‐centralized control strategy. The experimental results of the scenario in Western China airspace is presented, which verify the effectiveness of the proposed ACTP method through the comparison of other methods. Furthermore, a multi‐criteria decision‐making problem is discussed to strike a better trade‐off between different objectives in the trajectory planning.