Cheng Yinlei, Li Qingfu
College of Arts and Sciences, Beijing Institute of Fashion Technology, Beijing, China.
PLoS One. 2025 May 14;20(5):e0322380. doi: 10.1371/journal.pone.0322380. eCollection 2025.
Air traffic congestion-induced flight accidents pose a significant challenge in the aviation sector. Currently, aviation navigation systems primarily rely on GPS and Inertial Navigation Systems (INS) to track aircraft, yet they lack the capability to recognize and provide early warnings about the surrounding environment. To address this issue, this paper proposes a multi-aircraft parallel approach aimed at enabling coordinated flight along the same route. This method utilizes a neural network-based semantic segmentation model to monitor aircraft and perform situational awareness of the surrounding environment, thereby assisting in multi-aircraft route scheduling. When wake turbulence is generated, the model can identify the wake, further enhancing flight safety. Recently, state-space models (SSMs) based on Mamba have demonstrated outstanding performance in computational efficiency and inference speed. Based on this, we designed a U-shaped State Space Block UNet (USS-Net), which consists of StateConvBlock and ResConvBlock. The StateConvBlock integrates Mamba as a fundamental module for understanding temporal dynamics and contextual information. By constructing a symmetrical encoder-decoder structure, the model progressively extracts image features and performs multi-scale fusion to achieve high-precision pixel-level segmentation. Experimental results show that USS-Net achieved outstanding performance on the aircraft simulation dataset. On an NVIDIA A100-SXM4-40GB GPU, USS-Net attained a mean Intersection over Union (mIoU) of 95.70% and a pixel accuracy (PA) of 97.80% on the simulation training dataset. These results demonstrate that USS-Net performs effectively in assisting multi-aircraft parallel route scheduling tasks.
空中交通拥堵引发的飞行事故给航空业带来了重大挑战。目前，航空导航系统主要依靠全球定位系统（GPS）和惯性导航系统（INS）来跟踪飞机，但它们缺乏识别周围环境并提供早期预警的能力。为了解决这个问题，本文提出了一种多架飞机并行方法，旨在实现沿同一路线的协同飞行。该方法利用基于神经网络的语义分割模型来监测飞机并对周围环境进行态势感知，从而辅助多架飞机的航线调度。当产生尾流湍流时，该模型可以识别尾流，进一步提高飞行安全性。最近，基于曼巴（Mamba）的状态空间模型（SSM）在计算效率和推理速度方面表现出卓越性能。基于此，我们设计了一种U形状态空间块UNet（USS-Net），它由StateConvBlock和ResConvBlock组成。StateConvBlock将曼巴集成作为理解时间动态和上下文信息的基本模块。通过构建对称的编码器-解码器结构，该模型逐步提取图像特征并进行多尺度融合，以实现高精度的像素级分割。实验结果表明，USS-Net在飞机模拟数据集上取得了优异的性能。在NVIDIA A100-SXM4-40GB GPU上，USS-Net在模拟训练数据集上的平均交并比（mIoU）达到了95.70%，像素准确率（PA）达到了97.80%。这些结果表明，USS-Net在协助多架飞机并行航线调度任务中表现有效。
