基于视觉注视信息的飞行员态势感知状态评估

Evaluation of pilot situation awareness state based on visual fixation information

正确的态势感知对飞行员合理决策和操作绩效、飞行安全提高有重要影响。为有效评估飞行员态势感知状态,提出了一种基于视觉注视信息的深度学习方法。该方法扩展了飞行员视觉注视分析通道,并在此基础上建立了用于态势感知状态评估的深度神经网络模型。模型采用自注意力结构,以视觉注视数据为输入,分别对高度、航向、空速等多种飞行要素的视觉感知状态进行分析,最终综合评估并实时输出飞行员的态势感知状态。实验结果表明,模型的平均精度可达到91.53%,能够显著提升现有飞行员态势感知状态评估的准确率。同时,模型评估结果与实际飞行绩效的相关性分析验证了模型的有效性。

Correct situation awareness has a profound impact on pilot's rational decision-making and the improvement in operation performance and flight safety.To effectively evaluate the state of pilot's situation awareness,a deep learning method based on visual fixation information is proposed.The method extends the pilot visual fixation analysis channel and builds a deep neural network model for situation awareness state assessment.The model adopts a self-attention structure and takes visual fixation data as input to analyze the visual perception state of various flight elements such as altitude,heading and airspeed etc.,and finally comprehensively evaluate and output the situation awareness state of pilots in real time.Experimental results show that the average accuracy of the model can reach 91.53%,which can significantly improve the accuracy of current pilot situation awareness state assessment.Meanwhile,the correlation analysis between model evaluation results and actual flight performance verifies the effectiveness of the model.