The cooperation of a human pilot with an autonomous agent during flight control realizes parallel autonomy. A parallel-autonomous system acts as a guardian that significantly enhances the robustness and safety of flight operations in challenging circumstances. Here, we propose an air-guardian concept that facilitates cooperation between an artificial pilot agent and a parallel end-to-end neural control system. Our vision-based air-guardian system combines a causal continuous-depth neural network model with a cooperation layer to enable parallel autonomy between a pilot agent and a control system based on perceived differences in their attention profile. The attention profiles are obtained by computing the networks' saliency maps (feature importance) through the VisualBackProp algorithm. The guardian agent is trained via reinforcement learning in a fixed-wing aircraft simulated environment. When the attention profile of the pilot and guardian agents align, the pilot makes control decisions. If the attention map of the pilot and the guardian do not align, the air-guardian makes interventions and takes over the control of the aircraft. We show that our attention-based air-guardian system can balance the trade-off between its level of involvement in the flight and the pilot's expertise and attention. We demonstrate the effectivness of our methods in simulated flight scenarios with a fixed-wing aircraft and on a real drone platform.

提出了一种基于注意力特征的空气监护系统，并通过强化学习在固定翼飞机模拟环境中对其进行训练，实现了飞行中人工驾驶员和自主机器人的并行自治，最终在固定翼飞机和真实无人机平台上的模拟飞行试验中证明了该方法的有效性。

使用视觉注意力的协同飞行控制 -- 空中卫士