Dodging emergent dangers is an innate cognitive ability for animals, which helps them to survive in the natural environment. The retina-superior colliculus (SC)-pulvinar–amygdala–periaqueductal gray pathway is responsible for the visual fear responses, and it is able to quickly detect the looming obstacles for innate dodging. Inspired by the mechanism of the visual fear responses pathway, we propose a brain-inspired emergent obstacle dodging method to model the functions of the related brain regions. This method first detects the moving direction and speed of the salient point of moving objects (retina). Then, we detect the looming obstacles (SC). Third, we modulate attention to the most dangerous area (pulvinar). Fourth, if the degree of danger exceeds the threshold (amygdala), the unmanned ariel vehicle (UAV) moves back to dodge it (periaqueductal gray). Two types of experiments are conducted to validate the effectiveness of the proposed model. In a simulated scene, we simulate the process of mice’s fear responses by putting looming dark lights shining on them. In a natural scene, we apply the proposed model to the UAV emergent obstacles dodging. Compared to the stereo vision model, the proposed model is not only more biologically realistic from the mechanisms perspective, but also more accurate and faster for computation.

中文翻译：

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一种用于无人机紧急避障的大脑启发视觉恐惧反应模型

躲避突如其来的危险是动物与生俱来的认知能力，这有助于它们在自然环境中生存。视网膜-上丘（SC）-叶突-杏仁核-导水管周围灰色通路负责视觉恐惧反应，它能够快速检测到隐约的障碍以进行先天躲避。受视觉恐惧反应通路机制的启发，我们提出了一种受大脑启发的紧急障碍躲避方法来模拟相关大脑区域的功能。该方法首先检测运动物体（视网膜）突出点的运动方向和速度。然后，我们检测迫在眉睫的障碍物（SC）。第三，我们将注意力调整到最危险的区域（pulvinar）。四、如果危险程度超过阈值（杏仁核），无人飞行器 (UAV) 向后移动以躲避它（导水管周围灰色）。进行了两种类型的实验来验证所提出模型的有效性。在模拟场景中，我们通过将若隐若现的暗光照射在老鼠身上来模拟老鼠的恐惧反应过程。在自然场景中，我们将所提出的模型应用于无人机紧急障碍物的躲避。与立体视觉模型相比，所提出的模型不仅从机制的角度在生物学上更逼真，而且计算更准确和更快。我们将提出的模型应用于无人机紧急障碍躲避。与立体视觉模型相比，所提出的模型不仅从机制的角度在生物学上更逼真，而且计算更准确和更快。我们将提出的模型应用于无人机紧急障碍躲避。与立体视觉模型相比，所提出的模型不仅从机制的角度在生物学上更逼真，而且计算更准确和更快。