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Simplified bionic solutions: a simple bio-inspired vehicle collision detection system

机译：简化的仿生解决方案：简单的受生物启发的车辆碰撞检测系统

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Modern cars are equipped with both active and passive sensor systems that can detect potential collisions. In contrast, locusts avoid collisions solely by responding to certain visual cues that are associated with object looming. In neurophysiological experiments, I investigated the possibility that the ‘collision-detector neurons’ of locusts respond to impending collisions in films recorded with dashboard cameras of fast driving cars. In a complementary modelling approach, I developed a simple algorithm to reproduce the neuronal response that was recorded during object approach.Instead of applying elaborate algorithms that factored in object recognition and optic flow discrimination, I tested the hypothesis that motion detection restricted to a ‘danger zone’, in which frontal collisions on the motorways are most likely, is sufficient to estimate the risk of a collision. Furthermore, I investigated whether local motion vectors, obtained from the differential excitation of simulated direction-selective networks, could be used to predict evasive steering maneuvers and prevent undesired responses to motion artifacts.The results of the study demonstrate that the risk of impending collisions in real traffic scenes is mirrored in the excitation of the collision-detecting neuron (DCMD) of locusts. The modelling approach was able to reproduce this neuronal response even when the vehicle was driving at high speeds and image resolution was low (about 200 × 100 pixels). Furthermore, evasive maneuvers that involved changing the steering direction and steering force could be planned by comparing the differences in the overall excitation levels of the simulated right and left direction-selective networks. Additionally, it was possible to suppress undesired responses of the algorithm to translatory movements, camera shake and ground shadows by evaluating local motion vectors. These estimated collision risk values and evasive steering vectors could be used as input for a driving assistant, converting the first into braking force and the latter into steering responses to avoid collisions. Since many processing steps were computed on the level of pixels and involved elements of direction-selective networks, this algorithm can be implemented in hardware so that parallel computations enhance the processing speed significantly.
机译：现代汽车配备了主动和被动传感器系统，可以检测潜在的碰撞。相反，蝗虫仅通过响应与物体隐约相关的某些视觉提示来避免碰撞。在神经生理学实验中，我调查了蝗虫的“碰撞检测器神经元”对快速驾驶汽车的仪表板相机记录的影片中即将发生的碰撞做出反应的可能性。在一种互补的建模方法中，我开发了一种简单的算法来重现在物体接近过程中记录的神经元反应。最可能发生高速公路正面碰撞的区域”足以估计发生碰撞的风险。此外，我研究了从模拟方向选择网络的微分激励中获得的局部运动矢量是否可用于预测规避转向操作并防止对运动伪影的不良响应。研究结果表明，可能发生碰撞的风险真实的交通场景反映在蝗虫的碰撞检测神经元（DCMD）的激发中。即使车辆高速行驶且图像分辨率较低（约200×100像素），该建模方法也能够重现这种神经元反应。此外，可以通过比较模拟的左右方向选择网络的总激励水平的差异来计划涉及改变转向方向和转向力的规避操纵。此外，可以通过评估局部运动矢量来抑制算法对平移运动，相机抖动和地面阴影的不良响应。这些估计的碰撞风险值和逃避的转向向量可以用作驾驶助手的输入，将前者转换为制动力，而后者转换为转向响应以避免碰撞。由于许多处理步骤都是在像素级别和方向选择网络的相关元素上进行计算的，因此该算法可以在硬件中实现，因此并行计算可以显着提高处理速度。

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