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A Supervised Learning Approach to An Unmanned Autonomous Vehicle

机译:无人驾驶自动驾驶汽车的监督学习方法

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Human performance is prone to error in case of taking optimal decision on driving issues. It is due to the lack of concentration or because of some faulty characteristics of human nature. This is one of the core reasons for road accidents in our country. To diminish away this fact, autonomous vehicle system can be a profound solution. Also in modern technological aspects, it is higher seeking concept now. Addressing these events, we attempted to implement an autonomous vehicle system with the aid of computer vision and neural network based learning process. The system learns from image frames from a camera and real-time direction command corresponding to every frame. Then it moves autonomously by matching the learned frames with the current frames through neural network. It is also capable of detecting obstacle, stop and traffic signals and act accordingly.
机译:在对驾驶问题做出最佳决策的情况下,人的表现容易出错。这是由于缺乏集中力或由于人性的某些错误特征。这是我国道路交通事故的主要原因之一。为了消除这一事实,自动驾驶汽车系统可以是一个深刻的解决方案。同样在现代技术方面,它现在是更高要求的概念。针对这些事件,我们尝试借助计算机视觉和基于神经网络的学习过程来实现自动驾驶汽车系统。该系统从摄像机的图像帧和与每个帧相对应的实时方向命令中学习。然后,它通过神经网络将学习到的帧与当前帧进行匹配,从而自动移动。它还能够检测障碍物,停车和交通信号并采取相应措施。

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