自动驾驶场景中增强深度学习的时空特征提取方法

摘要/Abstract

摘要： 自动驾驶是当下的热点研究方向,同时交通拥堵也是国内常年存在的社会问题。在未来,交通拥堵很大概率会出现在自动驾驶车辆和人为驾驶车辆共存的道路上。考虑到多种可能会影响自动驾驶的因素,在已有学说的基础上进行实验。为了提升整体交通的运行效率,在保证安全的情况下,所有自动驾驶车辆应当尽可能进行高速的行驶,以提升道路效率,从而解决交通拥堵的问题。通过使用二维平面表示道路,将二维信息堆叠形成三维数据以及混合神经网络结构的不同方法来解决这一问题,并利用深度神经网络从中提取出所需的时空特征来进行车辆控制,从而使车辆做出较优的响应。最后,我们利用增强学习的方法来搭建并训练该系统,完成神经网络结构效果的测试。

关键词: 卷积神经网络, 深度学习, 循环神经网络, 增强学习, 自动驾驶

Abstract: Autopilot is a hot research direction,and traffic congestion is a perennial social problem in China.Inthe future,traffic congestion is likely to occur on the road where self-driving vehicle and artificial driving vehicle coexist.On the basis of existing theories,this paper considered a variety of factors that may affect autopilot,including different speeds and neural networks.In order to improve the overall traffic efficiency,on the premise of maintaining safety,all self-driving vehicles should be as fast as possible to improve road efficiency and fundamentally solve traffic congestion.The feature extraction problem of this special case is different from the feature extraction of the image data,so the method of representing the road in the two-dimensional plane is used to process the three-dimensional data formed by the stacking of two-dimensional informationand hybrid neural network.The spatial and temporal features are extracted by using the depth neural network,so that the vehicle can make a better response.Finally,the system design is conbining with reinforcement learning so that it can be trained,and thus the effect of neural networks can be tested.

Key words: Autonomous vehicle, Convolutional neural networks, Deep learning, Recurrent neural networks, Reinforcement learning

中图分类号:

TP18

敬颉, 陈潭, 杜文丽, 刘志康, 尹皓. 自动驾驶场景中增强深度学习的时空特征提取方法[J]. 计算机科学, 2019, 46(11A): 1-4. https://doi.org/

JING Jie, CHEN Tan, DU Wen-li, LIU Zhi-kang, YIN Hao. Spatio-temporal Features Extraction of Traffic Based on Deep Neural Network[J]. Computer Science, 2019, 46(11A): 1-4. https://doi.org/

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