End-to-end Learning Approach for Autonomous Driving: A Convolutional Neural Network Model
End-to-end Learning Approach for Autonomous Driving: A Convolutional Neural Network Model
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DOI：
10.5220/0007575908330839
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发表时间：
2019
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通讯作者：
Yaqin Wang;Dongfang Liu;Hyewon Jeon;Zhiwei Chu;E. Matson
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文献类型：
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作者：
Yaqin Wang;Dongfang Liu;Hyewon Jeon;Zhiwei Chu;E. Matson
端到端方法是自动驾驶系统常用的方法之一。在这项研究中，我们采用了端到端的方法，因为这种方法已被批准，以导致一个更简单的系统的杰出表现。我们构建了一个卷积神经网络（CNN）来映射来自三个不同角度的摄像头的原始像素，并生成转向命令以在Udacity模拟器中驾驶汽车。我们提出的模型有一个有前途的结果，这是更准确，具有较低的损失率相比，
End-to-end approach is one of the frequently used approaches for the autonomous driving system. In this study, we adopt the end-to-end approach because this approach has been approved to lead to a distinguished performance with a simpler system. We build a convolutional neural network (CNN) to map raw pixels from cameras of three different angles and to generate steering commands to drive a car in the Udacity simulator. Our proposed model has a promising result, which is more accurate and has lower loss rate comparing to