End-to-end Learning Approach for Autonomous Driving: A Convolutional Neural Network Model

被引：16
|
作者：
Wang, Yaqin [1 ]
Liu, Dongfang [1 ]
Jeon, Hyewon [1 ]
Chu, Zhiwei [1 ]
Matson, Eric T. [1 ]
机构：
[1] Purdue Univ, Dept Comp & Informat Technol, W Lafayette, IN 47907 USA
来源：
PROCEEDINGS OF THE 11TH INTERNATIONAL CONFERENCE ON AGENTS AND ARTIFICIAL INTELLIGENCE (ICAART), VOL 2 | 2019年
关键词：
Autonomous Driving; AI; Convolutional Neural Network; End-to-end Approach;
D O I：
10.5220/0007575908330839
中图分类号：
TP18 [人工智能理论];
学科分类号：
081104 ; 0812 ; 0835 ; 1405 ;
摘要：
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 previous models.
引用
收藏
页码：833 / 839
页数：7
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