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

页码：833 / 839

页数：7

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