Deep Learning Algorithm using Virtual Environment Data for Self-driving Car

被引：0

作者：

Kim, Juntae ^{[1
]}

Lim, GeunYoung ^{[1
]}

Kim, Youngi ^{[1
]}

Kim, Bokyeong ^{[1
]}

Bae, Changseok ^{[1
]}

机构：

[1] Daejeon Univ, Dept Elect Info & Comm Engn, Daejeon, South Korea

来源：

2019 1ST INTERNATIONAL CONFERENCE ON ARTIFICIAL INTELLIGENCE IN INFORMATION AND COMMUNICATION (ICAIIC 2019) | 2019年

基金：

新加坡国家研究基金会;

关键词：

Deep learning; artificial intelligence; machine learning; self-driving;

D O I：

10.1109/icaiic.2019.8669037

中图分类号：

TP18 [人工智能理论];

学科分类号：

081104 ; 0812 ; 0835 ; 1405 ;

摘要：

Recent outstanding progresses in artificial intelligence researches enable many tries to implement self-driving cars. However, in real world, there are a lot of risks and cost problems to acquire training data for self-driving artificial intelligence algorithms. This paper proposes an algorithm to collect training data from a driving game, which has quite similar environment to the real world. In the data collection scheme, the proposed algorithm gathers both driving game screen image and control key value. We employ the collected data from virtual game environment to learn a deep neural network. Experimental result for applying the virtual driving game data to drive real world children's car show the effectiveness of the proposed algorithm.

引用

页码：444 / 448

页数：5