End-to-End Learning-based Self-Driving Control Imitating Human Driving

被引：1

作者：

Kim, Donghyun ^{[1
]}

Kwon, Jaerock ^{[2
]}

Nam, Haewoon ^{[1
]}

机构：

[1] Hanyang Univ, Div Elect Engn, Ansan, South Korea

[2] Univ Michigan, Elect & Comp Engn, Dearborn, MI 48128 USA

来源：

12TH INTERNATIONAL CONFERENCE ON ICT CONVERGENCE (ICTC 2021): BEYOND THE PANDEMIC ERA WITH ICT CONVERGENCE INNOVATION | 2021年

基金：

新加坡国家研究基金会;

关键词：

End-to-End learning; Autonomous driving;

D O I：

10.1109/ICTC52510.2021.9620894

中图分类号：

TM [电工技术]; TN [电子技术、通信技术];

学科分类号：

0808 ; 0809 ;

摘要：

In recent years, End-to-End learning-based self-driving cars have been actively researched. Unlike conventional methods, neural networks are trained to drive like human drivers by mapping directly from sensory data to control commands. In this paper, we propose a neural network architecture for recognizing visual information and controlling the steering and speed of the vehicle like humans.

引用

页码：1763 / 1765

页数：3

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